ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

Abstract

This work introduces ParAMS -- a versatile Python package that aims to make parameterization workflows in computational chemistry and physics more accessible, transparent and reproducible. We demonstrate how ParAMS facilitates the parameter optimization for potential energy surface (PES) models, which can otherwise be a tedious specialist task. Because of the package's modular structure, various functionality can be easily combined to implement a diversity of parameter optimization protocols. For example, the choice of PES model and the parameter optimization algorithm can be selected independently. An illustration of ParAMS' strengths is provided in two case studies: i) a density functional-based tight binding (DFTB) repulsive potential for the inorganic ionic crystal ZnO, and ii) a ReaxFF force field for the simulation of organic disulfides.

arXiv comment16 pages, 5 figures (of which 1 in SI), 3 tables (of which 2 in SI)

References in corpus (5)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• SchNet - a deep learning architecture for molecules and materials
• Less is more: sampling chemical space with active learning
• Classical interaction potentials for diverse materials from ab initio data: a review of potfit
• PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Cited by in corpus (4)

• Mixing I and Br in Inorganic Perovskites: Atomistic Insights from Reactive Molecular Dynamics Simulations
• Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol
• Improving the Silicon Interactions of GFN-xTB
• Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: an Application to Water Adsorption on Alumina