2 citations · 7 across the 4 of their papers we have counts for
4 papers
Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins
Jie Li, Oufan Zhang, Seokyoung Lee +5
We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordin…
NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan +10
We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of dir…
Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar-Zadeh +3
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we…
A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography
Shuai Liu, Jie Li, Kochise C. Bennett +6
We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data repr…