We present a physically-motivated topology of a deep neural network that can efficiently
infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily …

Recently, it has been shown that neural networks not only approximate the ground-state
wave functions of a single molecular system well but can also generalize to multiple …

Interatomic potentials learned using machine learning methods have been successfully
applied to atomistic simulations. However, accurate models require large training datasets …

Progress towards the energy breakthroughs needed to combat climate change can be
significantly accelerated through the efficient simulation of atomistic systems. However …

Massive scale, in terms of both data availability and computation, enables important
breakthroughs in key application areas of deep learning such as natural language …

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those
with equivariant representations such as NequIP, are attracting significant attention due to …

The ability to understand and engineer molecular structures relies on having accurate
descriptions of the energy as a function of atomic coordinates. Here, we outline a new …

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …

In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum
mechanical data have seen tremendous success recently. Top-down approaches that learn …

Machine learning has recently entered into the mainstream of coarse-grained (CG)
molecular modeling and simulation. While a variety of methods for incorporating deep …