Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. - Yao Zhang; Andrew M. Saxe; Madhu S. Advani; Alpha A. Lee (CoRR, 2018)

Geometry of energy landscapes and the optimizability of deep neural networks. - Simon Becker; Yao Zhang; Alpha A. Lee (CoRR, 2018)

Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation. - Philippe Schwaller; Teodoro Laino; Théophile Gaudin; Peter Bolgar; Costas Bekas; Alpha A. Lee (CoRR, 2018)

Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. - Yao Zhang; Alpha A. Lee (CoRR, 2019)

Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. - Rhys E. A. Goodall; Alpha A. Lee (CoRR, 2019)

Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation. - Ryan-Rhys Griffiths; Miguel Garcia-Ortegon; Alexander A. Aldrick; Alpha A. Lee (CoRR, 2019)

Investigating 3D Atomic Environments for Enhanced QSAR. - William McCorkindale; Carl Poelking; Alpha A. Lee (CoRR, 2020)

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes. - Penelope Jones; Fabian Coupette; Andreas Härtel; Alpha A. Lee (CoRR, 2020)

Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction. - Matthew C. Robinson; Robert C. Glen; Alpha A. Lee (J. Comput. Aided Mol. Des., 2020)

Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes. - Todd J. Wills; Dmitrii A. Polshakov; Matthew C. Robinson; Alpha A. Lee (J. Chem. Inf. Model., 2020)

Perspective: new insights from loss function landscapes of neural networks. - Sathya R. Chitturi; Philipp C. Verpoort; Alpha A. Lee; David J. Wales (Mach. Learn. Sci. Technol., 2020)

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design. - Ryan-Rhys Griffiths; Philippe Schwaller; Alpha A. Lee (CoRR, 2021)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Bojana Rankovic; Yuanqi Du; Arian R. Jamasb; Julius Schwartz; Austin Tripp; Gregory Kell; Anthony Bourached; et al. (CoRR, 2022)

Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. - Ryan-Rhys Griffiths; Alexander A. Aldrick; Miguel Garcia-Ortegon; Vidhi Lalchand; Alpha A. Lee (Mach. Learn. Sci. Technol., 2022)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Matbench Discovery - An evaluation framework for machine learning crystal stability prediction. - Janosh Riebesell; Rhys E. A. Goodall; Anubhav Jain 001; Philipp Benner; Kristin A. Persson; Alpha A. Lee (CoRR, 2023)

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials. - Janosh Riebesell; T. Wesley Surta; Rhys E. A. Goodall; Michael Gaultois; Alpha A. Lee (CoRR, 2024)