Publications by Max David Veit

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Jigyasa Nigam, Andrea Grisafi, Max David Veit

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Nicola Marzari, Michele Ceriotti, Giovanni Pizzi, Michele Kotiuga, Max David Veit

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 description of the electronic processes and the thermodynamic equilibrium that driv ...

NATURE PORTFOLIO2022

Efficient implementation of atom-density representations

Michele Ceriotti, Guillaume André Jean Fraux, Till Junge, Félix Benedito Clément Musil, Michael John Willatt, Max David Veit, Alexander Jan Goscinski

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

2021

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

Michele Ceriotti, David Mark Wilkins, Max David Veit, Yang Yang

The molecular dipole moment (mu) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly-via t ...

AMER INST PHYSICS2020

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

Max David Veit

The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macrosco ...

AMER CHEMICAL SOC2019

Multi-Scale Electrolyte Transport Simulations for Lithium Ion Batteries

Max David Veit

Establishing a link between atomistic processes and battery cell behavior is a major challenge for lithium ion batteries. Focusing on liquid electrolytes, we describe parameter-free molecular dynamics predictions of their mass and charge transport properti ...

2019