Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as “designer solvents” as they can be mixed to precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) to simulate ILs is still relatively unexplored, several questions need to be answered to see if MLIPs can be transformative for ILs. Since ILs are often not pure, but are either mixed together or contain additives, we first demonstrate that a MLIP can be trained to be compositionally transferable; i.e., the MLIP can be applied to mixtures of ions not directly trained on, while only being trained on a few mixtures of the same ions. We also investigated the accuracy of MLIPs for a novel IL, which we experimentally synthesize and characterize. Our MLIP trained on ∼200 DFT frames is in reasonable agreement with our experiments and DFT.

中文翻译：

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等变机器学习原子间势的离子液体模拟的可转移性和准确性


离子液体 (IL) 是一类令人兴奋的电解质，在从储能到溶剂的许多领域都有应用，它们被称为“设计溶剂”，因为它们可以混合以精确调整物理化学性质。由于使用机器学习原子间势 (MLIP) 来模拟 IL 仍相对未经探索，因此需要回答几个问题才能了解 MLIP 是否可以为 IL 带来变革。由于 IL 通常不是纯的，而是混合在一起或含有添加剂，因此我们首先证明 MLIP 可以经过训练以实现成分可转移；即，MLIP 可以应用于未直接训练的离子混合物，而仅针对相同离子的少数混合物进行训练。我们还研究了新型 IL 的 MLIP 的准确性，我们通过实验合成并表征了该新型 IL。我们在~200 DFT 框架上训练的 MLIP 与我们的实验和 DFT 相当一致。