EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics
Aravind Krishnamoorthy,
Ankit Mishra,
Deepak Kamal,
Sungwook Hong,
Ken-ichi Nomura,
Subodh Tiwari,
Aiichiro Nakano,
Rajiv Kalia,
Rampi Ramprasad,
Priya Vashishta
Affiliations
Aravind Krishnamoorthy
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Ankit Mishra
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Deepak Kamal
Georgia Institute of Technology, 771 Ferst Drive, Northwest Atlanta, Atlanta, GA 30332, United States of America
Sungwook Hong
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America; Department of Physics & Engineering, California State University, Bakersfield, CA 93311, United States of America
Ken-ichi Nomura
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America; Corresponding author.
Subodh Tiwari
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Aiichiro Nakano
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Rajiv Kalia
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Rampi Ramprasad
Georgia Institute of Technology, 771 Ferst Drive, Northwest Atlanta, Atlanta, GA 30332, United States of America
Priya Vashishta
Collaboratory of Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, United States of America
Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.
