可转移的神经网络潜力和凝聚阶段属性
Anna Katharina Picha1,2, Marcus Wieder3, Stefan Boresch1
1Faculty of Chemistry, Institute of Computational Biological Chemistry, University of Vienna, Wien 1090, Austria.
Journal of chemical information and modeling
|September 11, 2025
概括
可转移的神经网络潜力 (NNP) 难以准确预测凝聚相性质. 测试揭示了ANI-2x和MACE-OFF23等模型的特定弱点,突出了在模拟中需要仔细选择的需要.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 可转移的神经网络潜能 (NNP) 正在迅速发展,主要是通过单分子数据进行训练.
- 目前的应用往往侧重于分子模拟,而不是复杂的凝结相.
- 对于其培训数据之外的系统,NNP的准确性在很大程度上是未被探索的.
研究的目的:
- 评估可转移NNP在重现凝结相特性方面的性能.
- 在液体模拟中应用时识别NNP的特定弱点.
- 为选择合适的NNP进行复杂的模拟提供信息.
主要方法:
- 根据参考数据评估了两个可转让的NNP (ANI-2x,MACE-OFF23(S/M)).
- 纯液体的模拟性质:密度,蒸发热量,热容量,同热压缩性.
- 分析了辐射分布函数和自我扩散常数.
主要成果:
- 在模拟缩相时,这两个NNP都表现出了特定的弱点.
- 即使是轻微的模型缺陷也导致了液体模拟中的显著性能下降.
- 在测试的NNP模型之间,性能差异很大.
结论:
- 当前可转移的NNP可能对一般的冷凝相模拟不够准确.
- 谨慎的模型选择和严格的测试对于超出其培训领域的NNP应用至关重要.
- 需要进一步开发,以提高复杂系统的NNP可靠性.
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