2 citations · 5 across the 3 of their papers we have counts for
4 papers
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…
Extended Experimental Inferential Structure Determination Method for Evaluating the Structural Ensembles of Disordered Protein States
James Lincoff, Mickael Krzeminski, Mojtaba Haghighatlari +5
Characterization of proteins with intrinsic or unfolded state disorder comprises a new frontier in structural biology, requiring the characterization of diverse and dynamic structu…
Advances of Machine Learning in Molecular Modeling and Simulation
Mojtaba Haghighatlari, Johannes Hachmann
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, com…