The quantum chemical search for novel materials and the issue of data processing: The InfoMol project. - Hans Peter Lüthi; Stefan N. Heinen; Gisbert Schneider; Andreas Glöss; Martin P. Brändle; Rollin A. King; Edward Pyzer-Knapp; Fahhad H. Alharbi; Sabre Kais (J. Comput. Sci., 2016)

Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. - José Miguel Hernández-Lobato; James Requeima; Edward O. Pyzer-Knapp; Alán Aspuru-Guzik (ICML, 2017)

Efficient and Scalable Batch Bayesian Optimization Using K-Means. - Matthew Groves; Edward O. Pyzer-Knapp (CoRR, 2018)

Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization. - Dipti Jasrasaria; Edward O. Pyzer-Knapp (CoRR, 2018)

Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks. - Clyde Fare; Lukas Turcani; Edward O. Pyzer-Knapp (CoRR, 2018)

Bayesian optimization for accelerated drug discovery. - Edward O. Pyzer-Knapp (IBM J. Res. Dev., 2018)

A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes. - Anna Paola Carrieri; Will P. M. Rowe; Martyn D. Winn; Edward O. Pyzer-Knapp (AAAI, 2019)

Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning. - Matt Benatan; Edward O. Pyzer-Knapp (CoRR, 2019)

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields. - James L. McDonagh; Ardita Shkurti; David J. Bray; Richard L. Anderson 003; Edward O. Pyzer-Knapp (J. Chem. Inf. Model., 2019)

Privacy-Preserving Gaussian Process Regression - A Modular Approach to the Application of Homomorphic Encryption. - Peter Fenner; Edward Pyzer-Knapp (AAAI, 2020)

Modelling Mobile Signal Strength by Combining Geospatial Big Data and Artificial Intelligence. - Paolo Fraccaro; Matt Benatan; Katharina Reusch; Clyde Fare; Blair Edwards; Edward Pyzer-Knapp (ICVISP, 2020)

Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19. - Edward O. Pyzer-Knapp (CoRR, 2020)

A Fast Parallel Particle Filter for Shared Memory Systems. - Alessandro Varsi; Jack Taylor; Lykourgos Kekempanos; Edward Pyzer-Knapp; Simon Maskell (IEEE Signal Process. Lett., 2020)

Heterogeneous Computing Systems for Complex Scientific Discovery Workflows. - Christoph Hagleitner; Dionysios Diamantopoulos; Burkhard Ringlein; Constantinos Evangelinos; Charles R. Johns; Rong N. Chang; Bruce D'Amora; James A. Kahle; James C. Sexton; Michael Johnston; Edward Pyzer-Knapp; Chris Ward (DATE, 2021)

SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure. - Aikaterini Chatzopoulou; Ángel F. García-Fernández; Edward Pyzer-Knapp; Simon Maskell (FUSION, 2021)

A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets. - Clyde Fare; Peter Fenner; Edward O. Pyzer-Knapp (CoRR, 2022)

Provably Reliable Large-Scale Sampling from Gaussian Processes. - Anthony Stephenson; Robert Allison; Edward Pyzer-Knapp (CoRR, 2022)

Roughness of Molecular Property Landscapes and Its Impact on Modellability. - Matteo Aldeghi; David E. Graff; Nathan C. Frey; Joseph A. Morrone; Edward O. Pyzer-Knapp; Kirk E. Jordan; Connor W. Coley (J. Chem. Inf. Model., 2022)

Self-Focusing Virtual Screening with Active Design Space Pruning. - David E. Graff; Matteo Aldeghi; Joseph A. Morrone; Kirk E. Jordan; Edward O. Pyzer-Knapp; Connor W. Coley (J. Chem. Inf. Model., 2022)

Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression. - Robert Allison; Anthony Stephenson; Samuel F; Edward O. Pyzer-Knapp (NeurIPS, 2023)

Physics Inspired Approaches Towards Understanding Gaussian Processes. - Maximilian P. Niroomand; Luke Dicks; Edward O. Pyzer-Knapp; David J. Wales (CoRR, 2023)

Coalitions of Large Language Models Increase the Robustness of AI Agents. - Prattyush Mangal; Carol Mak; Theo Kanakis; Timothy Donovan; Dave Braines; Edward Pyzer-Knapp (CoRR, 2024)

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents. - Nicholas J. Williams; Lara Kabalan; Ljiljana Stojanovic; Viktor Zolyomi; Edward O. Pyzer-Knapp (CoRR, 2024)

TopSearch: a Python package for topographical analysis of machine learning models and physical systems. - Luke Dicks; Edward O. Pyzer-Knapp (J. Open Source Softw., 2024)

Explainable Gaussian processes: a loss landscape perspective. - Maximilian P. Niroomand; Luke Dicks; Edward O. Pyzer-Knapp; David J. Wales (Mach. Learn. Sci. Technol., 2024)

Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models. - Matthew P. Wilson; Edward O. Pyzer-Knapp; Nicolas Galichet; Luke Dicks (CoRR, 2025)