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

Author:

Haghighatlari Mojtaba1ORCID,Vishwakarma Gaurav1,Altarawy Doaa23,Subramanian Ramachandran45,Kota Bhargava U.45,Sonpal Aditya1,Setlur Srirangaraj456,Hachmann Johannes178ORCID

Affiliation:

1. Department of Chemical and Biological Engineering University at Buffalo, The State University of New York Buffalo New York

2. The Molecular Sciences Software Institute, Virginia Tech Blacksburg Virginia

3. Computer and Systems Engineering Department Alexandria University Alexandria Egypt

4. Department of Computer Science and Engineering University at Buffalo, The State University of New York Buffalo New York

5. Center for Unified Biometrics and Sensors University at Buffalo, The State University of New York Buffalo New York

6. Center of Excellence for Document Analysis and Recognition, University at Buffalo The State University of New York Buffalo New York

7. Computational and Data‐Enabled Science and Engineering Graduate Program University at Buffalo, The State University of New York Buffalo New York

8. New York State Center of Excellence in Materials Informatics Buffalo New York

Funder

Armament Research, Development and Engineering Center

National Science Foundation

Office of Science

Publisher

Wiley

Subject

Materials Chemistry,Computational Mathematics,Physical and Theoretical Chemistry,Computer Science Applications,Biochemistry

Reference50 articles.

1. Machine learning for molecular and materials science

2. National Science and Technology Council. Materials genome initiative for global competitiveness Technical report;2011.

3. HachmannJ WindusTL McLeanJA et al. Framing the role of big data and modern data science in chemistry. Technical report;2018.

4. Advances of machine learning in molecular modeling and simulation

5. Simulation and design of energy materials accelerated by machine learning;Wang H;WIREs Comput Mol Sci,2019

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