Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons

2021 ◽  
Author(s):  
Amir Hajibabaei ◽  
Miran Ha ◽  
Saeed Pourasad ◽  
Junu Kim ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Force Fields

Machine-Learning-Assisted First-Principles Calculations of Strained InAs1−xSbx Alloys for Curved Focal-Plane Arrays

2021 ◽  
Vol 15 (6) ◽  
Author(s):  
Alexandros Kyrtsos ◽  
John Glennon ◽  
Andreu Glasmann ◽  
Mark R. O’Masta ◽  
Binh-Minh Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
First Principles Calculations

First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

2021 ◽  
Author(s):  
Mohammad Zafari ◽  
Arun S. Nissimagoudar ◽  
Muhammad Umer ◽  
Geunsik Lee ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
First Principles ◽  
Conjugated Polymer ◽  
2D Materials ◽  
Single Atom ◽  
Vacancy Defects ◽  
Catalytic Activities ◽  
Polymer Supported ◽  
New Machine

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

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.

Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations

2017 ◽  
Vol 25 (7) ◽  
pp. 075003 ◽  
Author(s):  
Tomoyuki Tamura ◽  
Masayuki Karasuyama ◽  
Ryo Kobayashi ◽  
Ryuichi Arakawa ◽  
Yoshinori Shiihara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Boundaries ◽  
First Principles ◽  
Local Energy ◽  
First Principles Calculations
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