Phosphorescent Material Search Using a Combination of High-Throughput Evaluation and Machine Learning
Author:
Affiliation:
1. Toyota Central R&D Labs, Inc., Nagakute, Aichi 480-1192, Japan
2. DENSO CORPORATION, Nisshin, Aichi 470-0111, Japan
Publisher
American Chemical Society (ACS)
Subject
Inorganic Chemistry,Physical and Theoretical Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.inorgchem.9b01370
Reference27 articles.
1. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
2. Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling
3. Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)
4. Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity
5. Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
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