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 …

Graph neural networks (GNNs) have been widely used for predicting molecular properties,
especially for single molecules. However, when treating multi-component systems, GNNs …

Deep learning for complex chemical systems | National Science Review | Oxford Academic
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Deep learning methods applied to problems in chemoinformatics often require the use of
recursive neural networks to handle data with graphical structure and variable size. We …

If the promise of generative modeling techniques is realized, it may fundamentally change
how we carry out molecular simulation. The suite of techniques and models collectively …

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 …

Emerging concepts from scientific deep machine learning such as physics-informed neural
networks (PINNs) enable a data-driven approach for the study of complex kinetic problems …

Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of
chemical space and simulation of large reactive systems. One important path to improving …

While much of synthetic biology was founded on the creation of reusable, standardized
parts, there is now a growing interest in synthetic networks which can compute unique …

In this article, we review the mathematical foundations of convolutional neural nets (CNNs)
with the goals of:(i) highlighting connections with techniques from statistics, signal …