Calculating material properties with purely data-driven methods

Author:

Papastamatiou Konstantinos1,Sofos Filippos1,Karakasidis Theodoros E.1

Affiliation:

1. Department of Physics/ School of Science/ Condensed Matter Physics Laboratory, University of Thessaly, Greece

Funder

Center of Research Innovation and Excellence of University of Thessaly, Special Account for Research Grants of University of Thessaly

Publisher

ACM

Reference28 articles.

1. Michael P Allen and Dominic J Tildesley . 2017. Computer Simulation of Liquids : Second Edition (2 nd ed.). Oxford University Press , Oxford . DOI:https://doi.org/10.1093/oso/9780198803195.001.0001 10.1093/oso Michael P Allen and Dominic J Tildesley. 2017. Computer Simulation of Liquids: Second Edition (2nd ed.). Oxford University Press, Oxford. DOI:https://doi.org/10.1093/oso/9780198803195.001.0001

2. Joshua P Allers , Jacob A Harvey , Fernando H Garzon , and Todd M Alam . 2020. Machine learning prediction of self-diffusion in Lennard-Jones fluids. The Journal of Chemical Physics ( 2020 ), 12. DOI:https://doi.org/10.1063/5.0011512 10.1063/5.0011512 Joshua P Allers, Jacob A Harvey, Fernando H Garzon, and Todd M Alam. 2020. Machine learning prediction of self-diffusion in Lennard-Jones fluids. The Journal of Chemical Physics (2020), 12. DOI:https://doi.org/10.1063/5.0011512

3. Widom Line for the Liquid–Gas Transition in Lennard-Jones System

4. S. Chapman T.G. Cowling D. Burnett and C. Cercignani. 1990. The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity Thermal Conduction and Diffusion in Gases. Cambridge University Press. Retrieved from https://books.google.gr/books?id=Cbp5JP2OTrwC S. Chapman T.G. Cowling D. Burnett and C. Cercignani. 1990. The Mathematical Theory of Non-uniform Gases: An Account of the Kinetic Theory of Viscosity Thermal Conduction and Diffusion in Gases. Cambridge University Press. Retrieved from https://books.google.gr/books?id=Cbp5JP2OTrwC

5. Miles Cranmer , Alvaro Sanchez-Gonzalez , Peter Battaglia , Rui Xu , Kyle Cranmer , David Spergel , and Shirley Ho . 2020 . Discovering Symbolic Models from Deep Learning with Inductive Biases. arXiv:2006.11287 [astro-ph, physics:physics, stat] (November 2020) . Retrieved April 12, 2021 from http://arxiv.org/abs/2006.11287 Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. 2020. Discovering Symbolic Models from Deep Learning with Inductive Biases. arXiv:2006.11287 [astro-ph, physics:physics, stat] (November 2020). Retrieved April 12, 2021 from http://arxiv.org/abs/2006.11287

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