Deductive solution strategies are required in prediction scenarios that are under determined,
when contradictory information is available, or more generally wherever one-to-many non …

Quantum mechanical methods like density functional theory (DFT) are used with great
success alongside efficient search algorithms for studying kinetics of reactive systems …

Lack of quality data and difficulty generating these data hinder quantitative understanding of
reaction kinetics. Specifically, conventional methods to generate transition state structures …

There is a perceived dichotomy between structure-based and descriptor-based molecular
representations used for predictive chemistry tasks. Here, we study the performance …

This paper focuses on the development of multifidelity modeling approaches using neural
network surrogates, where training data arising from multiple model forms and resolutions …

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical
reaction pathways from time resolved species concentration data in a deterministic manner …

Predicting stochastic cellular dynamics as emerging from the mechanistic models of
molecular interactions is a long-standing challenge in systems biology: low-level chemical …

The emergence of artificial intelligence has profoundly impacted computational chemistry,
particularly through machine-learned potentials (MLPs), which offer a balance of accuracy …

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

The last decade has seen remarkable advances in speech, image, and language
recognition tools that have been made available to the public through computer and mobile …