Toward accurate relative energy predictions of the bioactive conformation of drugs. - Keith T. Butler; F. Javier Luque; Xavier Barril (J. Comput. Chem., 2009)

SMACT: Semiconducting Materials by Analogy and Chemical Theory. - Daniel W. Davies; Keith T. Butler; Adam J. Jackson; Jonathan Skelton; Kazuki Morita; Aron Walsh (J. Open Source Softw., 2019)

Distributed Representations of Atoms and Materials for Machine Learning. - Luis M. Antunes; Ricardo Grau-Crespo; Keith T. Butler (CoRR, 2021)

UnlockNN: Uncertainty quantification for neural network models of chemical systems. - Alexander Moriarty; Kazuki Morita; Keith T. Butler; Aron Walsh (J. Open Source Softw., 2022)

Predicting thermoelectric transport properties from composition with attention-based deep learning. - Luis M. Antunes; Keith T. Butler; Ricardo Grau-Crespo (Mach. Learn. Sci. Technol., 2023)

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? - Matthew Walker; Keith T. Butler (CoRR, 2025)