Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Graph-based deep learning frameworks for molecules and solid-state materials

Journal Article · · Computational Materials Science

Not provided.

Research Organization:
Temple Univ., Philadelphia, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0020310
OSTI ID:
1853566
Journal Information:
Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 195; ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

References (37)

Quantum-chemical insights from deep tensor neural networks journal January 2017
Learning atoms for materials discovery journal June 2018
Extended-Connectivity Fingerprints journal April 2010
SchNet – A deep learning architecture for molecules and materials journal June 2018
DeepTox: Toxicity Prediction using Deep Learning journal February 2016
Quantum chemistry structures and properties of 134 kilo molecules journal August 2014
Deep learning for automated classification and characterization of amorphous materials journal January 2020
Machine learning: Trends, perspectives, and prospects journal July 2015
Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride journal February 2019
Machine learning reveals orbital interaction in materials journal January 2017
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons journal April 2010
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation journal July 2013
OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features journal September 2020
Deep elastic strain engineering of bandgap through machine learning journal February 2019
Deep learning for computational chemistry journal March 2017
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions journal November 2019
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity journal October 2016
Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data journal January 2009
Geometric Deep Learning: Going beyond Euclidean data journal July 2017
Deep learning journal May 2015
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning journal January 2012
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials journal June 2019
970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13 journal July 2009
Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction journal January 2020
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals journal April 2019
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies journal July 2013
Multi-fidelity machine learning models for accurate bandgap predictions of solids journal March 2017
Machine learning unifies the modeling of materials and molecules journal December 2017
Analyzing Learned Molecular Representations for Property Prediction journal July 2019
Machine learning of molecular electronic properties in chemical compound space journal September 2013
Molecular graph convolutions: moving beyond fingerprints journal August 2016
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach journal April 2015
Machine learning of accurate energy-conserving molecular force fields journal May 2017
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties journal April 2018
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space journal June 2015
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism journal August 2019
Machine-learning-assisted materials discovery using failed experiments journal May 2016

Similar Records

Graph-based deep learning frameworks for molecules and solid-state materials
Journal Article · 2021 · Computational Materials Science · OSTI ID:1783312

Graph neural network for Hamiltonian-based material property prediction
Journal Article · 2021 · Neural Computing and Applications · OSTI ID:1976637

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Journal Article · 2019 · Chemistry of Materials · OSTI ID:1564028

Related Subjects