Matthew J. Rosseinsky

14 papers B 1C 3Journal 10

Year	Rank	Type	Title / Venue / Authors
2025	C	`conf`	Evolutionary Train-Test Split for Hierarchical Monte Carlo Ensemble. BDCAT Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Ahsan Ahmad Ursani, Andrij Vasylenko
2025	J	`jnl`	MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures. CoRR 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
2025	J	`jnl`	Physics-informed diffusion models for extrapolating crystal structures beyond known motifs. CoRR Andrij Vasylenko, Federico Ottomano, Christopher M. Collins, Rahul Savani, Matthew S. Dyer, Matthew J. Rosseinsky
2025	J	`jnl`	Probabilistic Isolation of Crystalline Inorganic Phases. J. Chem. Inf. Model. Daniel Ritchie, Michael W. Gaultois, Vladimir V. Gusev, Vitaliy Kurlin, Matthew J. Rosseinsky, Matthew S. Dyer
2024	J	`jnl`	Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials. CoRR 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
2024	J	`jnl`	Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics. CoRR Michael Moran, Vladimir V. Gusev, Michael W. Gaultois, Dmytro Antypov, Matthew J. Rosseinsky
2024	C	`conf`	Hierarchical Supervised Monte Carlo Ensemble Learning. ICMLA Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Judith Clymo, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Andrij Vasylenko
2024	C	`conf`	The Theory of Probabilistic Hierarchical Supervised Ensemble Learning. ICMLA Ziauddin Ursani, Dmytro Antypov, Katie Atkinson, Judith Clymo, Matthew S. Dyer, Matthew J. Rosseinsky, Sven Schewe, Andrij Vasylenko
2023	J	`jnl`	Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context. CoRR Samantha Durdy, Michael W. Gaultois, Vladimir V. Gusev, Danushka Bollegala, Matthew J. Rosseinsky
2023	J	`jnl`	Optimality guarantees for crystal structure prediction. Nat. 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
2022	J	`jnl`	Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties. CoRR Andrij Vasylenko, Dmytro Antypov, Vladimir V. Gusev, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky
2022	J	`jnl`	Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties. CoRR Samantha Durdy, Michael W. Gaultois, Vladimir V. Gusev, Danushka Bollegala, Matthew J. Rosseinsky
2020	B	`conf`	Crystal Structure Prediction via Oblivious Local Search. SEA Dmytro Antypov, Argyrios Deligkas, Vladimir V. Gusev, Matthew J. Rosseinsky, Paul G. Spirakis, Michail Theofilatos
2020	J	`jnl`	Crystal Structure Prediction via Oblivious Local Search. CoRR Dmytro Antypov, Argyrios Deligkas, Vladimir V. Gusev, Matthew J. Rosseinsky, Paul G. Spirakis, Michail Theofilatos