Eco-Comp: Towards Responsible Computing in Materials Science
Keywords: High Performance Computing, Reactive Force Fields, Machine Learning Potentials, Molecular Dynamics, Responsible Computing
TL;DR: This tutorial aims to give a broad overview of the Eco-Comp software, an automated benchmarking tool, and its potential use for the material science community through an interactive guide.
Abstract: 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. 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. This tutorial gives a broad overview of the Eco-Comp software and its potential use for the material science community through an interactive guide.
Submission Track: Tutorials
Submission Category: AI-Guided Design + Automated Material Characterization
Digital Discovery Special Issue: Yes
Submission Number: 58
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