重新审视酸盐玻璃的机器学习潜力:分散相互作用的缺失作用
Alfonso Pedone1, Marco Bertani1, Matilde Benassi1
1Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.
Journal of chemical theory and computation
|April 24, 2025
概括
机器学习的原子间潜力 (MLIP) 可以模拟氧化玻璃. MACE模型对酸玻璃具有前景,分散相互作用提高了准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 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) 正在成为材料模拟的强大工具.
- 传统方法在模拟氧化玻璃等复杂系统方面存在局限性.
- 预先训练有素的MLIP具有更广泛应用的潜力,但它们在无序系统上的性能需要评估.
研究的目的:
- 为了评估预训练的MACE (多ACE) 模型模拟酸盐玻璃.
- 为了比较MACE的性能与对酸盐进行定制训练的DeePMD MLIP.
- 在玻璃模拟中研究分散相互作用对MLIP精度的影响.
主要方法:
- 使用了预训练的MACE模型和定制训练的DeePMD MLIP.
- 模拟酸盐玻璃作为一个测试案例.
- 纳入D3(BJ) 分散校正以评估它们的影响.
主要成果:
- MACE准确地复制了诸如中子结构因子,对分布函数和Si物种化等结构性质.
- 与定制MLIP相比,MACE的弹性性能准确度略低.
- 两种MLIP都表现出改善密度和弹性性质的复制,并进行分散校正.
结论:
- 预训练的MACE模型适合模拟酸盐玻璃.
- 散射相互作用对于准确建模玻璃密度和弹性特性至关重要.
- 将一般MLIP转移到无序系统是可行的,但需要仔细考虑训练数据,特别是关于分散的训练数据.
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