Max Veit

About Max Veit

Max Veit, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at École Polytechnique Fédérale de Lausanne, specializes in the field of Computational chemical physics, machine learning potentials, long-range interactions.

His recent articles reflect a diverse array of research interests and contributions to the field:

Incorporating explicit electrostatic interactions in machine learning potentials

Roadmap on machine learning in electronic structure

Dielectric response of BaTiO3 from an integrated machine learning model

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Efficient implementation of atom-density representations

Machine learning the molecular dipole moment with atomic partial charges and atomic dipoles

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

Representative trajectory data supporting" Multiscale Electrolyte Transport Simulations for Lithium Ion Batteries"

Max Veit Information

University

Position

Scientist (EPFL)

Citations(all)

306

Citations(since 2020)

300

Cited By

51

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

6

i10Index(since 2020)

6

Email

University Profile Page

Google Scholar

Max Veit Skills & Research Interests

Computational chemical physics

machine learning potentials

long-range interactions

Top articles of Max Veit

Incorporating explicit electrostatic interactions in machine learning potentials

Bulletin of the American Physical Society

2024/3/5

Max Veit
Max Veit

H-Index: 3

Dielectric response of BaTiO3 from an integrated machine learning model

APS March Meeting Abstracts

2022

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

npj Computational Materials

2022/9/29

Efficient implementation of atom-density representations

The Journal of Chemical Physics

2021/3/21

Machine learning the molecular dipole moment with atomic partial charges and atomic dipoles

APS March Meeting Abstracts

2021

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

The Journal of Chemical Physics

2020/6/27

Representative trajectory data supporting" Multiscale Electrolyte Transport Simulations for Lithium Ion Batteries"

2020/5/14

Ivan Korotkin
Ivan Korotkin

H-Index: 8

Max Veit
Max Veit

H-Index: 3

See List of Professors in Max Veit University(École Polytechnique Fédérale de Lausanne)

Co-Authors

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