Ichor: A Python library for computational chemistry data management and machine learning force field development

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

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Institutions

Publication Details

Journal
Journal of Computational Chemistry
Published
2024-08-31
DOI
https://doi.org/10.1002/jcc.27477
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.23

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article

Ichor: A Python library for computational chemistry data management and machine learning force field development

Matthew J. Burn, Yulian T. Manchev, Paul L. A. Popelier
2 citations
Journal of Computational Chemistry
Machine Learning in Materials Science
0.23
article

Ichor: A Python library for computational chemistry data management and machine learning force field development

Matthew J. Burn, Yulian T. Manchev, Paul L. A. Popelier
article en
2 citations

Abstract

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Journal of Computational ChemistryVol. 45(32)
University of Manchester (GB)
UK Research and Innovation, Biotechnology and Biological Sciences Research Council
Quality Education
Openalex Percentile: Top 52%
Machine Learning in Materials Science
0.23
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Ichor: A Python library for computational chemistry data management and machine learning force field development — Matthew J. Burn, Yulian T. Manchev, et al. · Journal of Computational Chemistry (2024) | TGRS Research Map | TGRS