Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
Chair of Theoretical Chemistry, Technische Universitát München, 85747 Garching, Germany.
J Chem Theory Comput. 2022 Jul 12;18(7):4586-4593. doi: 10.1021/acs.jctc.2c00343. Epub 2022 Jun 16.
Co-crystals are a highly interesting material class as varying their components and stoichiometry in principle allows tuning supramolecular assemblies toward desired physical properties. The prediction of co-crystal structures represents a daunting task, however, as they span a vast search space and usually feature large unit cells. This requires theoretical models that are accurate and fast to evaluate, a combination that can in principle be accomplished by modern machine-learned (ML) potentials trained on first-principles data. Crucially, these ML potentials need to account for the description of long-range interactions, which are essential for the stability and structure of molecular crystals. In this contribution, we present a strategy for developing Δ-ML potentials for co-crystals, which use a physical baseline model to describe long-range interactions. The applicability of this approach is demonstrated for co-crystals of variable composition consisting of an active pharmaceutical ingredient and various co-formers. We find that the Δ-ML approach offers a strong and consistent improvement over the density functional tight binding baseline. Importantly, this even holds true when extrapolating beyond the scope of the training set, for instance in molecular dynamics simulations under ambient conditions.
共晶是一类非常有趣的材料，因为通过改变其组成和化学计量比，原则上可以调节超分子组装体以获得所需的物理性质。然而，预测共晶结构是一项艰巨的任务，因为它们跨越了广阔的搜索空间，并且通常具有较大的晶胞。这需要评估准确且快速的理论模型，原则上可以通过基于第一性原理数据训练的现代机器学习 (ML) 势来实现。至关重要的是，这些 ML 势需要考虑长程相互作用的描述，这对于分子晶体的稳定性和结构至关重要。在本贡献中，我们提出了一种用于共晶的 Δ-ML 势的开发策略，该策略使用物理基线模型来描述长程相互作用。该方法适用于由活性药物成分和各种共晶形成剂组成的可变组成的共晶。我们发现，与密度泛函紧束缚基线相比，Δ-ML 方法提供了强大而一致的改进。重要的是，即使在超出训练集范围的情况下，例如在环境条件下的分子动力学模拟中，这种情况仍然成立。
