Foreword to the focus issue: machine-learning assisted approaches for large-scale simulations

Tomofumi Tada; Satoshi Watanabe

doi:10.1080/27660400.2026.2697585

July 6, 2026Science and Technology of Advanced Materials MethodsOpen Access

Foreword to the focus issue: machine-learning assisted approaches for large-scale simulations

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Key Points

This foreword aims to introduce the focus issue on machine learning methods in computational materials science.
Overview of machine learning interatomic potentials (MLIPs)
Discussion of recent technological advancements in simulations
Analysis of integration of ML techniques in materials science.
Machine learning is revolutionizing approaches in materials simulations.
Recent developments show improved accuracy in predicting material behavior.
MLIPs facilitate faster and more efficient computational simulations.

Implication

Foreword highlights recent machine learning advancements in computational materials science, suggesting significant implications for future simulations.

Abstract

Recent advances in machine learning (ML) have opened a new era in computational materials science. In particular, the development of machine learning interatomic potentials (MLIPs), including neura...

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Tada et al. (2026) studied this question.

synapsesocial.com/papers/6a4dea28d2ea289ef6283fb7 https://doi.org/10.1080/27660400.2026.2697585

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Foreword to the focus issue: machine-learning assisted approaches for large-scale simulations

Key Points

Implication

Abstract

Discussion

Cite This Study

Foreword to the focus issue: machine-learning assisted approaches for large-scale simulations

Key Points

Implication

Abstract

Discussion

Cite This Study

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