Recent advances in equivariant graph neural networks (GNNs) have made deep learning
amenable to developing fast surrogate models to expensive ab initio quantum mechanics …

The use of machine learning in physical chemistry is not new. The physical chemistry
community has long embraced the power of data science and statistical learning tools in …

Neural network computing can help solve advanced analytical problems to meet the
demands of modern chemical research. In his Feature article, Grady Hanrahan of California …

Chemical kinetics and reaction engineering consists of the phenomenological framework for
the disentanglement of reaction mechanisms, optimization of reaction performance and the …

The suitability of pruned computational neural networks (CNNs) for resolving nonlinear
multicomponent systems involving synergistic effects by use of oscillating chemical reaction …

We report a series of deep learning models to solve complex forward and inverse design
problems in molecular modeling and design. Using both diffusion models inspired by …

Orders-of-coupling representations (representations of multivariate functions with low-
dimensional functions that depend on subsets of original coordinates corresponding to …

We present a time-dependent variational method to learn the mechanisms of equilibrium
reactive processes and efficiently evaluate their rates within a transition path ensemble. This …

Conspectus Machine learning interatomic potentials (MLIPs) are widely used for describing
molecular energy and continue bridging the speed and accuracy gap between quantum …

In this work, we present a general purpose deep neural network package for representing
energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called …