Fragmentation and embedding schemes are of great importance when applying quantum-
chemical calculations to more complex and attractive targets. The divide-and-conquer (DC) …

Modern semiempirical electronic structure methods have considerable promise in drug
discovery as universal “force fields” that can reliably model biological and drug-like …

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 …

We have introduced a machine learning workflow that allows for optimizing electronic
properties in the density functional tight binding method (DFTB). The workflow allows for the …

We present a new approach to studying nanoparticle collisions using density functional
based tight binding (DFTB). A novel DFTB parametrization has been developed to study the …

Despite their high gravimetric and volumetric energy densities, boron (B) particles suffer
from poor oxidative energy release rates as the boron oxide (B2O3) shell impedes the …

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 …

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular
dynamics simulations using a machine-learned interatomic potential. The model is shown to …

The increasing popularity of machine learning (ML) approaches in computational modeling,
most prominently ML interatomic potentials, opened possibilities that were unthinkable only …

First-principles electronic structure simulations are an invaluable tool for understanding
chemical bonding and reactions. While machine-learning models such as interatomic …