Matthew J. Rosseinsky

According to our database, Matthew J. Rosseinsky authored at least 13 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Physics-informed diffusion models for extrapolating crystal structures beyond known motifs.

Andrij Vasylenko, Federico Ottomano, Christopher M. Collins, Rahul Savani, Matthew S. Dyer, Matthew J. Rosseinsky

CoRR, October, 2025

Probabilistic Isolation of Crystalline Inorganic Phases.

Daniel Ritchie, Michael W. Gaultois, Vladimir V. Gusev, Vitaliy Kurlin, Matthew J. Rosseinsky, Matthew S. Dyer

J. Chem. Inf. Model., 2025

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures.

Elena Zamaraeva, Christopher M. Collins, George R. Darling, Matthew S. Dyer, Bei Peng, Rahul Savani, Dmytro Antypov, Vladimir V. Gusev, Judith Clymo, Paul G. Spirakis, Matthew J. Rosseinsky

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Evolutionary Train-Test Split for Hierarchical Monte Carlo Ensemble.

Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Ahsan Ahmad Ursani, Andrij Vasylenko

Proceedings of the IEEE/ACM 12th International Conference on Big Data Computing, 2025

2024

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials.

Federico Ottomano, John Yannis Goulermas, Vladimir V. Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa P. Manzanera, Emmeline G. Poole, Matthew S. Dyer, John B. Claridge, Jon Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin Sanderson, Matthew J. Rosseinsky

CoRR, 2024

Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics.

Michael Moran, Vladimir V. Gusev, Michael W. Gaultois, Dmytro Antypov, Matthew J. Rosseinsky

CoRR, 2024

Hierarchical Supervised Monte Carlo Ensemble Learning.

Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Judith Clymo, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Andrij Vasylenko

Proceedings of the International Conference on Machine Learning and Applications, 2024

The Theory of Probabilistic Hierarchical Supervised Ensemble Learning.

Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Judith Clymo, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Andrij Vasylenko

Proceedings of the International Conference on Machine Learning and Applications, 2024

2023

Optimality guarantees for crystal structure prediction.

Vladimir V. Gusev, Duncan Adamson, Argyrios Deligkas, Dmytro Antypov, Christopher M. Collins, Piotr Krysta, Igor Potapov, George R. Darling, Matthew S. Dyer, Paul G. Spirakis, Matthew J. Rosseinsky

Nat., 2023

Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context.

Samantha Durdy, Michael W. Gaultois, Vladimir V. Gusev, Danushka Bollegala, Matthew J. Rosseinsky

CoRR, 2023

2022

Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties.

Samantha Durdy, Michael W. Gaultois, Vladimir V. Gusev, Danushka Bollegala, Matthew J. Rosseinsky

CoRR, 2022

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties.

Andrij Vasylenko, Dmytro Antypov, Vladimir V. Gusev, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky

CoRR, 2022

2020

Crystal Structure Prediction via Oblivious Local Search.

Dmytro Antypov, Argyrios Deligkas, Vladimir V. Gusev, Matthew J. Rosseinsky, Paul G. Spirakis, Michail Theofilatos

Proceedings of the 18th International Symposium on Experimental Algorithms, 2020