Edward O. Pyzer‐Knapp

University of Manchester — United Kingdom

Materials Science · Physical Sciences

24h-index2.5kcitations98works7.02yr avg
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Research Topics

Machine Learning in Materials Science(39), Computational Drug Discovery Methods(27), X-ray Diffraction in Crystallography(10), Gaussian Processes and Bayesian Inference(10), Machine Learning and Data Classification(8)

Publications98 total

Global properties of the energy landscape: a testing and training arena for machine learned potentials
npj Computational Materials·2025· 2 citedOpen Access
Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor
arXiv (Cornell University)·2025Open Access
Foundation models for materials discovery – current state and future directions
npj Computational Materials·2025· 41 citedOpen Access
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
arXiv (Cornell University)·2025· 1 citedOpen Access
Encapsulation of hydrophobic pollutants within a large water-soluble [Fe4L6]4− cage
Cell Reports Physical Science·2025· 1 citedOpen Access
Providing Machine Learning Potentials with High Quality Uncertainty Estimates
arXiv (Cornell University)·2025Open Access
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
Scientific Data·2025· 6 citedOpen Access
DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon
npj Biofilms and Microbiomes·2024· 3 citedOpen Access
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
arXiv (Cornell University)·2024Open Access
LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction
2024· 1 citedOpen Access
Coalitions of Large Language Models Increase the Robustness of AI Agents
arXiv (Cornell University)·2024· 1 citedOpen Access
Explainable Gaussian processes: a loss landscape perspective
Machine Learning Science and Technology·2024· 1 citedOpen Access
TopSearch: a Python package for topographical analysisof machine learning models and physical systems
The Journal of Open Source Software·2024Open Access
Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery
Nature Chemistry·2024· 47 citedOpen Access
Advancing biomolecular simulation through exascale HPC, AI and quantum computing
Current Opinion in Structural Biology·2024· 12 citedOpen Access
Further Understanding of a Local Gaussian Process Approximation: Characterising Convergence in the Finite Regime
arXiv (Cornell University)·2024Open Access
A physics-inspired approach to the understanding of molecular representations and models
Molecular Systems Design & Engineering·2024· 4 citedOpen Access
Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)
Digital Discovery·2024· 6 citedOpen Access
A Physics-Inspired Approach to the Understanding of Molecular Representations and Models
2023Open Access
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
arXiv (Cornell University)·2023· 1 citedOpen Access
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Frequent Co-authors

Anna Paola Carrieri(9), Alán Aspuru‐Guzik(7), Graeme M. Day(6), Andrew I. Cooper(6), Laura‐Jayne Gardiner(6), Kirk E. Jordan(6), Martyn Winn(5), David Graff(5), Connor W. Coley(5), Clyde Fare(4), Will Rowe(4), Ritesh Krishna(4), Alessandro Curioni(3), Kim E. Jelfs(3), Hugh P. G. Thompson(3), Linjiang Chen(3), Matteo Aldeghi(3), Joseph A. Morrone(3), Dipti Jasrasaria(3), Peter Staar(2)