A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing [0.03%]
基于自然语言处理的大规模高分子材料语料库的通用材料属性数据提取方法研究
Pranav Shetty,Arunkumar Chitteth Rajan,Chris Kuenneth et al.
Pranav Shetty et al.
The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature. We used natural language processing methods to automatically extract material property data from the abs...
Minimal-active-space multistate density functional theory for excitation energy involving local and charge transfer states [0.03%]
用于涉及局域和电荷转移激发态的激发能的最小活性空间多状态密度泛函理论
Ruoqi Zhao,Christian P Hettich,Xin Chen et al.
Ruoqi Zhao et al.
Multistate density functional theory (MSDFT) employing a minimum active space (MAS) is presented to determine charge transfer (CT) and local excited states of bimolecular complexes. MSDFT is a hybrid wave function theory (WFT) and density f...
Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization [0.03%]
机器学习能量重正化系统的环氧树脂粗粒化方法
Andrea Giuntoli,Nitin K Hansoge,Anton van Beek et al.
Andrea Giuntoli et al.
A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We es...
Topological representations of crystalline compounds for the machine-learning prediction of materials properties [0.03%]
晶体化合物的拓扑表征用于机器学习预测材料性质
Yi Jiang,Dong Chen,Xin Chen et al.
Yi Jiang et al.
Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex dat...