Nowadays, many chemical investigations are supported by routine calculations of molecular
structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share …

This review covers a family of atomistic, mostly quantum chemistry (QC) based
semiempirical methods for the fast and reasonably accurate description of large molecules …

The powerful independent gradient model (IGM) method has been increasingly popular in
visual analysis of intramolecular and intermolecular interactions in recent years. However …

This work introduces a diffusion model for molecule generation in 3D that is equivariant to
Euclidean transformations. Our E (3) Equivariant Diffusion Model (EDM) learns to denoise a …

The evaluation of molecular electrostatic potential (ESP) is a performance bottleneck for
many computational chemical tasks like restrained ESP charge fitting or quantum …

Optoelectronic devices consist of heterointerfaces formed between dissimilar
semiconducting materials. The relative energy-level alignment between contacting …

We propose and discuss an efficient scheme for the in silico sampling for parts of the
molecular chemical space by semiempirical tight-binding methods combined with a meta …

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

Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …

Molecular conformer generation is a fundamental task in computational chemistry. Several
machine learning approaches have been developed, but none have outperformed state-of …