7 citations · 25 across the 14 of their papers we have counts for
21 papers
Composition based oxidation state prediction of materials using deep learning
Nihang Fu, Jeffrey Hu, Ying Feng +3
Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determina…
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Daniel Varivoda, Rongzhi Dong, Sadman Sadeed Omee +1
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active…
Probabilistic Generative Transformer Language models for Generative Design of Molecules
Lai Wei, Nihang Fu, Yuqi Song +2
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for…
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Lai Wei, Qinyang Li, Yuqi Song +4
Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic…
Genetic programming-based learning of carbon interatomic potential for materials discovery
Andrew Eldridge, Alejandro Rodriguez, Ming Hu +1
Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, poten…
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from Materials Composition
Rongzhi Dong, Yong Zhao, Yuqi Song +6
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of…