115 citations · 313 across the 4 of their papers we have counts for
4 papers
Thermodynamics and dielectric response of by data-driven modeling
Lorenzo Gigli, Max Veit, Michele Kotiuga +3
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate descri…
Efficient implementation of atom-density representations
Félix Musil, Max Veit, Alexander Goscinski +5
Physically-motivated and mathematically robust atom-centred representations of molecular structures are key to the success of modern atomistic machine learning (ML) methods. They l…
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
Max Veit, David M. Wilkins, Yang Yang +2
The molecular dipole moment () is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra, as well as induction a…
Equation of state of fluid methane from first principles with machine learning potentials
Max Veit, Sandeep Kumar Jain, Satyanarayana Bonakala +3
The predictive simulation of molecular liquids requires models that are not only accurate, but computationally efficient enough to handle the large systems and long time scales req…