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Stefan Chmiela

Technische Universitat Berlin

16H指数
52论文数
5.5K被引数
收录论文 17
发表时间
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Machine learning global atomic representations with Euclidean fast attention具有欧几里得快速注意力的机器学习全局原子表示
errNature Machine Intelligence
IF23.9
err2026-03-25
err0
PREAI
errJ. Thorben Frank; Stefan Chmiela; Klaus-Robert Müller; Oliver T. Unke
err分享
err收藏
Atomic orbits in molecules and materials for improving machine learning force fields分子和材料中的原子轨道用于改进机器学习力场
errmachine learning science and technology
IF0
err2025-07-16
err0
PREAI
errAnton Charkin-Gorbulin; Artem Kokorin; Huziel E Sauceda; Stefan Chmiela; Claudio Quarti; David Beljonne; Alexandre Tkatchenko; Igor Poltavsky
err分享
err收藏
Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach卤化物钙钛矿的稳定分子动力学模拟：基于温度-系综梯度域机器学习方法
errCHEMICAL PHYSICS LETTERS
IF3.1
err2025-05-01
err0
PREAI
errMendelsohn, Oz Y.; Hartstein, Michal; Chmiela, Stefan; Tkatchenko, Alexandre; Kronik, Leeor
err分享
err收藏
A Euclidean transformer for fast and stable machine learned force fields
errNATURE COMMUNICATIONS
IF15.7
err2024-08-06
err3
errOAAI
errFrank, J. Thorben; Unke, Oliver T.; Mueller, Klaus-Robert; Chmiela, Stefan
err分享
err收藏
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules (vol 14, 3562, 2023)
errNATURE COMMUNICATIONS
IF15.7
err2023-07-11
err0
errOAAI
errKabylda, Adil; Vassilev-Galindo, Valentin; Chmiela, Stefan; Poltavsky, Igor; Tkatchenko, Alexandre
err分享
err收藏
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
errNATURE COMMUNICATIONS
IF15.7
err2023-06-15
err14
errOAAI
errKabylda, Adil; Vassilev-Galindo, Valentin; Chmiela, Stefan; Poltavsky, Igor; Tkatchenko, Alexandre
err分享
err收藏
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence
errJOURNAL OF CHEMICAL THEORY AND COMPUTATION
IF5.5
err2023-05-08
err1
errOAAI
errBluecher, Stefan; Mueller, Klaus-Robert; Chmiela, Stefan
err分享
err收藏
Accurate global machine learning force fields for molecules with hundreds of atoms
errSCIENCE ADVANCES
IF12.5
err2023-01-13
err66
errOAAI
errChmiela, Stefan; Vassilev-Galindo, Valentin; Unke, Oliver T.; Kabylda, Adil; Sauceda, Huziel E.; Tkatchenko, Alexandre; Mueller, Klaus-Robert
err分享
err收藏
BIGDML-Towards accurate quantum machine learning force fields for materials
errNATURE COMMUNICATIONS
IF15.7
err2022-06-29
err44
errOAAI
errSauceda, Huziel E.; Galvez-Gonzalez, Luis E.; Chmiela, Stefan; Oliver Paz-Borbon, Lauro; Mueller, Klaus-Robert; Tkatchenko, Alexandre
err分享
err收藏
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effectsSpookyNet: 学习具有电子自由度和非局部效应的力场
errNATURE COMMUNICATIONS
IF15.7
err2021-12-14
err166
errOAAI
errUnke, Oliver T.; Chmiela, Stefan; Gastegger, Michael; Schuett, Kristof T.; Sauceda, Huziel E.; Mueller, Klaus-Robert
err分享
err收藏
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
errNATURE COMMUNICATIONS
IF15.7
err2021-01-19
err37
errOAAI
errSauceda, Huziel E.; Vassilev-Galindo, Valentin; Chmiela, Stefan; Mueller, Klaus-Robert; Tkatchenko, Alexandre
err分享
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Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2020-09-24
err33
errOAAI
errSauceda, Huziel E.; Gastegger, Michael; Chmiela, Stefan; Mueller, Klaus-Robert; Tkatchenko, Alexandre
err分享
err收藏
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2020-05-20
err42
errOAAI
errWang, Jiang; Chmiela, Stefan; Mueller, Klaus-Robert; Noe, Frank; Clementi, Cecilia
err分享
err收藏
sGDML: Constructing accurate and data efficient molecular force fields using machine learningsGDML: 使用机器学习构建准确且数据高效的分子力场
errCOMPUTER PHYSICS COMMUNICATIONS
IF3.4
err2019-07-01
err173
errOAAI
errChmiela, Stefan; Sauceda, Huziel E.; Poltavsky, Igor; Mueller, Klaus-Robert; Tkatchenko, Alexandre
err分享
err收藏
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
errJOURNAL OF CHEMICAL PHYSICS
IF3.1
err2019-03-18
err108
errOAAI
errSauceda, Huziel E.; Chmiela, Stefan; Poltavsky, Igor; Mueller, Klaus-Robert; Tkatchenko, Alexandre
err分享
err收藏
Machine learning of accurate energy-conserving molecular force fields精确能量保存分子力场的机器学习
errSCIENCE ADVANCES
IF12.5
err2017-05-05
err896
errOAAI
errChmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.; Poltavsky, Igor; Schuett, Kristof T.; Mueller, Klaus-Robert
err分享
err收藏
Quantum-chemical insights from deep tensor neural networks来自深度张量神经网络的量子化学见解
errNATURE COMMUNICATIONS
IF15.7
err2017-01-09
err1.1K
errOAAI
errSchuett, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Mueller, Klaus R.; Tkatchenko, Alexandre
err分享
err收藏

研究方向

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合作学者
合作期刊
D
David Beljonne
H 指数: 109 · 论文数: 837
L
Leeor Kronik
H 指数: 83 · 论文数: 503
A
Alexandre Tkatchenko
H 指数: 82 · 论文数: 513
F
Frank Noé
H 指数: 70 · 论文数: 405
C
Cecilia Clementi
H 指数: 55 · 论文数: 181
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