Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Top Cited Papers
- 19 October 2017
- journal article
- research article
- Published by American Chemical Society (ACS) in Chemistry of Materials
- Vol. 29 (21), 9436-9444
- https://doi.org/10.1021/acs.chemmater.7b03500
Abstract
No abstract availableKeywords
Funding Information
- U.S. Department of Energy (EDCBEE)
- Massachusetts Institute of Technology (MIT Energy Initiative)
- U.S. Department of Defense (N00014-16-1-2432)
- Government of Canada
- National Science Foundation (1534340)
This publication has 56 references indexed in Scilit:
- Materials science with large-scale data and informatics: Unlocking new opportunitiesMRS Bulletin, 2016
- From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric BreakdownChemistry of Materials, 2016
- Computational predictions of energy materials using density functional theoryNature Reviews Materials, 2016
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energiesnpj Computational Materials, 2015
- Big–deep–smart data in imaging for guiding materials designNature Materials, 2015
- The Materials Super Highway: Integrating High-Throughput Experimentation into Mapping the Catalysis Materials GenomeCatalysis Letters, 2014
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovationAPL Materials, 2013
- The high-throughput highway to computational materials designNature Materials, 2013
- The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community GridThe Journal of Physical Chemistry Letters, 2011
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional TheoryChemistry of Materials, 2010