论文标题
基于机器学习的小分子的预测 - 表面相互作用势
Machine-learning based prediction of small molecule -- surface interaction potentials
论文作者
论文摘要
预测小分子与靶表面的吸附亲和力对于从催化到药物递送和人类安全的一系列领域至关重要，但是考虑到周围培养基的影响时，要执行计算的一项复杂任务。我们提出了一种柔性的机器学习方法，以预测化学物质的平均力（PMF）和吸附能的电势 - 从每个伙伴的单独相互作用势与一组探针原子的单独相互作用势的表面对。我们使用通过原子分子动力学获得的PMF库来训练模型。我们在培训组和验证组中都发现了原始PMF和预测的PMF之间的良好一致性，从而确认了这种方法的预测能力，并通过在训练集外产生分子和表面的PMF来证明模型的灵活性。
Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (PMFs) and adsorption energies for chemical -- surface pairs from the separate interaction potentials of each partner with a set of probe atoms. We use a pre-existing library of PMFs obtained via atomistic molecular dynamics for a variety of inorganic materials and molecules to train the model. We find good agreement between original and predicted PMFs in both training and validation groups, confirming the predictive power of this approach, and demonstrate the flexibility of the model by producing PMFs for molecules and surfaces outside the training set.
