Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields

2020 ◽  
Vol 124 (40) ◽  
pp. 22127-22136 ◽  
Author(s):  
James Chapman ◽  
Rampi Ramprasad
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Surface Phenomena ◽  
Nanoscale Modeling

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals A universal framework for featurization of atomistic systems

2021 ◽  
Author(s):  
Xiangyun Lei ◽  
Andrew Medford
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Force Fields ◽  
Reactive Systems ◽  
Quantum Molecular Dynamics ◽  
Learning Models ◽  
Fixed Dimension ◽  
Comparable Performance ◽  
Invaluable Tool ◽  
Dynamics Simulations

Abstract Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.

scholarly journals Machine learning of correlated dihedral potentials for atomistic molecular force fields

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Pascal Friederich ◽  
Manuel Konrad ◽  
Timo Strunk ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies

2021 ◽  
Author(s):  
Saientan Bag ◽  
Manuel Konrad ◽  
Tobias Schlöder ◽  
Pascal Friederich ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Adsorption Energies

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

2019 ◽  
Vol 59 (10) ◽  
pp. 4278-4288 ◽  
Author(s):  
James L. McDonagh ◽  
Ardita Shkurti ◽  
David J. Bray ◽  
Richard L. Anderson ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Coarse Grained ◽  
Molecular Force

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

2019 ◽  
Vol 240 ◽  
pp. 38-45 ◽  
Author(s):  
Stefan Chmiela ◽  
Huziel E. Sauceda ◽  
Igor Poltavsky ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

scholarly journals Building Machine Learning Force Fields of Proteins with Fragment-Based Approach and Data Transfer

2021 ◽  
Author(s):  
Zheng Cheng ◽  
Jiahui Du ◽  
Lei Zhang ◽  
Jing Ma ◽  
Wei Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Transfer ◽  
Force Fields
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