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 in silico 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.

中文翻译：

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分子共晶筛选中结构稳定性预测的混合机器学习方法

共晶是一种非常有趣的材料类别，因为原则上改变它们的成分和化学计量允许将超分子组装体调整到所需的物理性质。计算机_然而，共晶结构的预测是一项艰巨的任务，因为它们跨越了巨大的搜索空间并且通常具有大的晶胞。这需要准确且快速评估的理论模型，这种组合原则上可以通过在第一性原理数据上训练的现代机器学习 (ML) 潜力来实现。至关重要的是，这些 ML 势需要考虑对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。在这篇文章中，我们提出了一种开发共晶 Δ-ML 势的策略，该策略使用物理基线模型来描述远程相互作用。这种方法的适用性被证明适用于由活性药物成分和各种共形成物组成的可变组成的共晶体。我们发现 Δ-ML 方法比密度功能紧密结合基线提供了强大且一致的改进。重要的是，当推断超出训练集的范围时，这甚至是正确的，例如在环境条件下的分子动力学模拟中。