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WIREs Comput Mol Sci

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ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Software Focus

Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava U. Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann

Published Online: Jan 30 2020

DOI: 10.1002/wcms.1458

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Abstract ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under: Software > Simulation Methods Computer and Information Science > Chemoinformatics Structure and Mechanism > Computational Materials Science Software > Molecular Modeling

ChemML provides a multitude of methods as part of seven core task classes to conduct data mining projects (such as the creation of data‐derived machine learning surrogate models)

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The process of applying transfer learning from a deep learning model 1 for target property α based on a large set of lower‐quality data to a more accurate model 2 based on a small set of high‐quality data

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The process of applying a model‐based active learning strategy to a pool of unlabeled candidates as implemented in ChemML. After initializing the training and test sets randomly, an ensemble of deep learning models score unlabeled candidates in a sequential or batch selection format to query one or several new data points that promise to improve the model the most

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Toy workflow/computation graph used in ChemML. (a) Plot of the computation graph with corresponding Python code (b) and input file (c); (d) shows the structure of a computation unit and (e) an example of its implementation in the Jupyter notebook graphical user interface. We stress that actual machine learning workflows are considerably more involved than this simplistic toy example

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Design scheme and architecture of the ChemML program package, consisting of ChemML Library and ChemML Wrapper. The overview shows the seven task classes and the methods available within them, as well as the six categories we distinguish with regards to the method sources

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