scholarly journals Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: CsPbBr3 , a phonon liquid with Cs rattlers

2022 ◽  
Vol 105 (2) ◽  
Author(s):  
Jonathan Lahnsteiner ◽  
Menno Bokdam
Keyword(s):  
Machine Learning ◽  
Lattice Dynamics ◽  
Force Fields ◽  
Anharmonic Lattice

CONI-Net: Machine Learning of Separable Intermolecular Force Fields

2021 ◽  
Author(s):  
Manuel Konrad ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Intermolecular Force

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...

Computational Aspects of Anharmonic Lattice Dynamics

1972 ◽  
pp. 339-350
Author(s):  
T. R. Koehler
Keyword(s):  
Lattice Dynamics ◽  
Anharmonic Lattice ◽  
Computational Aspects

scholarly journals Origin of anomalous anharmonic lattice dynamics of lead telluride

2014 ◽  
Vol 7 (4) ◽  
pp. 041801 ◽  
Author(s):  
Takuma Shiga ◽  
Takuru Murakami ◽  
Takuma Hori ◽  
Olivier Delaire ◽  
Junichiro Shiomi
Keyword(s):  
Lattice Dynamics ◽  
Lead Telluride ◽  
Anharmonic Lattice

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.

Anharmonic lattice dynamics of solid potassium

1977 ◽  
Vol 16 (8) ◽  
pp. 3476-3483 ◽  
Author(s):  
H. R. Glyde ◽  
J. P. Hansen ◽  
M. L. Klein
Keyword(s):  
Lattice Dynamics ◽  
Anharmonic Lattice

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
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