J. Thorben Frank

Beijing Institute of Big Data Research — China

Materials Science · Physical Sciences

7h-index183citations19works13.62yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Machine Learning in Materials Science(14), Protein Structure and Dynamics(5), Computational Drug Discovery Methods(4), Force Microscopy Techniques and Applications(3), Heat Transfer and Optimization(3)

Publications19 total

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Journal of the American Chemical Society·2025· 28 citedOpen Access
Sampling 3D Molecular Conformers with Diffusion Transformers
arXiv (Cornell University)·2025· 1 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 3 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 5 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
arXiv (Cornell University)·2024· 2 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2024· 17 citedOpen Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
A Euclidean transformer for fast and stable machine learned force fields
Nature Communications·2024· 60 citedOpen Access
Stress and heat flux via automatic differentiation
The Journal of Chemical Physics·2023· 16 citedOpen Access
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
arXiv (Cornell University)·2023· 1 citedOpen Access
Stress and heat flux via automatic differentiation
arXiv (Cornell University)·2023Open Access
Data and software: Stress and heat flux via automatic differentiation
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Data and software: Stress and heat flux via automatic differentiation
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Data and software: Stress and heat flux via automatic differentiation
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
arXiv (Cornell University)·2022· 15 citedOpen Access
Learning Neural Network Quantum States with the Linear Method
arXiv (Cornell University)·2021· 1 citedOpen Access

Frequent Co-authors

Stefan Chmiela(12), Oliver T. Unke(9), Klaus‐Robert Müller(9), Adil Kabylda(8), Alexandre Tkatchenko(8), Marcel F. Langer(5), Florian Knoop(5), Sergio Suarez Dou(4), Almaz Khabibrakhmanov(4), Leonardo Medrano Sandonas(4), Klaus-Robert Müller(4), Igor Poltavsky(4), Mirela Puleva(4), Anton Charkin-Gorbulin(4), Grégory Fonseca(4), Ilyes Batatia(4), Nicholas J. Browning(4), Mengnan Cui(4), Stefan Heinen(4), Bing Huang(4)