We introduce a machine learning method in which energy solutions from the Schrödinger
equation are predicted using symmetry adapted atomic orbital features and a graph neural …

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes
and materials. ML-based force fields, trained on large data sets of high-quality electron …

Conventional machine-learning (ML) models in computational chemistry learn to directly
predict molecular properties using quantum chemistry only for reference data. While these …

Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the
extended tight-binding schemes, allow for efficient quantum mechanical simulations of large …

Semi-empirical quantum chemical approaches are known to compromise accuracy for the
feasibility of calculations on huge molecules. However, the need for ultrafast calculations in …

Catalyzed by enormous success in the industrial sector, many research programs have
been exploring data-driven, machine learning approaches. Performance can be poor when …

We present a review of extended Lagrangian Born–Oppenheimer molecular dynamics and
its most recent development. The molecular dynamics framework is first derived for general …

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple
graphical processing units (GPUs) was accomplished using the MAGMA linear algebra …

Tensor cores, along with tensor processing units, represent a new form of hardware
acceleration specifically designed for deep neural network calculations in artificial …

Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of
plants native to South and Central America. However, these pigments are unstable under …