Quantum and classical reaction rate constant calculations come at the cost of exploring
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …

Complete understanding of most, if not all chemical processes requires at its very core the
knowledge of the underlying free-energy change. In computer-aided drug design, for …

Rapid computational exploration of the free energy landscape of biological molecules
remains an active area of research due to the difficulty of sampling rare state transitions in …

We develop a new deep potential─ range correction (DPRc) machine learning potential for
combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical …

A great need exists for computationally efficient quantum simulation approaches that can
achieve an accuracy similar to high-level theories at a fraction of the computational cost. In …

Over recent years, the use of statistical learning techniques applied to chemical problems
has gained substantial momentum. This is particularly apparent in the realm of physical …

Density functional tight binding (DFTB) is an attractive method for accelerated quantum
simulations of condensed matter due to its enhanced computational efficiency over standard …

Accurate modeling of the behavior of high-explosive (HE) materials requires knowledge of
the equation of state (EOS) for both the reactant and the product states of the material …

We describe a machine learning approach to rapidly tune density functional tight binding
models for the description of detonation chemistry in organic molecular materials. Resulting …

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are
attractive methods for obtaining quantum simulation data at longer time and length scales …