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

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …

In recent years, intramolecular noncovalent interaction has become an important means to
modulate the optoelectronic performances of organic/polymeric semiconductors. However, it …

In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …

Proton hop** is a key reactive process within zeolite catalysis. However, the accurate
determination of its kinetics poses major challenges both for theoreticians and …

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 …

Porous liquids (PLs), an emerging type of flowing liquid materials that combine the merits of
porous solids and flowing liquids, have garnered immense attention since the concept of …

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 …