Papers
arxiv:2404.07961

Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations

Published on Apr 11, 2024
Authors:
,
,
,

Abstract

A machine learning framework for molecular dynamics simulations using multipole featurization enables efficient calculations with multiple chemical elements by reducing density functional theory calls.

AI-generated summary

We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme.

Community

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2404.07961
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2404.07961 in a model README.md to link it from this page.

Datasets citing this paper 2

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2404.07961 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.