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Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale

Guanjie Wang^1,2, Changrui Wang¹, Xuanguang Zhang¹

¹School of Materials Science and Engineering, Beihang University, Beijing 100191, China.

|April 22, 2024

Summary

This summary is machine-generated.

Machine learning interatomic potentials (MLIPs) offer efficient and precise simulations for materials research. This review covers MLIP development, applications, and future directions for enhanced materials design.

Keywords:

Chemistry Computer science Materials science Physics

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Area of Science:

Materials Science
Computational Chemistry
Data Science

Background:

Density-functional theory (DFT) is accurate but computationally expensive.
Classical molecular dynamics (MD) is efficient but lacks accuracy.
Machine learning interatomic potentials (MLIPs) bridge the gap between DFT and classical MD.

Purpose of the Study:

To review the current state of machine learning interatomic potentials (MLIPs).
To discuss the essential stages of MLIP development: data generation, descriptors, algorithms, and software.
To explore MLIP applications and future perspectives in materials research.

Main Methods:

Review of data generation techniques for MLIPs.
Analysis of various material structure descriptors.
Examination of six distinct machine learning algorithms.
Survey of available MLIP software.

Main Results:

MLIPs significantly enhance efficiency and precision in materials simulations.
Key applications include phase-change memory materials, structure searching, and property prediction.
Pre-trained universal models show promise for broad applicability.
Future research directions focus on standard datasets, transferability, and generalization.

Conclusions:

MLIPs are a transformative tool for materials research and design.
Standardization of datasets and improved transferability are crucial for future MLIP development.
Balancing accuracy and complexity is key for practical MLIP implementation.