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Machine learning and cheminformatics

P Xu, H Chen, M Li, W Lu, New Opportunity: Machine Learning for Polymer Materials Design and Discovery, ATS 5, 2100565 (2022)

J Janet, C Duan, A Nandy, F Liu, H Kulik, Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design, ACR 54, 532 (2021)

A Saeki, K Kranthiraja, A high throughput molecular screening for organic electronics via machine learning: present status and perspective, Japan J Appl Phys 59, SD0801 (2020)


S Monaco, R Baer, R Giernacky, M Villalba, T Garcia, C Mora-Perez, S Brady, K Erlitz, C Kunkel, S Jezowski, H Oberhofer, C Lange, B Schatschneider, Electronic property trends of single-component organic molecular crystals containing C, N, O, and H, CMS 197, 110510 (2021)

W Tatum, D Torrejon, A Resing, J Onorato, C Luscombe, Algorithmically extracted morphology descriptions for predicting device performance, CMS 197, 110599 (2021)

J Janet, S Ramesh, C Duan, H Kulik, Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization, ACS Cent Sci 6, 513 (2020)

F Musil, S De, J Yang, J Campbell, G Day, M Ceriotti, Machine learning for the structure-energy-property landscapes of molecular crystals, Chem Sci 9, 1289 (2018) – zip