Predicting Thermal Properties of Crystals Using Machine Learning-Reference-Cited by-同舟云学术

Predicting Thermal Properties of Crystals Using Machine Learning

Published:2019-12-17 Issue:2 Volume:3 Page:1900208
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Tawfik Sherif Abdulkader¹^ORCID,Isayev Olexandr²,Spencer Michelle J. S.¹,Winkler David A.³⁴⁵⁶

Affiliation:

1. School of ScienceRMIT University GPO Box 2476 Melbourne Victoria 3001 Australia

2. Laboratory for Molecular ModelingDivision of Chemical Biology and Medicinal ChemistryUNC Eshelman School of PharmacyUniversity of North Carolina Chapel Hill NC 27599 USA

3. School of PharmacyUniversity of Nottingham Nottingham NG7 2RD UK

4. Monash Institute of Pharmaceutical SciencesMonash University 381 Royal Parade Parkville Victoria 3052 Australia

5. Latrobe Institute for Molecular ScienceLa Trobe University Kingsbury Drive Bundoora Victoria 3086 Australia

6. CSIRO Manufacturing Bayview Avenue Clayton Victoria 3168 Australia

Publisher

Wiley

Subject

Multidisciplinary,Modelling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.201900208

Reference29 articles.

1. The high-throughput highway to computational materials design

2. Universal fragment descriptors for predicting properties of inorganic crystals

3. How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

4. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

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