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Faraday Discussions|July 21, 2018
Materials discovery by chemical analogy: role of oxidation states in structure predictionDaniel W Davies, Keith T Butler, Olexandr Isayev, et al.Chemical Science|June 2, 2017
Organised chaos: entropy in hybrid inorganic-organic systems and other materialsKeith T Butler, Aron Walsh, Anthony K Cheetham, et al.The Journal of Physical Chemistry Letters|May 25, 2021
Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case StudyScott D Midgley, Said Hamad, Keith T Butler, et al.Journal of Physics. Condensed Matter : an Institute of Physics Journal|February 26, 2021
Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering dataKeith T Butler, Manh Duc Le, Jeyan Thiyagalingam, et al.Nature|July 27, 2018
Machine learning for molecular and materials scienceKeith T Butler, Daniel W Davies, Hugh Cartwright, et al.Chemical Science|December 15, 2023
Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistryAndy S Anker, Keith T Butler, Raghavendra Selvan, et al.Physical Chemistry Chemical Physics : PCCP|July 2, 2020
Differentiating the role of organic additives to assemble open framework aluminosilicates using INS spectroscopyAntony Nearchou, Jeff Armstrong, Keith T Butler, et al.ACS Applied Materials & Interfaces|March 20, 2018
Band Engineering of Carbon Nitride Monolayers by N-Type, P-Type, and Isoelectronic Doping for Photocatalytic ApplicationsMeysam Makaremi, Sean Grixti, Keith T Butler, et al.JACS Au|September 27, 2024
Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo SamplingKelvin Wong, Runzhang Qi, Ye Yang, et al.Chemical Society Reviews|March 19, 2016
Computational materials design of crystalline solidsKeith T Butler, Jarvist M Frost, Jonathan M Skelton, et al.Pageof 8