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Tristan Maxson¹, Tibor Szilvási¹

¹Department of Chemical and Biological Engineering, University of Alabama, Tuscaloosa, Alabama 35487, United States.

The journal of physical chemistry letters

|March 28, 2024

概括

在液态水上训练的等效机器学习原子间潜力 (MLIP) 准确地预测了各种水相的特性,包括蒸汽-液体平衡和冰. 这些MLIP证明了模拟水的行为具有广泛的可转移性.

科学领域:

计算化学是一种计算化学.
材料科学是一种材料科学.
统计力学就是统计力学.

背景情况:

机器学习原子间潜力 (MLIPs) 为分子模拟提供了量子力学的经济有效替代方案.
之前的研究表明,仅在液态水数据上训练的MLIP无法转移到其他阶段,例如蒸汽液体平衡.
这种限制意味着MLIPs可能无法捕捉水相互作用的基本物理.

研究的目的:

开发和验证具有等价架构的MLIP,以在不同阶段进行精确的水模拟.
评估这些MLIPs对蒸汽-液体平衡,气相集群和固体冰相的可转移性.
确认等价MLIP是否可以学习物理准确的水相互作用.

主要方法:

在3200个液态水结构上使用等价架构来训练机器学习原子间潜力 (MLIP).
对液态水的特性 (密度) 的实验和理论数据验证MLIP.
测试MLIP在模拟蒸汽-液体平衡,气相水群 (多体分解) 和冰相 (能量,状态的振动密度) 的性能.

主要成果:

开发的等价MLIP准确地复制了液态水密度在广泛的温度范围内 (230-365K).
MLIPs表现出极好的可转移性,准确地预测了高达550K的蒸汽-液体平衡特性.
这些潜能成功地捕获了气相水群中的多体相互作用,并准确地预测了冰相的特性.

结论:

在液态水上训练的等效机器学习原子间潜能在任意水相中表现出显著的可转移性.
这些MLIP提供了精确的水行为模拟,包括相位过渡和分子间相互作用.
这些发现表明,等价架构使MLIP能够学习物理上有意义的水相互作用表示.

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