Stefan Chmiela

发表

Towards exact molecular dynamics simulations with machine-learned force fields

K. Müller, A. Tkatchenko, Stefan Chmiela, 2018, Nature Communications.

Quantum-chemical insights from deep tensor neural networks

Alexandre Tkatchenko, Farhad Arbabzadah, Kristof T. Schütt, 2016, Nature Communications.

Machine learning of accurate energy-conserving molecular force fields

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2016, Science Advances.

Towards exact molecular dynamics simulations with invariant machine-learned models

Stefan Chmiela, Stefan Chmiela, 2019 .

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces.

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2019, The Journal of chemical physics.

Machine Learning Force Fields

Klaus-Robert Müller, Michael Gastegger, Alexandre Tkatchenko, 2020, Chemical reviews.

BIGDML—Towards accurate quantum machine learning force fields for materials

Luis E. Gálvez-González, A. Tkatchenko, Stefan Chmiela, 2022, Nature communications.

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

Klaus-Robert Müller, Michael Gastegger, Oliver T. Unke, 2021, Nature Communications.

Accurate and Data-Efficient Machine Learning Force Fields for Periodic Systems

Klaus-Robert Müller, Alexandre Tkatchenko, Stefan Chmiela, 2020 .

Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields

Stefan Chmiela, Klaus-Robert Müller, Niklas Schmitz, 2022, ArXiv.

Accurate global machine learning force fields for molecules with hundreds of atoms

Oliver T. Unke, A. Tkatchenko, Stefan Chmiela, 2022, 2209.14865.

Local Function Complexity for Active Learning via Mixture of Gaussian Processes

S. Nakajima, Danny Panknin, Stefan Chmiela, 2019, 1902.10664.

Accurate global machine learning force fields for molecules with hundreds of atoms

Oliver T. Unke, A. Tkatchenko, Stefan Chmiela, 2022, Science advances.

Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields

Stefan Chmiela, Klaus-Robert Müller, Niklas Schmitz, 2022, The journal of physical chemistry letters.

Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules

A. Tkatchenko, Stefan Chmiela, I. Poltavsky, 2022, 2209.03985.

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

Stefan Chmiela, Klaus-Robert Müller, Niklas Schmitz, 2022, The journal of physical chemistry letters.

Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence

Stefan Chmiela, Klaus-Robert Müller, Stefan Blücher, 2022, ArXiv.

Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules

A. Tkatchenko, Stefan Chmiela, I. Poltavsky, 2022, 2209.03985.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

A. Tkatchenko, M. Gastegger, Stefan Chmiela, 2021, Chemical reviews.

Machine Learning Force Fields

Michael Gastegger, Klaus-Robert Muller, Alexandre Tkatchenko, 2020, Chemical reviews.

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

K. Müller, A. Tkatchenko, Stefan Chmiela, 2020, Nature communications.

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Klaus-Robert Muller, Alexandre Tkatchenko, Stefan Chmiela, 2019, Machine Learning Meets Quantum Physics.

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Klaus-Robert Müller, Alexandre Tkatchenko, Pieter-Jan Kindermans, 2017, NIPS.

BIGDML—Towards accurate quantum machine learning force fields for materials pdf

Klaus-Robert Müller, Alexandre Tkatchenko, Stefan Chmiela, 2021, Nature Communications.

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields.

K. Müller, A. Tkatchenko, M. Gastegger, 2020, The Journal of chemical physics.

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Klaus-Robert Müller, Alexandre Tkatchenko, Igor Poltavsky, 2018, Comput. Phys. Commun..

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature

A. Tkatchenko, Stefan Chmiela, H. E. Sauceda, 2021, Nature Communications.

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach.

Cecilia Clementi, Klaus-Robert Muller, Stefan Chmiela, 2020, The Journal of chemical physics.

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

A. Tkatchenko, Stefan Chmiela, H. E. Sauceda, 2019, Machine Learning Meets Quantum Physics.

Machine Learning Meets Quantum Physics

A. Tkatchenko, K. Tsuda, O. A. V. Lilienfeld, 2020, Lecture Notes in Physics.

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

A. Tkatchenko, Stefan Chmiela, I. Poltavsky, 2023, Nature communications.

Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence

Stefan Chmiela, Klaus-Robert Müller, Stefan Blücher, 2022, Journal of Chemical Theory and Computation.

Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems pdf

Stefan Chmiela, Thorben Frank, Stefan Chmiela, 2021, ArXiv.

From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields

Oliver T. Unke, Stefan Chmiela, J. Frank, 2023, ArXiv.

Introduction

A. Tkatchenko, O. A. von Lilienfeld, Stefan Chmiela, 2020, Machine Learning Meets Quantum Physics.

Local Function Complexity for Active Learning via Mixture of Gaussian Processes

S. Nakajima, Danny Panknin, Stefan Chmiela, 2019, 1902.10664.