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

Nature communications, 2021-12, Vol.12 (1), p.7273-7273, Article 7273

2021

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Towards exact molecular dynamics simulations with machine-learned force fields

Nature communications, 2018-09, Vol.9 (1), p.3887-10, Article 3887

2018

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Quantum-chemical insights from deep tensor neural networks

Nature communications, 2017-01, Vol.8 (1), p.13890-13890, Article 13890

2017

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

The Journal of chemical physics, 2019-03, Vol.150 (11), p.114102-114102

2019

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

Computer physics communications, 2019-07, Vol.240, p.38-45

2019

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

The Journal of chemical physics, 2020-05, Vol.152 (19), p.194106-194106

2020

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

Nature communications, 2021-01, Vol.12 (1), p.442-442, Article 442

2021

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Machine learning of accurate energy-conserving molecular force fields

Science advances, 2017-05, Vol.3 (5), p.e1603015-e1603015

2017

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

The Journal of chemical physics, 2020-09, Vol.153 (12), p.124109-124109

2020

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

Nature communications, 2023-06, Vol.14 (1), p.3562-3562, Article 3562

2023

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

Nature communications, 2023-07, Vol.14 (1), p.4116-4116, Article 4116

2023

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BIGDML—Towards accurate quantum machine learning force fields for materials

Nature communications, 2022-06, Vol.13 (1), p.3733-3733, Article 3733

2022

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Machine Learning Meets Quantum Physics

1st ed. 2020., 2020

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

Chemical reviews, 2021-08, Vol.121 (16), p.9816-9872

2021

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Machine Learning Force Fields

Chemical reviews, 2021-08, Vol.121 (16), p.10142-10186

2021

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Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence

Journal of chemical theory and computation, 2023-07, Vol.19 (14), p.4619-4630

2023

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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

The journal of physical chemistry letters, 2022-11, Vol.13 (43), p.10183-10189

2022

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

Science advances, 2023-01, Vol.9 (2), p.eadf0873-eadf0873

2023

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Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

Machine Learning Meets Quantum Physics, p.129-154

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

Machine Learning Meets Quantum Physics, p.277-307

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Introduction

Machine Learning Meets Quantum Physics, 2020, p.1-4

2020

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Introduction

Machine Learning Meets Quantum Physics, 2020, Vol.968

2020

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Part III Deep Learning of Atomistic Representations

Machine Learning Meets Quantum Physics, 2020, Vol.968

2020

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Part I Fundamentals

Machine Learning Meets Quantum Physics, 2020, Vol.968

2020

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Intro

Machine Learning Meets Quantum Physics, 2020, Vol.968

2020

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Weitere Dienste

Einstellungen

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

Towards exact molecular dynamics simulations with machine-learned force fields

Quantum-chemical insights from deep tensor neural networks

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

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

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

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

Machine learning of accurate energy-conserving molecular force fields

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

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

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

BIGDML—Towards accurate quantum machine learning force fields for materials

Machine Learning Meets Quantum Physics

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

Machine Learning Force Fields

Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

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

Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches

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

Introduction

Introduction

Part III Deep Learning of Atomistic Representations

Part I Fundamentals

Intro

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