精确和可转移的机器学习潜力用于分子动力学模拟酸盐玻璃的酸盐玻璃
Marco Bertani1, Thibault Charpentier2, Francesco Faglioni1
1Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.
Journal of chemical theory and computation
|January 13, 2024
概括
一个新的机器学习潜力准确地模拟了各种成分和温度的酸盐玻璃. 这种先进的模型在预测玻璃结构和性能方面超越了传统方法.
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:
- 模拟玻璃的行为需要精确的原子间电位.
- 现有的经验潜力与广泛的组成和温度范围进行斗争.
- 机器学习为开发更强大的潜力提供了一个有希望的途径.
研究的目的:
- 为二元酸盐玻璃开发一个准确和可转移的机器学习潜力.
- 为了能够在广泛的成分 (0-50% Na2O) 和温度 (300-3000 K) 中进行模拟.
- 在预测玻璃结构和性能方面超越现有的经验潜力.
主要方法:
- 利用神经网络算法 (DeePMD代码) 根据当地的原子几何来近似潜在能量表面.
- 在从密度函数理论 (DFT) 计算中获得的总能量和原子力的大数据集上训练模型.
- 采用经典分子动力学 (MD) 模拟来生成不同温度的训练结构.
主要成果:
- 开发了一个强大的和可转移的机器学习潜力,用于酸玻璃.
- ML潜能准确地复制结构和属性,如结合角度分布,总分布函数和状态的振动密度.
- 在预测关键玻璃特性方面表现优于实证潜力.
结论:
- 开发的机器学习潜力为模拟酸盐玻璃提供了重大进步.
- 该方法表现出高精度和跨组成和温度范围的可转移性.
- 这种方法为模拟复杂的多组件氧化物玻璃铺平了道路,在降低计算成本的情况下,接近初始准确度.
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