论文标题
预测分子材料的特性：多尺度模拟工作流程满足机器学习
Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning
论文作者
论文摘要
如今，使用从高通量计算模型中提取的数据集广泛地广泛应用于分子材料的性质的预测。在某些科学和技术相关性的情况下，分子材料的特性与从纳米级到宏观尺度的分子结构和现象之间发生的联系有关。在这里，我们描述了一种基于多尺度模拟和机器学习来预测分子聚集体性能的方法。
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological relevance, the properties of molecular materials are related to the link between molecular structure and phenomena occurring across a wide set of spatial scales, from the nanoscale to the macroscale. Here, we describe an approach for predicting the properties of molecular aggregates based on multiscale simulations and machine learning.
