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ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data
preprintsubmitted on 25.06.2019, 20:13 and posted on 26.06.2019, 21:42 by Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava Urala Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann
ChemML is an open machine learning 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 machine learning 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 outlined in our recent publication1.
Email Address of Submitting Authormojtabah@buffalo.edu
InstitutionUniversity at Buffalo
ORCID For Submitting Author0000-0002-3779-2246
Declaration of Conflict of InterestThe authors declare to have no competing financial interests.
Read the published paper
in Wiley Interdisciplinary Reviews: Computational Molecular Science