Eco-COMP: Towards Responsible Computing in Materials Science

Bridging the time and length scales and the use of large molecular dynamics (MD) simulations in material science is expected to surge in the next few years, partially due to the development of highly accurate machine learning inter-atomic potentials that enable the simulation of multi-million atomic systems. The emergence of easy-to-use machine learning potentials (MLP), such as the Machine-Learned Spectral Neighbor Analysis Potential (ML-SNAP) is indicative of the growing accessibility and versatility of this tool in the scientific community. It allows scientists and engineers to understand key chemical reactions at the atomic level relevant to the development of green energy and environment solutions such as catalysts, batteries, and solar cell. We also expect a high demand for material science simulations using multiple nodes within high-performance computing facilities (HPCs) due to their computational intensity. Through the analysis of catalysis simulation setups consisting of bulk metallic systems with adsorbed molecular species on the surface, we identified various factors that affect parallel computing efficiency. To foster sustainable and ethical computing practices, this study employs the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to find the optimal allocation of computing resources based on the simulation input. We thus propose guidelines to promote responsible computing within HPC architecture: Eco-Comp is a user-friendly automated Python tool that allows material scientists to optimize the power consumption of their simulations using one command, by recognizing, evaluating, and recommending choices on resource usage to a user based on their simulation data, saving time and energy. This work serves towards ensuring that the developing technology of our future will be build upon a foundation that prioritizes sustainability and the ethics of responsible computing.