基于统一图的原子间潜力,用于优化矿结构
Maitreyo Biswas1, Rushik Desai1, Gavin Bidna1
1School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
Journal of chemical information and modeling
|January 16, 2026
概括
我们开发了一种机器学习模型来预测化 Perowskites (HaPs) 的特性. 这种统一的方法有效地探索它们的复杂结构,以发现新材料.
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
- 机器学习 机器学习
背景情况:
- 化烯矿 (HaPs) 在光电子和催化学方面表现有前途.
- 它们复杂的组成空间 (合金,缺陷,表面) 阻碍了优化.
- 有效地探索哈尔太阳能潜在能量表面是具有挑战性的.
研究的目的:
- 为HaPs开发一个统一的基于图形的深度学习的原子间潜力.
- 为了实现高效的优化和预测跨多种HAP结构的能量.
- 为了导航复杂的潜在能量表面 (PES) 的 HaPs.
主要方法:
- 训练了一个基于M3GNet的机器学习原子间潜力 (IAP) 在12,000个HaP结构的全面DFT数据集上.
- 在培训数据中包括散装合金,原生/杂质缺陷和表面板.
- 在IAP框架中,我们训练了能量,力和压力,以实现基于梯度的优化.
主要成果:
- 在复杂的 HaP PES 中,M3GNet-IAP 证明了强大的通用性.
- 实现了较低的预测误差:能量 (3.7 meV/原子),力 (16.5 meV/Å) 和应力 (5.5 MPa).
- 准确预测HaPs的形成,分解,缺陷和表面能量.
结论:
- 统一的替代模型为HaP几何优化提供了一个整体的方法.
- 这种方法有助于有效地探索HAP的各种结构变异.
- 该模型对于发现新的HaP组成,缺陷,剂和表面特性具有变革性.
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