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

P y SCF is a Python-based general-purpose electronic structure platform that supports first-
principles simulations of molecules and solids as well as accelerates the development of …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

The accuracy of any machine learning potential can only be as good as the data used in the
fitting process. The most efficient model therefore selects the training data that will yield the …

Automatic differentiation represents a paradigm shift in scientific programming, where
evaluating both functions and their derivatives is required for most applications. By removing …

Accurate quantum mechanics based predictions of property trends are so important for
material design and discovery that even inexpensive approximate methods are valuable …

Semiempirical quantum chemistry has recently seen a renaissance with applications in high-
throughput virtual screening and machine learning. The simplest semiempirical model still in …

Based on thermodynamic integration, we introduce atoms in molecules (AIM) using the
orbital-free framework of alchemical perturbation density functional theory (APDFT). Within …

We introduce a computational method for global optimization of structure and ordering in
atomic systems. The method relies on interpolation between chemical elements, which is …

Do** compounds can be considered a perturbation to the nuclear charges in a molecular
Hamiltonian. Expansions of this perturbation in a Taylor series, ie, quantum alchemy, have …