Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics

Michael Moran, Michael W. Gaultois, Vladimir V. Gusev, Dmytro Antypov and Matthew J. Rosseinsky

Digital Discovery, 2025, 4, 790
DOI: 10.1039/D4DD00202D

Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models

Chi Zhang, Dmytro Antypov, Matthew J. Rosseinsky and Matthew S. Dyer

Digital Discovery, 2024, 3, 2509
DOI: 10.1039/D4DD00161C

Composition-property extrapolation for compositionally complex solid solutions based on word embeddings

Lei Zhang, Lars Banko, Wolfgang Schuhmann, Alfred Ludwig and Markus Stricker

Digital Discovery, 2025, 4, 1578
DOI: 10.1039/D5DD00169B

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

Federico Ottomano, John Y. Goulermas, Vladimir Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa Partida Manzanera, Emmeline G. Poole, Matthew S. Dyer, John B. Claridge, Jon Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin Sanderson and Matthew J. Rosseinsky

Digital Discovery, 2025, 4, 1794
DOI: 10.1039/D5DD00010F

Comprehensive Review on the Use of Machine Learning Techniques Applied to the Ultrasound Data for the Characterisation of Porosity across Carbon Fibre Reinforced Polymer Layers

Mihai M. Vasilache, Rostand B. Tayong and Vladan Velisavljevic

Appl Compos Mater, 2025, 32, 1315
DOI: 10.1007/s10443-025-10342-4

Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks

Kevin A. Spiekermann, Xiaorui Dong, Angiras Menon, William H. Green, Mark Pfeifle, Frederik Sandfort, Oliver Welz and Maike Bergeler

J. Phys. Chem. A, 2024, 128, 8384
DOI: 10.1021/acs.jpca.4c04121

Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening

Jian‐Ming Liao and Hui‐Hsu Gavin Tsai

Solar RRL, 2024, 8
DOI: 10.1002/solr.202400287

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

Miaoxian Guo, Jin Zhou, Xing Li, Zhijian Lin and Weicheng Guo

Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-42968-4

Prediction of flow stress of Ta–W alloys using machine learning

A Kedharnath, Rajeev Kapoor and Apu Sarkar

Mach. Learn.: Sci. Technol., 2024, 5, 045007
DOI: 10.1088/2632-2153/ad8061

Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning

Hajime Shimakawa, Akiko Kumada and Masahiro Sato

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-023-01194-2

Machine Learning Validation via Rational Dataset Sampling with astartes

Jackson W. Burns, Kevin A. Spiekermann, Himaghna Bhattacharjee, Dionisios G. Vlachos and William H. Green

JOSS, 2023, 8, 5996
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Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

Kevin A Spiekermann, Thijs Stuyver, Lagnajit Pattanaik and William H Green

Mach. Learn.: Sci. Technol., 2023, 4, 048001
DOI: 10.1088/2632-2153/acee42

Comparative analysis of artificial neural networks models for predicting mortar properties with diatomite incorporation

Younes El Miski, Yassine Kharbouch, Mohamed Ameur, Oussama Zine and Driss Taoukil

Materials Chemistry and Physics, 2025, 333, 130386
DOI: 10.1016/j.matchemphys.2025.130386

High-Throughput Image-Based Assay for Identifying In Vitro Hepatocyte Microtubule Disruption

Yang Li, Andrew J. Bowling, Audrey Lehman, Kristina Johnson, Heather E. Pence, Lori A. Breitweiser, Eric Sherer, Jessica LaRocca and Wei Chen

J. Agric. Food Chem., 2024, 72, 21804
DOI: 10.1021/acs.jafc.4c04969

Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

Puck van Gerwen, Matthew D Wodrich, Ruben Laplaza and Clemence Corminboeuf

Mach. Learn.: Sci. Technol., 2023, 4, 048002
DOI: 10.1088/2632-2153/acee43

Automated machine learning approach for developing a quantitative structure–activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase

Yohei Takada and Kazuhiro Kaneko

Pharmacol. Rep, 2023, 75, 1017
DOI: 10.1007/s43440-023-00508-x

C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles

Stephanie Felten, Cyndi Qixin He and Marion H. Emmert

J. Org. Chem., 2025, 90, 2613
DOI: 10.1021/acs.joc.4c02574