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

Journal Article · · Wiley Interdisciplinary Reviews: Computational Molecular Science
DOI: https://doi.org/10.1002/wcms.1458 · OSTI ID:1803108
ORCiD logo [1];  [2];  [3];  [2];  [2];  [2];  [2]; ORCiD logo [4]
  1. State Univ. of New York at Buffalo, NY (United States); OSTI
  2. State Univ. of New York at Buffalo, NY (United States)
  3. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Alexandria Univ., Ibrahimia, Alexandria (Egypt)
  4. State Univ. of New York at Buffalo, NY (United States); New York State Center of Excellence in Materials Informatics, Buffalo, NY (United States)
+ Show Author Affiliations

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. Finally, 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.

Research Organization:
Kitware, Inc., Clifton Park, NY (United States)
Sponsoring Organization:
Armament Research; National Science Foundation (NSF); New York Center of Excellence in Materials Informatics; USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0017193
OSTI ID:
1803108
Journal Information:
Wiley Interdisciplinary Reviews: Computational Molecular Science, Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science Journal Issue: 4 Vol. 10; ISSN 1759-0876
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
chemistry
data science
data-driven research
informatics
machine learning
mathematical & computational biology
program package