Photo-electrochemical and photocatalytic technologies are promising solutions for solar fuel
production and involve a number of physical and chemical phenomena. We provide an …

Deep neural operators, such as DeepONet, have changed the paradigm in high-
dimensional nonlinear regression, paving the way for significant generalization and speed …

We introduce a data-driven forecasting method for high-dimensional chaotic systems using
long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural …

Classical molecular dynamics simulates the time evolution of molecular systems through the
phase space spanned by the positions and velocities of the constituent atoms. Molecular …

Chemical reactor modelling based on insights and data on a molecular level has become
reality over the last few years. Multiscale models describing elementary reaction steps and …

We describe and implement a computer-assisted approach for accelerating the exploration
of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction …

We propose an approach to learn effective evolution equations for large systems of
interacting agents. This is demonstrated on two examples, a well-studied system of coupled …

Machine learning has had an enormous impact in many scientific disciplines. It has also
attracted significant interest in the field of low-temperature plasma (LTP) modeling and …

Abstract We present an Equation/Variable free machine learning (EVFML) framework for the
control of the collective dynamics of complex/multiscale systems modeled via …

We introduce a data-driven approach to building reduced dynamical models through
manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold …