Many learning tasks, including learning potential energy surfaces from ab initio calculations,
involve global spatial symmetries and permutational symmetry between atoms or general …

Deep learning has achieved remarkable success in learning representations for molecules,
which is crucial for various biochemical applications, ranging from property prediction to …

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

Atomic neural networks (ANNs) constitute a class of machine learning methods for
predicting potential energy surfaces and physicochemical properties of molecules and …

Chemical reactions occur in energy, environmental, biological, and many other natural
systems, and the inference of the reaction networks is essential to understand and design …

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

In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

Extracting meaningful information from spectroscopic data is key to species identification as
a first step to monitoring chemical reactions in unknown complex mixtures. Spectroscopic …

Fast and accurate simulation of complex chemical systems in environments such as
solutions is a long standing challenge in theoretical chemistry. In recent years, machine …

The design and implementation of adaptive chemical reaction networks, capable of
adjusting their behavior over time in response to experience, is a key goal for the fields of …