Quantum Electronic StructureMarch 26, 2026

ORCA Meets Python─The ORCA Python Interface OPI
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Tim Tetenberg
Tim Tetenberg
FACCTs GmbH, Cologne 50677, Germany
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Hagen Neugebauer*
Hagen Neugebauer
FACCTs GmbH, Cologne 50677, Germany
*E-mail: [email protected]
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Christoph Plett
Christoph Plett
FACCTs GmbH, Cologne 50677, Germany
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Nakul Santhosh
Nakul Santhosh
FACCTs GmbH, Cologne 50677, Germany
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Markus Bursch*
Markus Bursch
FACCTs GmbH, Cologne 50677, Germany
*E-mail: [email protected]
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https://orcid.org/0000-0001-6711-5804
Christoph Riplinger*
Christoph Riplinger
FACCTs GmbH, Cologne 50677, Germany
*E-mail: [email protected]
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Journal of Chemical Theory and Computation

Cite this: J. Chem. Theory Comput. 2026, XXXX, XXX, XXX-XXX

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https://doi.org/10.1021/acs.jctc.5c02141

Published March 26, 2026

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Abstract

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The ORCA program suite is one of the most widely used quantum chemistry software packages. It features a wide range of electronic structure methods and algorithms for the prediction of molecular chemical properties, reactivity, and spectroscopy. In this paper, we present a fully featured ORCA Python Interface termed OPI to drastically increase the accessibility of ORCA’s method portfolio and enable efficient automation of quantum chemical workflows. OPI is an open-source Python library that provides straightforward low-level access to ORCA’s input, execution, and output with a few lines of Python code. In the following, we introduce OPI version 2.0 and its key features, also outlining its general architecture. In addition, we demonstrate its application through several diverse examples of quantum chemical workflows. These examples include a system-dependent optimal-tuning procedure for range-separated hybrid functionals, generation of training data for machine learning purposes, orbital localization and visualization for chemical education, and calculations with density functional ensembles. Finally, we outline the current status of OPI and future plans for its development. OPI is compatible with ORCA ≥ 6.1.1 and Python ≥ 3.11. The project, its code, and its including documentation are available at https://github.com/faccts/opi. OPI is also available through PyPI (https://pypi.org/project/orca-pi).

This publication is licensed for personal use by The American Chemical Society.

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1. Introduction

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Quantum chemistry has become an integral part of modern chemistry, chemical education, biology, life and material sciences, and pharmaceutical research and development. (1−11) Not only methodological developments but also increasingly efficient and accessible software packages and computational tools have contributed to the fact that quantum chemistry can now be routinely used to solve a wide range of chemical challenges. (12,13) Some prominent quantum chemistry programs include Q-Chem, (14) AMS, (15) TURBOMOLE, (16−18) NWChem, (19) PySCF, (20,21) Quantum Espresso, (22,23) and Psi4 (24) as well as ORCA. (25−27) ORCA, which is freely available for academic use, is recognized in particular for its efficiency, completeness, and remarkable ease of use. These qualities make it a powerful tool for science, industry, and chemical education. However, accessibility and automation of quantum chemical tasks and data generation have become more and more important, driven by the increasing availability of computational resources and the high demand for large-scale, high-quality data for the development of computational chemistry methods based on machine learning (ML). In particular, open-source software packages and standardized data sets have become cornerstones of development. (28,29) Recent ML models such as ANI, (30−34) AIMNet2 (35) and UMA (36,37) or new ML-based density functionals like Skala (38,39) require large and diverse training data sets to achieve a sensible level of robustness and accuracy. In this context, interfaces with common programming languages and other automation tools (40−45) have become crucial. Although ORCA supports its own internal scripting language termed Compound, (27) it has been missing fully functional interfaces to other programming languages like Python, (46−49) which is widely used to process and analyze quantum chemical calculations. Python is particularly popular due to its versatility and flexibility, but still relatively easy to learn, making it suitable for both experts and beginners, even at the secondary and undergraduate levels. Examples of quantum chemistry interfaces with Python or quantum chemistry codes written at least partially in Python include PySCF, (20,21) Psi4, (24) CCLIB, (50) ASH, (51) the Atomic Simulation Environment (ASE), (40) and the Python Library for Automating Molecular Simulations (PLAMS). (52)

Consequently, we developed the ORCA Python interface called OPI. (53) Designed as an open-source project, it provides the best-possible access to ORCA’s versatile methodological toolkit and allows for efficient access to computed quantum mechanical properties via Python.

In this article, we highlight the design philosophy of OPI, introduce its architecture, and employ OPI to various diverse example tasks ranging from everyday quantum chemistry to chemical education and the generation of data sets for ML approaches. Together with this article, we release OPI Version 2.0, which is compatible with ORCA ≥ 6.1.1 and Python ≥ 3.11 and provides even broader and easier access to ORCA than the initial release version.

2. Project Philosophy

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Accessibility: The OPI project is designed as an open-source community project available at https://github.com/faccts/opi. In addition to the OPI code and technical documentation, (54) a collection of example Python scripts and Jupyter notebooks (55,56) is provided. These examples illustrate how OPI can be used for simple and advanced workflows, (57) and how it integrates seamlessly with other established Python-based software tools. Notable community-contributed tutorials are notebooks on the generation of descriptors with OPI for CheMeleon (58) to predict gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and on reference barrier heights calculated with OPI for use with ChemTorch. (59) In general, contributions from external developers to the code, its documentation, tutorials, and examples are very welcome, and details on how to contribute can be found in ref (60). OPI is specifically designed to be as accessible as possible for all conceivable scientific and educational applications and to utilize the full range of methods offered by ORCA.

Interplay with ORCA: ORCA is known for its ease of use and accessibility to beginners. Accordingly, OPI is designed as a straightforward interface to ORCA’s input and output. Abstraction is kept to a minimum as communication with ORCA takes place via ORCA’s standard user interface. This ensures that existing resources such as the official ORCA manual (61) and tutorial (62) pages are also applicable to OPI. By this, OPI remains readily accessible to beginners, while offering particular advantages to experienced ORCA users. Further, OPI aims to provide direct access to ORCA’s capabilities by a high coverage of ORCA’s method portfolio. Thus, it is designed to maintain full task and workflow control even though OPI does not yet provide predefined procedures for quantum chemical tasks such as energy and gradient evaluations, geometry optimizations, or frequency calculations.

3. Architecture of OPI

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OPI is written solely in Python and is based on a small set of third-party Python packages (Numpy, (63) platformdirs, (64) Pydantic, (65) RDKit, (66) semantic-version (67,68)). uv (69) is used for dependency pinning and mypy, (70) Nox, (71) ruff, (72) codespell, (73) and vulture (74) to maintain a high-quality codebase. Pytest (75) and Hypothesis (76) are utilized as a code test framework.

Any ORCA application basically consists of four fundamental steps: (1) Input creation: In case of ORCA, preparation of a single text input file with job instructions and settings. Structural data can be provided within the main input file or as an external structure file. (2) Job execution: Setup of technical environment and program execution. (3) Data extraction: Extraction of the desired data from the generated output. (4) Data processing: Further data analysis, correlation, visualization, or processing.

OPI is designed to particularly cover the first three steps, while extensive data processing is made possible by Python’s extensive ecosystem. The interface can be used for any of these steps independently or in sequence, making OPI flexible enough to be integrated into existing workflows and pipelines.

In its core, OPI utilizes five main classes (Figure 1). The Calculator class (Section 3.1) is the centerpiece of any OPI job while input definition is done mainly via the Input and Structure classes (Section 3.2). The Runner class covers execution (Section 4) and output handling is done via the Output class (Section 4.1). Any low-level routines and helper functions are stored in a separate subtree in the utils subpackage (for more information, refer to the OPI API documentation (54)).

Figure 1. Schematic outline of OPI’s general structure and its five major classes: The Calculator class combines most of the functionality. The other major classes facilitate input creation (Structure and Input), job execution (Runner), and data extraction (Output). OPI benefits from Python’s ecosystem to visualize, analyze, or postprocess the data further and may be integrated into it at any point.

In the following sections, OPI’s core classes are described in detail.

3.1. The Calculator Class

The Calculator class is OPI’s central class and combines input creation, job execution, and data extraction. A typical OPI job starts with the initialization of a Calculator object, which requires at least a unique basename (Figure 2).

Figure 2. OPI example that shows the initialization of the Calculator class.

3.2. Input Generation: The Input and Structure Classes

3.2.1. The ORCA Input

ORCA typically uses a single text-based input file that provides easy access to a plethora of applications such as geometry optimization, frequency calculation, solvation models (e.g., CPCM, (77,78) SMD, (79) openCOSMO-RS, (80)), transition state search, (81) GOAT conformer sampling, (82) and many other advanced functionalities like LED analysis (83) or simply plotting molecular orbitals. In addition to fundamental methods such as density functional and wave function theory electronic methods, OPI can also access the ORCA-External-Tools (OET) interface, (84) enabling the use of any interfaced method within large parts of ORCA’s infrastructure.

Figure 3 shows an example ORCA input that demonstrates all major input elements. Note that, depending on the nature of the calculation, other external files might also be necessary, and that the ORCA input is almost completely case-insensitive. However, certain options, such as file paths, remain case-sensitive, as their case-sensitivity is determined by the underlying operating system.

Figure 3. Example of a simple ORCA input containing all major input elements: Simple input keywords (blue), key-value options (orange), block options (purple), and the coordinates block (black).

The job definition in the ORCA input is based on two types of job parameters: simple input keywords and block options. Simple input keywords are convenience keywords that simplify more complex inputs and parameter settings. Their definition starts with an exclamation mark, e.g. !wB97M-V def2-TZVP and they either map directly to internal parameter in ORCA or change the state of several at once. Block options are key-value pair options grouped into blocks that map directly to an internal parameter in ORCA. Blocks start with a percentage sign, directly followed by the name of the block, and end with an end statement, e.g., %method RunTyp Gradient end. ORCA also features some key-value options that are not part of a block but begin with % and do not end with end, e.g., %maxcore.

In addition to these job parameters, structure data have to be defined via a special block that follows its own syntax. The structure block starts with an asterisk (*) followed by a specifier for the structure data format, where a list of atomic Cartesian coordinates in the XYZ format is the most common. This initial line ends with two integers, where the first represents the total charge and the second the total spin multiplicity (M = 2S + 1), e.g., * xyz 0 1. Then the structure definition follows in the specified format. These can be provided either directly in the input file or via an external structure file. Note that ORCA features some other structure formats. (61) The structure definition block is closed by another asterisk (*) statement on a separate line after the last atom entry.

3.2.2. General Remarks on Input Generation with OPI

In OPI, the job parameters, such as simple input keywords and blocks, are handled completely independently from the structure data. Thereby individual job types can be easily defined and applied to various different structures and vice versa. However, there is a limitation to this freedom for structure specific job parameters that may be used in some ORCA calculation setups.

To provide maximum functionality, OPI natively supports the majority of ORCA’s simple input keywords and block options and thus access to most of its rich method portfolio. (61) Here, OPI aims to mimic the established keyword structure of ORCA but uses its own internal representation. Thus, the naming of the keyword representations in OPI is mostly similar or kept to a minimum degree of abstraction, which provides clarity and convenience for users familiar with the ORCA input. However, internally, OPI must adhere to Python’s rules for naming objects; in practice, this means that object names may contain only letters, digits, and underscores and must not begin with a digit. (85) Thus, ORCA keyword names containing inadmissible special characters are represented by replacing these characters with underscores or, if the names are short, by spelling them out. For example the basis set 6-311++G(2dp,dp) is named G6_311PLUSPLUSG_2DP_2DP in OPI and the basis set 6-31G* is named G6_31GSTAR, respectively. In general, OPI does not perform semantic checks on the input but verifies keyword spelling and data types of block option parameters. Accordingly, care must be taken to ensure that the chosen combination of simple input keywords, block options, and structural data is valid in the ORCA context.

3.2.3. Input: Simple Input Keywords

In OPI, simple input keywords are represented as subclasses of a common base class SimpleKeyword, where each simple input keyword is an instance (object) of that. Furthermore, these objects are grouped by similar or related functionality into enum-like objects that are based on a mutual base class SimpleKeywordBox (e.g., Method or BasisSet). Simple input keywords can be added to the calculation setup accordingly (Figure 4). The example also shows how to define new instance of SimpleKeyword for simple input keywords that exist in ORCA but are not yet defined in OPI.

Figure 4. OPI example of how to add simple keywords to the Calculator.

3.2.4. Input: Block Inputs

Block inputs are represented by their own Pydantic models named BlockName where Name is the name of the respective block spelled in camel-casing (CamelCasing). Pydantic is a third-party Python library that offers native data validation in Python. Thereby, proper representation of simple and complex option blocks as well as stability are guaranteed. Custom Python classes streamline the storage and handling of complex block options. A block and its options can be added accordingly (Figure 5).

Figure 5. OPI example of how to add the maxiter options from ORCA’s %geom block to a job definition in OPI.

Key-value option blocks that are not closed by an end statement such as %maxcore, %ncores and %moinp can be added directly (Figure 6).

Figure 6. An example showing how to specify key-value options in OPI.

Furthermore, not natively implemented options can be added to BlockName objects via BlockName.add_option(“OptionName”, “Value”).

Note that every SimpleKeyword, BlockName, and custom class that represents a block option has to implement the format_orca() method, which emits the string representation of the modeled object in the ORCA input.

3.2.5. Input: Arbitrary Strings

Comments, simple input keywords, and block options that are not defined in OPI can be added manually to the input using the ArbitraryString class (Figure 7).

Figure 7. A code example showcasing how to add not defined or arbitrary input to the ORCA input in OPI.

An ArbitraryString object holds the string to be added to the ORCA input and a principal position parameter. OPI supports three principal positions for ArbitraryString objects in the ORCA input: top at the very top of the input file, before_coords (default) just above of the coordinates block, and bottom at the very bottom of the file (Figure 8).

Figure 8. Excerpt of a minimal ORCA input file showing the three principal positions for input keywords defined in OPI: *top*: At the very top of the file; *before_coords*: Right before the coordinates block; *bottom*: At the very end of the file.

Multiple ArbitraryString objects with the same principal position are inserted sequentially in order of definition. The position control is especially useful when using blocks that require the coordinates to appear first, e.g., the Nuclei option from the %eprnmr-block or the %frag-block.

3.2.6. Structure: Structure Data

Chemical structures are represented by the Structure class. It stores a list of Atom objects, the total electronic charge and total multiplicity. An Atom object comprises the atom type and its Cartesian coordinates. To simplify creation of Structure objects, a set of custom constructors have been implemented. These include creating a Structure object from an XYZ file (from_xyz()), from a SMILES string (from_smiles()), from two Python lists, where the first contains the atomic symbols and the second, a list of 3-tuples with Cartesian coordinates of the atoms (from_lists()). We also provide a simple converter from RDKit’s molecular structure class Mol (from_rdkitmol()), as well as from ASE’s Atoms class (from_ase()). OPI also supports reading the molecular charge and multiplicity from Mol and Atoms objects directly, if not overwritten by the user via the charge and multiplicity parameters, respectively (Figure 9).

Figure 9. OPI example showcasing the different methods to create a Structure object from different sources.

4. Job Execution: The Runner Class

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OPI also has a direct interface to the ORCA binaries which allows for the execution of ORCA jobs directly from Python on a standard workstation. The ORCA version and Open MPI (86) version (required for multicore and multinode parallelism) can be configured via an optional configuration file which adheres to the TOML (87) file standard as shown below or via environmental variables (for more details refer to the OPI documentation (54)) (Figure 10).

Figure 10. Example of an OPI configuration file specifying the location of ORCA and Open MPI. The file adheres to the TOML format.

The number of CPU cores and the memory per core in Megabytes can be defined as described in Section 3.2.4. With the input defined, the ORCA run can be executed via calc.run() (Figure 11).

Figure 11. OPI example showing how to create the ORCA input and execute the job after configuring the compute resources.

Note that OPI provides functionalities to use ORCA’s auxiliary binaries for postprocessing calculation results. Assistance for execution of ORCA on multinode hardware, such as computer-clusters, is not yet supported, as these usually require hardware-specific configuration that is currently not covered by OPI. Thanks to OPI’s modular architecture, substituting the Runner class with a custom executor is straightforward.

4.1. Output Handling: The Output Class

During execution, ORCA produces several outputs, the most important being:

1.	A binary GBW file, which contains geometry, basis set, and wave function information. These data can be extracted into a JSON format using an ORCA utility.
2.	A structured property file, which contains computed quantities such as energies, thermochemical, and spectral data. This file can also be converted to JSON format.
3.	A free-form text-based output file collected from the STDOUT stream of the program. This file is intended for humans to read, but contains some information that is not present in the machine-readable files.

Note that the JSON files are currently created optionally by ORCA and that OPI can also be used to create them from existing ORCA calculations. In an effort to structure and standardize the ORCA output as much as possible, the JSON output has been significantly expanded and further developed since its initial implementation. Therefore, OPI is designed to make extensive use of the JSON output via a set of Pydantic models with one-to-one correspondence to the JSON data trees. This allows swift parsing and access to all available quantities and guarantees type safety. However, since the JSON output does not yet include all possible properties and output data generated by ORCA, OPI also provides functionalities to handle the text output (cf. Section 4.1.1).

Overall, the whole functionality is bundled in a single class Output. An Output object setup for the current job can be requested via calc.get_output() and the data from the JSON files be parsed via output.parse() (Figure 12).

Figure 12. OPI example showing the creation of an Output from an existing Calculator object and parsing of the respective JSON files.

The GBW and property data are available through its two main member variables results_gbw and results_properties, respectively (Figure 13).

Figure 13. OPI example of how to access GBW and property file data from the Output object after parsing the files.

The Output class also facilitates the creation and parsing of the JSON files and the method output.print_graph() can be used to print a nested list of all supported quantities from each of the two JSON files. It also shows if a quantity is present in the JSON files of the current calculation─and if yes, by what Python data type it is represented. An abbreviated example output of this method is shown in (Figure 14).

Figure 14. Shortened output example of the output.print_graph() method. Omitted parts are designated with [...].

4.1.1. Output: getter Methods, the Grepper Class, and Health-Checks

For convenience, the Output class also features a set of getter methods (getters). These can be used to access common quantities, such as the total energy or HOMO and LUMO energies. All getters require ORCA’s JSON output to be parsed beforehand (output.parse()). Figure 15 shows an example usage of the get_final_energy() method to conveniently access the final energy.

Figure 15. OPI has a series of *getter* methods to swiftly fetch properties from completed ORCA calculations. This example show the get_final_energy() method, which returns the final energy of the final structure.

Quantities that are not listed in ORCA’s JSON files, but in any of its other text output files, can be extracted with the Grepper class (Figure 16). This tool allows quick scraping through text searching for patterns, similarly to the well-known Unix tool grep.

Figure 16. Minimal OPI example of the Grepper class, showcasing how to use the class to search for the line containing “Magnitude (Debye): 2.152988155” in the ORCA output of a completed calculation and extracting the value.

The most common example is to check for specific text messages in the ORCA output file, as ORCA does not yet provide standardized exit codes to communicate the final status of a calculation. For example, the phrase “ORCA TERMINATED NORMALLY” indicates that the ORCA process finished without any technical errors. A set of predefined search functions can be found in recipes.py, (88) and some commonly used health-checks and how to use them are shown in Figure 17. The shown health-check functions return a boolean, where True indicates that the phrase was found, and False indicates that it was not.

Figure 17. OPI example of predefined health-check routines.

Note that these health-check functions should only be used if the corresponding type of calculation that they verify has actually been performed.

4.2. Hello Water: A First OPI Script

The previous sections described the basic technical functionalities of OPI. In this section, we discuss an example OPI script for a simple energy calculation for a water molecule (Figure 18).

Figure 18. OPI example script of a simple energy calculation for a water molecule. It displays all steps involved: input definition, input creation, job execution, and printing of the final energy.

First, as done for any typical OPI job, a Calculator is initiated and a unique basename is chosen (calc = Calculator(basename = “job”)).

Next, structural parameters are read from an external structure file in XYZ format (calc.structure = Structure.from_xyz (“H2O.xyz”)). The multiplicity and charge of the system are defined separately via the Structure class to be charge = 0 and multiplicity = 1. Now all other input specifications are defined. The electronic structure method is chosen by adding the respective simple input keyword via calc.input.add_simple_keywords(). Here, the Hartree–Fock (HF) method is chosen via Method.HF and combined with the def2-TZVP (89) Ahlrich-type basis set (BasisSet.DEF2_TZVP). Further, the number of computing cores (calc.input.ncores = 2) and the memory per core in megabytes (calc.input.maxcore = 1000) are set. With the ORCA input defined, the input file is created via calc.write_input(). Note that by default ORCA performs a single point energy calculation if no other run mode like, e.g., an optimization, is requested. Finally, calc.run() executes ORCA which uses the constructed input file and once the calculation is completed, any output treatment is further done via the Output class (obtained with calc.get_output()). By default, the Calculator class ensures that the required output files are automatically created by ORCA after the calculation is completed. After job termination a health-check for normal termination is performed using the convenience getter output.terminated_normally(). Now, the JSON files are parsed by output.parse() and the output.get_final_energy() method is used to return the final energy of the water molecule. As this example only showcases a small fraction of OPI’s functionalities, more complex and diverse examples are presented in the following Section 5.

It is also important to note from this example that the states of Calculator, Input, Runner, and Output are not influenced by the associated ORCA job in any way, except when the user explicitly changes a state via method calls such as Output.parse().

4.3. Post-Processing Existing ORCA Outputs

Section 4.2 demonstrated how to perform an ORCA job with OPI, including creation, execution and data collection. However, existing ORCA output that may have not been generated with OPI can also be postprocessed with OPI as demonstrated in Figure 19. Here, the initialization of a Calculator is omitted, and instead an instance of the Output class is created directly. Subsequently, it is recommended to perform a series of health checks appropriate to the calculation type. The desired properties can then be accessed directly via the corresponding getter methods (cf. Section 4.1.1) or from the data trees Output.results_gbw and Output.results_properties.

Figure 19. OPI example depicting how to only postprocess the results from a single-point calculation.

5. Examples

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To provide the best possible accessibility, the OPI GitHub repository (https://github.com/faccts/opi) and the main documentation (https://www.faccts.de/docs#opi) include various examples like ready-to-use Python scripts and descriptive Jupyter notebooks on various topics of varying complexity. However, to demonstrate the basic features of OPI in the context of this work, several showcases will be discussed in the following.

5.1. General ORCA Tasks

The basic tasks most often encountered in quantum chemistry are the evaluation of the energy, optimization of a molecular geometry toward a stable minimum, and calculation of vibrational frequencies to verify the position on the potential energy surface and to obtain free energy corrections. The latter two tasks are often performed at the same level of theory, while the final energies are evaluated at a higher level. In the example for such basic workflows, we calculate the relative free energy ordering of butadiene isomers, namely cis-, trans-, and iso-butene (See SI for the full example). The most important function calls for the initial DFT geometry optimization and frequency calculation are shown in Figure 20.

Figure 20. OPI example for performing a r²SCAN-3c geometry optimization followed by a frequency calculation to obtain a free energy.

Initial geometry optimizations and frequency calculations are performed with the efficient r²SCAN-3c composite DFT method. (90) This calculation can be specified using the Calculator by adding the simple input keywords Dft.R2SCAN_3C, Task.OPT, Task.FREQ (cf. section 3.2). Free energies can be obtained conveniently with the get_free_energy() function of the Output class (cf. section 4.1). Higher level electronic energies are then calculated on the optimized DFT structures at the DLPNO–CCSD(T) (91,92)/def2-TZVP level as shown in Figure 21.

Figure 21. OPI example of how to combine a high-level DLPNO–CCSD(T) single-point energy with thermostatistical corrections from DFT to obtain a free energy.

Note that the moderate basis set and the default PNO settings are selected to keep the computational costs for this example low, but they can easily be adjusted to all settings available in ORCA. The optimized DFT structures are easily accessible by the get_structure() function from the DFT Output object, which returns a Structure object. The structure is then put into calculators for the DLPNO–CCSD(T) single-point calculations. After the calculations are finished, final DLPNO–CCSD(T) energies are obtained from the get_final_energy() function and the DFT thermostatistical corrections are obtained via the get_free_energy_delta() and added to obtain high-level free energies (cf. Section 4.1).

5.2. Optimal Tuning of a Range-Separated Hybrid Functional

The accurate prediction of excited-state properties is a key aspect of the quantum chemical development of optoelectronic materials such as organic light-emitting devices (OLEDs). Specifically, the prediction of singlet–triplet gaps is the key to design thermally activated delayed fluorescence (TADF) emitters. (93) In this context, density functional theory (DFT) based methods and in particular range-separated hybrid functionals are commonly used. (94) To improve their performance for such properties, molecule-specific optimal tuning of the range-separation parameter ω can be employed. In the approach proposed by Head-Gordon and co-workers, the optimal tuning target function J² is minimized with respect to ω (eq 1). (95)

J^{2} (ω) = \sum_{N = Z, Z + 1} [ϵ_{N}^{HOMO} (ω) - {IP}_{N} (ω)]^{2}

(1)

Here, N is the number of electrons, Z the atomic number, ϵ_N^HOMO the energy of the highest occupied molecular orbital (HOMO), and IP_N the respective ionization potential.

In this example, we will utilize OPI to tune the parameter ω in the ωB97M-V (96) functional in combination with the def2-SVP basis set (89) to calculate the singlet–triplet gap for 5,9-dioxa-13b-boranaphtho[3,2,1-de]anthracene (DOBNA) (Figure 22a). Within ORCA ω can be varied via the RangeSepMu option of the %method block (cf. section 3.2). The respective OPI workflow is depicted in Figure 22b.

Figure 22. (a) Molecular structure of the DOBNA molecule; (b) Schematic OPI workflow.

We start the workflow by setting up the OPI calculator for IP and EA calculations with variable ω parameter. This is depicted in Figure 23. With the setup function, calculations on the ωB97M-V/def2-SVP level of theory for different ω values, charges and multiplicities can be set up in simple Python functions.

Figure 23. Single-point calculator setup from the OPI script to perform the optimal tuning workflow described in Section 5.2.

Calculations for the neutral, the positively charged species, and the negatively charged species are set up and executed. After the execution, outputs for the different charged species are obtained and eq 1 is evaluated as shown in Figure 24.

Figure 24. Function body for evaluation of J²(ω) from the OPI script to perform the optimal tuning workflow described in Section 5.2.

The OPI getters for accessing the final energy and the HOMO allow one to compute J²(ω) in a few lines of code. Setup, execution, and evaluation of the results are combined in one function that takes ω as input and returns the corresponding J²(ω) value. This function can then be easily minimized with SciPy. (97) The minimization is performed with the lower bound of ω = 0.01 and the upper bound of ω = 0.45 and converges in this example in seven steps to ω = 0.19. After optimization, the entire range is scanned to plot the dependence of J(ω)². The scan is depicted together with the ΔUKS results for ΔE(S1-T1) with the default and the optimal ω value in Figure 25. For this example, calculations with the default ω = 0.30 yield an S1-T1 gap of 0.25 eV while the optimized ω = 0.19 yields 0.20 eV which is much better in agreement with the experimental value of 0.18 eV. (98) With the presented OPI notebook, we fully automate the optimal tuning process. In this demonstration, OPI orchestrated 135 calculations for the ω(J²) scan, 21 calculations for the ω minimization, and four additional calculations for the S1-T1 gaps.

Figure 25. Plot of ω as a function of J² and S1-T1 gaps computed with the default and optimally tuned ω in comparison to the experimental reference (cf. ref (98)).

5.3. Generating ML Training Data

One application that massively profits from OPI is the generation of large-scale data that can be easily transformed into standardized databases for training, e.g., machine-learned interatomic potentials (MLIPs) such as AIMNet2 (35) and UMA. (36) With the massive emergence of ML-based models, data sets such as the OMol25 (37) have become indispensable. ORCA not only offers great versatility, efficiency, and robustness to reliably generate these reference data, but also provides a rich toolkit to check for, e.g., electronic stability and multireference character. (99−101) As an example, we generated a set of arbitrary structures of molecules containing the elements H, B, C, N, O, and F using MindlessGen. (102) The procedure orchestrated by OPI after initial structure generation with MindlessGen is shown in Figure 26.

Figure 26. Schematic OPI workflow to generate new, standardized Mindless data based using MindlessGen with OPI.

We constrained the molecules to have a maximum of 20 atoms, be neutral and closed-shell. With this procedure, we generated 304 data points in total. Further, with OPI’s versatile functionalities, we performed different checks to exclude, e.g., potential multireference cases, and with easy access to the results through OPI, we extracted a broad range of electronic properties. These include common ones like electronic energies, gradients, and HOMO–LUMO gaps, as well as properties like Minimal Basis Iterative Stockholder (MBIS) charges and dynamic polarizabilities. Using community-established Python packages such as h5py (103) allows to export the data to an efficient format like HDF5 (104) to curate databases. An example is shown in Figure 27.

Figure 27. Example molecule generated with MindlessGen and a human-readable excerpt of the output generated with OPI and Python.

5.4. Density Functional Ensembles

With a zoo of density functional approximations (DFAs) available, the choice of a suitable method can become tedious. Typically, the performance of various DFAs is evaluated on established benchmark sets and the best-performing functional is chosen. Dral et al. (105) have recently proposed a different approach to mix the results of different DFAs forming an ensemble of functionals. Depending on the number of functionals included in the ensemble, this approach requires many calculations that can be automated with OPI. In this example, we will demonstrate the automated application of an DFA ensemble approach on the MB16-43 (106) benchmark set by Grimme et al. including the comparison of the individual functionals and an automated reweighting of their components. The example ensemble is constructed from the ωB97M-V, (96) M06-2X, (107) and r²SCAN0-D4 (108) functionals in combination with the def2-TZVPD (109,110) basis set. We performed the necessary single-point calculations with OPI, calculated the respective reaction energies, and performed a linear regression as implemented in scikit-learn (111) similar to the procedure originally proposed using a ridge regression with the hyperparameter α = 10. The resulting functional weights ω_i of the single-point energies E_i of the respective methods

E = \sum_{i}^{N} ω_{i} E_{i}

(2)

were determined as 0.13 for ωB97M-V, 0.12 for r²SCAN0-D4, and 0.75 for M06-2X. The results for the individual methods as well as for the ensemble are shown in Figure 28.

Figure 28. (a) Flowchart of the process of determining the ensemble weights. (b) Correlation plot of the tested functionals on the MB16-43 as well as the ensemble optimized on the set. MSDs and MADs are given in kcal·mol^–1. One data point below 0 kcal·mol^–1 and two above 900 kcal·mol^–1 are omitted for clarity.

5.5. OPI in Chemical Education

Quantum chemistry can make a valuable contribution to chemical education by making fundamental chemical and physical effects understandable in detail and visualizable attractively. (9,11) For example, the basic concept of molecular orbital theory can be used to convey knowledge about the nature of chemical bonds and their reactivity. (10,112) In this context, the graphical representation of the orbitals involved is of great didactic value. With the widespread availability of 3D printing techniques, (113−115) these structures and orbitals can also be turned into physical objects that students can hold in their hands. (115) With ORCA and OPI, various variants of orbitals, including molecular orbitals (MOs) and localized molecular orbitals (LMOs), can be easily computed for any molecule. In this example, an electronic structure calculation at the semiempirical GFN2-xTB (116) level and subsequent orbital localization were performed for the water molecule. Further, the orbital space is directly visualized as a MO diagram and both canonical MOs and LMOs are plotted and saved in Gaussian cube format that can be further processed to be printed as a 3D model of the respective orbitals. Visualization of the (L)MOs directly in the notebook is possible with py3Dmol. (117) The most important function calls for cube file generation and visualization within a Jupyter notebook are shown in Figure 29.

Figure 29. Function calls for cube file generation and visualization within a Jupyter notebook.

The HOMO, HOMO-1, and HOMO-2 frontier orbitals are shown in Figure 30a. The corresponding orbitals localized with Pipek-Mezey scheme (118) are shown in Figure 30b.

Figure 30. (a) Canonical frontier orbitals and (b) Pipek–Mezey localized orbitals of water (GFN2-xTB level) as plotted in the example notebook.

To showcase how easily the underlying theoretical method or the investigated molecule can be adjusted, a second example was created where the localized PBE0 (119)/def2-SVP orbitals for octachlorodirhenate ([Re₂Cl₈]^2–) are plotted, as shown in Figure 31, to investigate the Re–Re quadruple bond. In line with chemical intuition, one δ-bond, two π-bonds, and one σ-bond LMOs are found.

Figure 31. Bonding LMOs (PBE0/def2-SVP, Pipek-Mezey orbital localization) for [Re₂Cl₈]^2– as plotted in the example notebook. The δ-, two π-, and the σ-bonds can be identified in line with chemical intuition.

6. Conclusions and Outlook

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In this work, we introduced an open-source ORCA Python interface termed OPI which provides convenient access to ORCA input creation, job execution, and output parsing. OPI is designed to provide a low level of abstraction that guarantees great similarity to the established ORCA input syntax. In addition, the central classes Calculator, Input, Structure, Runner, and Output are tailored to provide maximum control over any imaginable ORCA calculation. Accordingly, OPI provides the user with a powerful toolkit for efficient automation of complex quantum chemical tasks and workflows based on ORCA and subsequent data processing, also benefiting from the vast ecosystem of Python. In the future, we also plan to implement more abstract task functions in OPI, starting with basic single-point calculations, geometry optimizations, and frequency calculations. These tasks should not require any knowledge about ORCA by the user to provide even simpler access to quantum chemical calculations to an even wider community. The current capabilities of OPI are illustrated with a diverse set of applications including computational tasks such as system specific optimal-tuning procedures of range-separated hybrid functionals, ML training data generation, density functional ensemble calculations, and MO visualization for educational purposes. These examples demonstrate that OPI represents a key addition to the ORCA infrastructure that drastically improves its accessibility and usability in complex scientific and industrial scenarios including the automation of quantum chemical calculations. The latter became an integral aspect of modern computational chemistry in the wake of developments driven by artificial intelligence and machine learning. Such approaches also include innovative agentic AIs such as ElAgente, (120) which further help to democratize quantum chemistry in the future. In this context, the OPI also greatly extends the toolkit of chemistry educators to teach with a novel degree of interactivity. In addition, OPI provides access to various mean-field quantities from ORCA, e.g., energies, coefficients, and occupations of molecular orbitals, which can be useful for developing applications in quantum-computing. Access to such quantities is planned to be further improved in the future. In conclusion, OPI represents a significant step in the accessibility and automation of quantum chemical calculations. Overall, we are convinced that the combination of ORCA and OPI, which are both free for academic use, will strengthen all fields of computational chemistry with ORCA and establish new, innovative strategies in research and development as well as new standards for chemical education. We are also striving to further expand the community network of OPI strengthening the open-source spirit and extending its codebase, documentation, and tutorials.

Data Availability

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The OPI code is freely available as open source on GitHub (https://github.com/faccts/opi) and via PyPI (https://pypi.org/project/orca-pi). An online documentation of the project can be found at https://www.faccts.de/docs#opi.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.5c02141.

Computed data for the ML training data example (ZIP)
Additional overview of currently available quantities from the GBW JSON file and the property JSON file (PDF)
Computation-ready scripts and Jupyter Notebooks for all presented examples (ZIP)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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Corresponding Authors
- Hagen Neugebauer - FACCTs GmbH, Cologne 50677, Germany; Email: [email protected]
- Markus Bursch - FACCTs GmbH, Cologne 50677, Germany; https://orcid.org/0000-0001-6711-5804; Email: [email protected]
- Christoph Riplinger - FACCTs GmbH, Cologne 50677, Germany; Email: [email protected]
Authors
- Tim Tetenberg - FACCTs GmbH, Cologne 50677, Germany
- Christoph Plett - FACCTs GmbH, Cologne 50677, Germany
- Nakul Santhosh - FACCTs GmbH, Cologne 50677, Germany
Notes
The authors declare the following competing financial interest(s): FACCTs GmbH manages the commercial licensing of the ORCA quantum chemistry software package.

Acknowledgments

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The authors thank Shiyao Ju for creating useful tutorial notebooks and initial testing of OPI and Mathieu C. Brandenburg for the initial codevelopment of OPI. The authors also thank Dr. Gregor Giesen for technical support and Dr. Georgi L. Stoychev for helpful discussions and proofreading of the manuscript. For open-source contributions to the OPI project until the time of submission, the authors thank Frederic Bender, Jackson Burns, Anselm Hahn, Jan Hamaekers, Prof. Esther Heid, Julia Kaczmarek, Matt Taylor, Anton Zamyatin, and Matthew Burn.

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Abstract
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Figure 1
Figure 1. Schematic outline of OPI’s general structure and its five major classes: The Calculator class combines most of the functionality. The other major classes facilitate input creation (Structure and Input), job execution (Runner), and data extraction (Output). OPI benefits from Python’s ecosystem to visualize, analyze, or postprocess the data further and may be integrated into it at any point.
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Figure 2
Figure 2. OPI example that shows the initialization of the Calculator class.
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Figure 3
Figure 3. Example of a simple ORCA input containing all major input elements: Simple input keywords (blue), key-value options (orange), block options (purple), and the coordinates block (black).
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Figure 4
Figure 4. OPI example of how to add simple keywords to the Calculator.
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Figure 5
Figure 5. OPI example of how to add the maxiter options from ORCA’s %geom block to a job definition in OPI.
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Figure 6
Figure 6. An example showing how to specify key-value options in OPI.
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Figure 7
Figure 7. A code example showcasing how to add not defined or arbitrary input to the ORCA input in OPI.
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Figure 8
Figure 8. Excerpt of a minimal ORCA input file showing the three principal positions for input keywords defined in OPI: top: At the very top of the file; before_coords: Right before the coordinates block; bottom: At the very end of the file.
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Figure 9
Figure 9. OPI example showcasing the different methods to create a Structure object from different sources.
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Figure 10
Figure 10. Example of an OPI configuration file specifying the location of ORCA and Open MPI. The file adheres to the TOML format.
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Figure 11
Figure 11. OPI example showing how to create the ORCA input and execute the job after configuring the compute resources.
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Figure 12
Figure 12. OPI example showing the creation of an Output from an existing Calculator object and parsing of the respective JSON files.
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Figure 13
Figure 13. OPI example of how to access GBW and property file data from the Output object after parsing the files.
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Figure 14
Figure 14. Shortened output example of the output.print_graph() method. Omitted parts are designated with [...].
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Figure 15
Figure 15. OPI has a series of getter methods to swiftly fetch properties from completed ORCA calculations. This example show the get_final_energy() method, which returns the final energy of the final structure.
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Figure 16
Figure 16. Minimal OPI example of the Grepper class, showcasing how to use the class to search for the line containing “Magnitude (Debye): 2.152988155” in the ORCA output of a completed calculation and extracting the value.
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Figure 17
Figure 17. OPI example of predefined health-check routines.
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Figure 18
Figure 18. OPI example script of a simple energy calculation for a water molecule. It displays all steps involved: input definition, input creation, job execution, and printing of the final energy.
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Figure 19
Figure 19. OPI example depicting how to only postprocess the results from a single-point calculation.
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Figure 20
Figure 20. OPI example for performing a r²SCAN-3c geometry optimization followed by a frequency calculation to obtain a free energy.
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Figure 21
Figure 21. OPI example of how to combine a high-level DLPNO–CCSD(T) single-point energy with thermostatistical corrections from DFT to obtain a free energy.
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Figure 22
Figure 22. (a) Molecular structure of the DOBNA molecule; (b) Schematic OPI workflow.
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Figure 23
Figure 23. Single-point calculator setup from the OPI script to perform the optimal tuning workflow described in Section 5.2.
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Figure 24
Figure 24. Function body for evaluation of J²(ω) from the OPI script to perform the optimal tuning workflow described in Section 5.2.
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Figure 25
Figure 25. Plot of ω as a function of J² and S1-T1 gaps computed with the default and optimally tuned ω in comparison to the experimental reference (cf. ref (98)).
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Figure 26
Figure 26. Schematic OPI workflow to generate new, standardized Mindless data based using MindlessGen with OPI.
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Figure 27
Figure 27. Example molecule generated with MindlessGen and a human-readable excerpt of the output generated with OPI and Python.
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Figure 28
Figure 28. (a) Flowchart of the process of determining the ensemble weights. (b) Correlation plot of the tested functionals on the MB16-43 as well as the ensemble optimized on the set. MSDs and MADs are given in kcal·mol^–1. One data point below 0 kcal·mol^–1 and two above 900 kcal·mol^–1 are omitted for clarity.
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Figure 29
Figure 29. Function calls for cube file generation and visualization within a Jupyter notebook.
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Figure 30
Figure 30. (a) Canonical frontier orbitals and (b) Pipek–Mezey localized orbitals of water (GFN2-xTB level) as plotted in the example notebook.
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Figure 31
Figure 31. Bonding LMOs (PBE0/def2-SVP, Pipek-Mezey orbital localization) for [Re₂Cl₈]^2– as plotted in the example notebook. The δ-, two π-, and the σ-bonds can be identified in line with chemical intuition.
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Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.5c02141.
- Computed data for the ML training data example (ZIP)
- Additional overview of currently available quantities from the GBW JSON file and the property JSON file (PDF)
- Computation-ready scripts and Jupyter Notebooks for all presented examples (ZIP)
Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

ORCA Meets Python─The ORCA Python Interface OPIClick to copy article linkArticle link copied!

Journal of Chemical Theory and Computation

Abstract

1. Introduction

2. Project Philosophy

3. Architecture of OPI

Figure 1

3.1. The Calculator Class

Figure 2

3.2. Input Generation: The Input and Structure Classes

3.2.1. The ORCA Input

Figure 3

3.2.2. General Remarks on Input Generation with OPI

3.2.3. Input: Simple Input Keywords

Figure 4

3.2.4. Input: Block Inputs

Figure 5

Figure 6

3.2.5. Input: Arbitrary Strings

Figure 7

Figure 8

3.2.6. Structure: Structure Data

Figure 9

4. Job Execution: The Runner Class

Figure 10

Figure 11

4.1. Output Handling: The Output Class

Figure 12

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4.1.1. Output: getter Methods, the Grepper Class, and Health-Checks

Figure 15

Figure 16

Figure 17

4.2. Hello Water: A First OPI Script

Figure 18

4.3. Post-Processing Existing ORCA Outputs

Figure 19

5. Examples

5.1. General ORCA Tasks

Figure 20

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5.2. Optimal Tuning of a Range-Separated Hybrid Functional

Figure 22

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5.3. Generating ML Training Data

Figure 26

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5.4. Density Functional Ensembles

Figure 28

5.5. OPI in Chemical Education

Figure 29

Figure 30

Figure 31

6. Conclusions and Outlook

Data Availability

Supporting Information

Terms & Conditions

Author Information

Acknowledgments

References

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ORCA Meets Python─The ORCA Python Interface OPI
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