具有神经网络潜力的建筑独立的绝对溶解自由能量计算
Anna Katharina Picha1,2, Sara Tkaczyk3,4, Thierry Langer3
1University of Vienna, Faculty of Chemistry, Institute of Computational Biological Chemistry, 1090 Vienna, Austria.
The journal of physical chemistry letters
|November 11, 2025
概括
我们介绍了一种使用神经网络潜力 (NNP) 的化学自由能量模拟 (FES) 中逐渐解原子和分子的新方法. 这种方法与各种NNP架构兼容,允许更准确的FES计算.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 化学自由能模拟 (FES) 对于预测分子性质至关重要.
- 传统的FES方法依赖于力场,但神经网络潜力 (NNP) 提供了更高的准确性.
- 在FES的一个关键挑战是原子和分子的逐渐脱.
研究的目的:
- 开发一种使用NNP在FES中的原子和分子脱的新方法.
- 确保该方法与各种NNP架构的兼容性.
- 为了验证该方法在FES中的准确性和适用性.
主要方法:
- 开发了一种技术,通过操纵NNP描述系统中的邻居列表来逐渐解原子/分子.
- 证明了这种邻居列表操纵与在力场FES中使用的软核潜力的等价性.
- 通过循环闭合测试和溶解自由能量的计算验证了该方法.
主要成果:
- 成功实施了一种在基于NNP的FES中逐步脱的方法.
- 显示邻居列表操纵有效地模仿软核心潜力.
- 使用MACE-OFF23 ((S/M) NNP. 实现了准确的溶解自由能量.
结论:
- 拟议的方法为基于NNP的FES提供了一种多功能方法.
- 这种技术对特定的NNP架构不可知,增强了其广泛的适用性.
- 该方法允许准确的自由能量计算,准确度可能更高的NNP.
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