Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, weâ€¦ (More)

Using the Kaczmarz algorithm, we obtain a Fourier series formulation for functions in the L2 space of singular measures on the unit circle. This formula is applied to the problem of findingâ€¦ (More)

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. Theâ€¦ (More)

The origin of the size-dependent Stokes shift in CsPbBr3 nanocrystals (NCs) is explained for the first time. Stokes shifts range from 82 to 20 meV for NCs with effective edge lengths varying from âˆ¼4â€¦ (More)

Using the Kaczmarz algorithm, we prove that for any singular Borel probability measure Î¼ on [0, 1), every f âˆˆ L2(Î¼) possesses a Fourier series of the form f (x) = âˆ‘n=0 cne. We show that theâ€¦ (More)

Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is toâ€¦ (More)

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forcesâ€¦ (More)