Condensed Matter > Materials Science
[Submitted on 1 May 2024]
Title:Environment-adaptive machine learning potentials
View PDFAbstract:The development of interatomic potentials that can effectively capture a wide range of atomic environments is a complex challenge due to several reasons. Materials can exist in numerous structural forms (e.g., crystalline, amorphous, defects, interfaces) and phases (solid, liquid, gas, plasma). Each form may require different treatment in potential modeling to reflect the real physical behavior correctly. Atoms interact through various forces such as electrostatic, van der Waals, ionic bonding, covalent bonding, and metallic bonding, which manifest differently depending on the chemical elements and their electronic structures. Furthermore, the effective interaction among atoms can change with external conditions like temperature, pressure, and chemical environment. Consequently, creating an interatomic potential that performs well across diverse conditions is difficult since optimizing the potential for one set of conditions can lead to a trade-off in the accuracy of predicted properties associated with other conditions. In this paper, we present a method to construct accurate, efficient and transferable interatomic potentials by adapting to the local atomic environment of each atom within a system. The collection of atomic environments of interest is partitioned into several clusters of atomic environments. Each cluster represents a distinctive local environment and is used to define a corresponding local potential. We introduce a many-body many-potential expansion to smoothly blend these local potentials to ensure global continuity of the potential energy surface. This is achieved by computing the probability functions that determine the likelihood of an atom belonging to each cluster. We apply the environment-adaptive machine learning potentials to predict observable properties for Ta element and InP compound, and compare them with density functional theory calculations.
Submission history
From: Ngoc Cuong Nguyen Dr. [view email][v1] Wed, 1 May 2024 04:01:28 UTC (5,797 KB)
Current browse context:
cond-mat.mtrl-sci
Change to browse by:
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
Connected Papers (What is Connected Papers?)
CORE Recommender (What is CORE?)
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.