Publications by Max David Veit

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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 ...

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 ...

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 ...

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 ...

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 ...

Max David Veit

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

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

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Multi-Scale Electrolyte Transport Simulations for Lithium Ion Batteries

Efficient implementation of atom-density representations