upward

Machine learning and cheminformatics

S Manzhos, S Tsuda, M Ihara, Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality, PCCP 25, 1546 (2023)

H Kulik, T Hammerschmidt, J Schmidt etal, Roadmap on Machine learning in electronic structure, Electronic Structure 4, 023004 (2022)

L Fiedler, K Shah, M Bussmann, A Cangi, Deep dive into machine learning density functional theory for materials science and chemistry, PRM 6, 040301 (2022)

K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, C Park, A Choudhary, A Agrawal, S Billinge, E Holm, S Ong, C Wolverton, Recent advances and applications of deep learning methods in materials science, npj Computational Materials 8, 59 (2022)

M Ceriotti, C Clementi, O Anatole von Lilienfeld, Machine learning meets chemical physics, JCP 154, 160401 (2021)

N Szymanski, Y Zeng, H Huo, C Bartel, H Kim, G Ceder, Toward autonomous design and synthesis of novel inorganic materials, MH 8, 2169 (2021)

N Artrith, K Butler, F Coudert, S Han, O Isayev, A Jain, A Walsh, Best practices in machine learning for chemistry, NC 13, 505 (2021)

E A Olivetti,J M Cole, E Kim, O Kononova, G Ceder, T Y Han, A M Hiszpanski, Data-driven materials research enabled by natural language processing and information extraction, APR 7, 041317 (2020)

O A von Lilienfeld, K Muller, A Tkatchenko, Exploring chemical compound space with quantum-based machine learning, NRC 4, 347 (2020)

E Muratov, J Bajorath, R Sheridan etal, QSAR without borders, CSR 49, 3525 (2020)

S P Ong, Accelerating materials science with high-throughput computations and machine learning, CMS 161, 143 (2019)

P V Balachandran, Machine learning guided design of functional materials with targeted properties, CMS 164, 82 (2019)

G Carleo, I Cirac, K Cranmer, L Daudet, M Schuld, N Tishby, L Vogt-Maranto, L Zdeborova, Machine learning and the physical sciences, RMP 91, 045002 (2019)

K T Butler, D W Davies, H Cartwright, O Isayev, A Walsh, Machine learning for molecular and materials science, Nature 559, 547 (2018)

T Mueller, A G Kusne, R Ramprasad, Machine Learning in Materials Science: Recent Progress and Emerging Applications, RCC 29, 186 (2016)


Z Han, D Sarker, R Ouyang, A Mazheika, Y Gao, S Levchenko, Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence, Nat Commun 12, 1833 (2021) – dat

J Jang, G Gu, J Noh, J Kim, Y Jung, Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning, JACS 142, 18836 (2020)

R Ouyang, E Ahmetcik, C Carbogno, M Scheffler, L M Ghiringhelli, Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO, J Phys Mat 2, 024002 (2019)

R Ouyang, S Curtarolo, E Ahmetcik, M Scheffler, L M Ghiringhelli, SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates, PRM 2, 083802 (2018)

A Sifain, N Lubbers, B Nebgen, J S Smith, A Y Lokhov, O Isayev, A E Roitberg, K Barros, S Tretiak, Discovering a Transferable Charge Assignment Model Using Machine Learning, JPCL 9, 4495 (2018)

Y Zhuo, A Tehrani, A Oliynyk, A Duke, J Brgoch, Identifying an efficient, thermally robust inorganic phosphor host via machine learning, Nat Commun 9, 4377 (2018)

L Ward, A Agrawal, A Choudhary, C Wolverton, A general-purpose machine learning framework for predicting properties of inorganic materials, npj Comput Mater 2, 16028 (2016) – zip

Software

J Hachmann, ChemML - machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data

G Landrum, RDKit - Open-Source Cheminformatics Software