Kristof T. Schütt

Kristof T. Schütt

Technische Universität Berlin

H-index: 26

Europe-Germany

About Kristof T. Schütt

Kristof T. Schütt, With an exceptional h-index of 26 and a recent h-index of 25 (since 2020), a distinguished researcher at Technische Universität Berlin, specializes in the field of machine learning, deep learning, quantum chemistry.

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

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

Automatic identification of chemical moieties

Roadmap on machine learning in electronic structure

Inverse design of 3d molecular structures with conditional generative neural networks

A standing molecule as a coherent single-electron field emitter

Perspective on integrating machine learning into computational chemistry and materials science

Machine learning force fields

Kristof T. Schütt Information

University

Position

___

Citations(all)

10093

Citations(since 2020)

9394

Cited By

3406

hIndex(all)

26

hIndex(since 2020)

25

i10Index(all)

31

i10Index(since 2020)

30

Email

University Profile Page

Technische Universität Berlin

Google Scholar

View Google Scholar Profile

Kristof T. Schütt Skills & Research Interests

machine learning

deep learning

quantum chemistry

Top articles of Kristof T. Schütt

Title

Journal

Author(s)

Publication Date

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Tuan Le

Julian Cremer

Frank Noé

Djork-Arné Clevert

Kristof Schütt

2024

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

The Journal of Chemical Physics

Kristof T Schütt

Stefaan SP Hessmann

Niklas WA Gebauer

Jonas Lederer

Michael Gastegger

2023/4/14

Automatic identification of chemical moieties

Physical Chemistry Chemical Physics

Jonas Lederer

Michael Gastegger

Kristof T Schütt

Michael Kampffmeyer

Klaus-Robert Müller

...

2023

Roadmap on machine learning in electronic structure

Electronic Structure

Heather J Kulik

Thomas Hammerschmidt

Jonathan Schmidt

Silvana Botti

Miguel AL Marques

...

2022/8/19

Inverse design of 3d molecular structures with conditional generative neural networks

Nature Communications

Niklas WA Gebauer

Michael Gastegger

Stefaan SP Hessmann

Klaus-Robert Müller

Kristof T Schütt

2022

A standing molecule as a coherent single-electron field emitter

Taner Esat

Marvin Knol

Philipp Leinen

Matthew FB Green

Malte Esders

...

2021/7/5

Perspective on integrating machine learning into computational chemistry and materials science

The Journal of Chemical Physics

Julia Westermayr

Michael Gastegger

Kristof T Schütt

Reinhard J Maurer

2021/6/21

Machine learning force fields

Oliver T Unke

Stefan Chmiela

Huziel E Sauceda

Michael Gastegger

Igor Poltavsky

...

2021/3/11

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

Nature Communications

Oliver T Unke

Stefan Chmiela

Michael Gastegger

Kristof T Schütt

Huziel E Sauceda

...

2021/5/1

Equivariant message passing for the prediction of tensorial properties and molecular spectra

Kristof T Schütt

Oliver T Unke

Michael Gastegger

2021

Datasets: Machine learning of solvent effects on molecular spectra and reactions

Michael Gastegger

Kristof T Schütt

Klaus-Robert Müller

2021/7/26

Machine learning of solvent effects on molecular spectra and reactions

Chemical Science

Michael Gastegger

Kristof T Schütt

Klaus Robert Mueller

2021/7/23

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

The Journal of Chemical Physics

Michael Gastegger

Adam McSloy

Michael Luya

Kristof T Schütt

Reinhard J Maurer

2020/7/28

Machine learning meets quantum physics

Lecture Notes in Physics

Kristof T Schütt

Stefan Chmiela

O Anatole Von Lilienfeld

Alexandre Tkatchenko

Koji Tsuda

...

2020

Learning representations of molecules and materials with atomistic neural networks

Machine Learning Meets Quantum Physics

Kristof T Schütt

Alexandre Tkatchenko

Klaus-Robert Müller

2020

Autonomous robotic nanofabrication with reinforcement learning

Science Advances

Philipp Leinen

Malte Esders

Kristof T Schütt

Christian Wagner

Klaus-Robert Müller

...

2020/9/2

See List of Professors in Kristof T. Schütt University(Technische Universität Berlin)

Co-Authors

H-index: 156
Klaus-Robert Müller

Klaus-Robert Müller

Technische Universität Berlin

H-index: 83
Alexandre Tkatchenko

Alexandre Tkatchenko

Université du Luxembourg

H-index: 38
Grégoire Montavon

Grégoire Montavon

Technische Universität Berlin

H-index: 24
Michael Gastegger

Michael Gastegger

Technische Universität Berlin

H-index: 19
Oliver T. Unke

Oliver T. Unke

Technische Universität Berlin

H-index: 18
Huziel E. Sauceda

Huziel E. Sauceda

Technische Universität Berlin

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