Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Contrastive learning has recently seen tremendous success in self-supervised learning. So
far, however, it is largely unclear why the learned representations generalize so effectively to …

Markov state models (MSMs) are a powerful framework for analyzing dynamical systems,
such as molecular dynamics (MD) simulations, that have gained widespread use over the …

There is an increasing demand for computing the relevant structures, equilibria, and long-
timescale kinetics of biomolecular processes, such as protein-drug binding, from high …

Inspired by the success of deep learning techniques in the physical and chemical sciences,
we apply a modification of an autoencoder type deep neural network to the task of …

Nonlinear ICA is a fundamental problem for unsupervised representation learning,
emphasizing the capacity to recover the underlying latent variables generating the data (ie …

Nonlinear independent component analysis (ICA) provides an appealing framework for
unsupervised feature learning, but the models proposed so far are not identifiable. Here, we …

Inference, prediction, and control of complex dynamical systems from time series is
important in many areas, including financial markets, power grid management, climate and …

Unsupervised learning of latent variable models (LVMs) is widely used to represent data in
machine learning. When such model reflects the ground truth factors and the mechanisms …