Abstract Machine learning photodynamics simulations are revolutionary tools to resolve
elusive photochemical reaction mechanisms with time-dependent high-fidelity structure …

Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …

Photo-induced processes are fundamental in nature but accurate simulations of their
dynamics are seriously limited by the cost of the underlying quantum chemical calculations …

While the primary bottleneck to a number of computational workflows was not so long ago
limited by processing power, the rise of machine learning technologies has resulted in an …

Computational chemistry has become an important tool to predict and understand molecular
properties and reactions. Even though recent years have seen a significant growth in new …

Photochemical reactions are being increasingly used to construct complex molecular
architectures with mild and straightforward reaction conditions. Computational techniques …

It is tenable to argue that nobody can predict the future with certainty, yet one can learn from
the past and make informed projections for the years ahead. In this Perspective, we …

Nonadiabatic molecular dynamics simulations (NAMD) go beyond Born–Oppenheimer
molecular dynamics by including two or more electronic states that are coupled. By doing so …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Achieving a balance between computational speed, prediction accuracy, and universal
applicability in molecular simulations has been a persistent challenge. This paper presents …