作为一种轻量级的通用原子间潜能,用于先进材料建模的PET-MAD.
Arslan Mazitov1, Filippo Bigi2, Matthias Kellner2
1Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. arslan.mazitov@epfl.ch.
Nature communications
|November 27, 2025
概括
我们开发了PET-MAD,一种机器学习的原子间潜力,用于原子规模的模拟. 它准确地模拟了各种材料,包括无机固体,有机材料和分子,为传统方法提供了具有成本效益的替代方案.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 机器学习原子间潜力 (MLIP) 能够以降低计算成本进行精确的原子尺度模拟.
- 当前的万能MLIP在周期表中脱而出,但可能有利于低能耗配置.
- 需要适用于各种材料类型和配置的多功能潜力.
研究的目的:
- 介绍PET-MAD,一种新的,普遍适用的原子间潜力.
- 提高训练数据的原子多样性,以实现更广泛的应用.
- 与最先进的方法对比,评估PET-MAD的性能.
主要方法:
- 在稳定的无机和有机固体的数据集上训练PET-MAD,并对原子多样性进行系统修改.
- 使用一致的,中等水平的电子结构理论用于训练数据生成.
- 根据六种材料的既定基准和先进模拟来评估PET-MAD.
主要成果:
- 对于无机固体,PET-MAD的准确性与最先进的MLIP具有竞争力.
- 潜力显示分子,有机材料和表面的可靠性.
- PET-MAD是稳定的,快速的,并且可以对材料特性和相位过渡进行近量化研究.
结论:
- 对于各种材料类的原子尺度模拟,PET-MAD提供了一种多功能和高效的解决方案.
- 潜力可以通过最小的目标计算微调以获得高精度.
- PET-MAD 便于研究诸如热波动和相变等复杂现象.
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