论文标题
使用基于边缘化图的主动学习的有效化学空间探索：用于预测具有分子模拟的烷烃的热力学特性的应用
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation
论文作者
论文摘要
我们基于高斯过程回归和边缘化图内（GPR-MGK）引入了一种探索性主动学习（AL）算法，以最低成本探索化学空间。使用高通量分子动力学模拟来生成数据和图神经网络（GNN）以预测，我们为热力学性质预测构建了一个主动学习分子模拟框架。在特定的靶向251,728个烷烃分子中，由4至19个碳原子及其液体物理特性组成：密度，热能和汽化焓，我们使用AL算法选择最有用的分子来代表化学空间。计算和实验测试集的验证表明，对于计算测试集，对于计算测试集的$ \ rm R^2> 0.99 $，只有313个分子（占总数的0.124 \％）足以训练具有$ \ rm rm r^2> 0.99 $的精确GNN模型，用于实验测试集。我们重点介绍了提出的AL算法的两个优点：与高通量数据生成和可靠的不确定性量化的兼容性。
We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost. Using high-throughput molecular dynamics simulation to generate data and graph neural network (GNN) to predict, we constructed an active learning molecular simulation framework for thermodynamic property prediction. In specific, targeting 251,728 alkane molecules consisting of 4 to 19 carbon atoms and their liquid physical properties: densities, heat capacities, and vaporization enthalpies, we use the AL algorithm to select the most informative molecules to represent the chemical space. Validation of computational and experimental test sets shows that only 313 (0.124\% of the total) molecules were sufficient to train an accurate GNN model with $\rm R^2 > 0.99$ for computational test sets and $\rm R^2 > 0.94$ for experimental test sets. We highlight two advantages of the presented AL algorithm: compatibility with high-throughput data generation and reliable uncertainty quantification.
