98 citations · 340 across the 6 of their papers we have counts for
6 papers
Data efficiency and extrapolation trends in neural network interatomic potentials
Joshua A. Vita, Daniel Schwalbe-Koda
Over the last few years, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivarian…
Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning
James Damewood, Daniel Schwalbe-Koda, Rafael Gomez-Bombarelli
Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive…
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce t…
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda +3
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a p…
Generative Models for Automatic Chemical Design
Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli
Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovat…
Graph similarity drives zeolite diffusionless transformations and intergrowth
Daniel Schwalbe-Koda, Zach Jensen, Elsa Olivetti +1
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis. Although interzeolite transformations enable selective crystallization, their des…