2 citations · 3 across the 4 of their papers we have counts for
4 papers
Learning a General Model of Single Phase Flow in Complex 3D Porous Media
Javier E. Santos, Agnese Marcato, Qinjun Kang +4
Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures an…
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Javier E Santos, Zachary R. Fox, Nicholas Lubbers +1
Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise.…
Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers +5
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based softwar…
Training Data Selection for Accuracy and Transferability of Interatomic Potentials
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers +3
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, lin…