Accelerating complex materials discovery with universal machine-learning potential-driven structure prediction

Yuqi An

doi:10.1016/j.mtener.2025.102059

2025-09-11

PRE

Yuqi An

Zhenbin WANG (Zhenbin Wang) *

DOI：10.1016/j.mtener.2025.102059

原文链接

摘要

En 中文

• 通用机器学习势能能够发现七种新的稳定四元氧化物材料 • M3GNet成功重新发现了其训练数据集中缺失的已知材料 • 结构搜索算法由于计算成本问题成为晶体结构预测的主要瓶颈

期刊

Materials Today Energy

IF:

8.6

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2.4K

被引数:

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机构

City University of Hong Kong

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