From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
- 12 February 2016
- journal article
- research article
- Published by American Chemical Society (ACS) in Chemistry of Materials
- Vol. 28 (5), 1304-1311
- https://doi.org/10.1021/acs.chemmater.5b04109
Abstract
No abstract availableKeywords
Funding Information
- Office of Naval Research (N00014-10-1-0944, N00014-15-1-2665)
- Laboratory Directed Research and Development (20140679PRD3)
This publication has 38 references indexed in Scilit:
- How Maxwell's equations came to lightNature Photonics, 2014
- The quantum centennialNature, 2000
- The Landau Theory of Phase TransitionsWorld Scientific Lecture Notes in Physics, 1987
- Newton's Discovery of GravityScientific American, 1981
- Yield Point Phenomena in Metals and AlloysPublished by Springer Science and Business Media LLC ,1970
- Science and PhenomenologyNature, 1969
- On the theory of dielectric breakdown in solidsProceedings of the Royal Society of London. Series A - Mathematical and Physical Sciences, 1947
- THEORY OF DIELECTRIC BREAKDOWN*Nature, 1943
- Electric Breakdown of Solid and Liquid InsulatorsJournal of Applied Physics, 1937
- Theory of electrical breakdown in ionic crystalsProceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1937