A systematic approach to generating accurate neural network potentials: the case of carbon (original) (raw)

MACHINE LEARNING POTENTIALS FOR GRAPHENE

Akash Singh

ASME International Mechanical Engineering Congress and Exposition, 2022

View PDFchevron_right

Development of Artificial Neural Network Potential for Graphene

Akash Singh

AIAA SciTech, 2020

View PDFchevron_right

Reliable machine learning potentials based on artificial neural network for graphene

Akash Singh, Yumeng Li

Elsevier, 2023

View PDFchevron_right

The Rise of Neural Networks for Materials and Chemical Dynamics

Maksim Kulichenko

The Journal of Physical Chemistry Letters

View PDFchevron_right

Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

Xiaoying Zhuang

Carbon, 2021

View PDFchevron_right

Combining Programmable Potentials and Neural Networks for Materials Problems

Ananta Bhattarai

2021

View PDFchevron_right

Atomistic Line Graph Neural Network for improved materials property predictions

Kamal Choudhary

npj Computational Materials

View PDFchevron_right

Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces from Modified High Dimensional Neural Networks for Applications in Energy Storage

Vidushi Sharma

2022

View PDFchevron_right

Simple machine-learned interatomic potentials for complex alloys

Flyura Djurabekova

Physical Review Materials

View PDFchevron_right

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Daniele Giofré

The Journal of Chemical Physics

View PDFchevron_right

Crystal structure representations for machine learning models of formation energies

Rickard Armiento

International Journal of Quantum Chemistry, 2015

View PDFchevron_right

Convolutional neural networks for atomistic systems

Iryna Luchak

Computational Materials Science

View PDFchevron_right

A machine-learned interatomic potential for silica and its relation to empirical models

Karsten Albe

npj Computational Materials

View PDFchevron_right

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Hsuan Ming Yu

2022

View PDFchevron_right

Learning atoms for materials discovery

Qimin Yan

Proceedings of the National Academy of Sciences of the United States of America, 2018

View PDFchevron_right

StrainNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks

Thiparat Chotibut

arXiv (Cornell University), 2023

View PDFchevron_right

Deep learning reveals key aspects to help interpret the structure-property relationships of materials

duong Nguyen

Research Square (Research Square), 2023

View PDFchevron_right

Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning ACS Nano XXXX, XXX, XXX−XXX

Nathan C . Frey

ACS Nano, 2019

View PDFchevron_right

Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds

Kamal Choudhary

Physical Review Materials

View PDFchevron_right

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Michael Bussmann

2021

View PDFchevron_right

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions

James Stevenson

ChemRxiv, 2021

View PDFchevron_right

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

Sergei Manzhos

International Journal of Quantum Chemistry, 2014

View PDFchevron_right

Graph neural network for Hamiltonian-based material property prediction

Jeng Yuan Tsai

Neural Computing and Applications

View PDFchevron_right

Taking materials dynamics to new extremes using machine learning interatomic potentials

Journal of Materials Informatics

Journal of Materials Informatics, 2021

View PDFchevron_right

Recent Advances and Applications of Deep Learning Methods in Materials Science

Kamal Choudhary

2021

View PDFchevron_right

Distributed representations of atoms and materials for machine learning

Ricardo Grau-Crespo

npj Computational Materials

View PDFchevron_right

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

Kamal Choudhary

Physical Review Materials

View PDFchevron_right

DScribe: Library of descriptors for machine learning in materials science

Eiaki Morooka

Computer Physics Communications

View PDFchevron_right

Opportunities and Challenges for Machine Learning in Materials Science

Ms. KANUPRIYA KHANDELWAL

Annual Review of Materials Research, 2020

View PDFchevron_right

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Stefan Goedecker

Nature Communications

View PDFchevron_right

Deep learning model to predict fracture mechanisms of graphene

Chi-Hua Yu

npj 2D Materials and Applications, 2021

View PDFchevron_right

Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials

Xiaoying Zhuang

Applied Materials Today, 2020

View PDFchevron_right