离子液体模拟与等价机器学习原子间潜力的可转移性和准确性
Zachary A H Goodwin1, Malia B Wenny2, Julia H Yang1,3
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.
机器学习的原子间潜力 (MLIP) 可以准确模拟离子液体 (IL),甚至是新的混合物. 这项研究表明,MLIP在组成上可以用于各种IL电解质应用.
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
- 电化学 电化学 电化学
背景情况:
- 离子液体 (ILs) 是具有可调节性质的多功能电解质,在储能及其他领域充当"设计溶剂".
- 机器学习原子间潜能 (MLIP) 的应用用于模拟IL仍然是一个未被充分探索的领域.
研究的目的:
- 评估MLIPs用于模拟离子液体的可行性和变革潜力.
- 为了确定MLIP是否可以在IL混合物和添加剂的组成上进行转移.
主要方法:
- 在密度函数理论 (DFT) 对离子液体计算的有限数据集上训练MLIP.
- 评估MLIP在预测IL混合物不包括在训练套件中的特性方面的准确性.
- 合成和实验性表征一种新的离子液体.
主要成果:
- 证明了MLIP的组成可转移性,使IL混合物的准确模拟成为可能.
- 开发的MLIP显示了与实验数据和DFT计算对新型IL的合理一致.
- 在大约200个DFT上训练的MLIPs被证明是有效的.
结论:
- MLIP对推进离子液体的模拟和设计具有显著的前景.
- 组合转移性是MLIPs处理复杂IL电解质系统的关键特征.
- MLIP为研究新型IL提供了一种计算效率高,准确的方法.
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