2026 · Preprint
Chemical filters for ultra-high-throughput materials screening and generation
arXiv preprint arXiv:2607.17910
Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park, Anthony Onwuli, Masahiro Negishi, Aron Walsh
Materials × machine learning
Anthony Onwuli
Machine Learning Scientist II · Materials Nexus
I work on atomistic machine learning, materials discovery and scientific computing. My interests sit between model development and the scientific systems around it: data generation, simulation, evaluation and reproducible research software.
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Research
Recent work
Publications spanning chemical representations, generative materials design, Bayesian optimisation and high-throughput computational screening.
2025 · Preprint
Bayesian Optimization Hackathon for Chemistry and Materials
ChemRxiv
Sterling G. Baird et al., including Anthony Onwuli
2025 · Article
Exploration of crystal chemical space using text-guided generative artificial intelligence
Nature Communications 16, 4379
Hyunsoo Park, Anthony Onwuli, Aron Walsh
2025 · Article
Mapping inorganic crystal chemical space
Faraday Discussions 256, 601–613
Hyunsoo Park, Anthony Onwuli, Keith T. Butler, Aron Walsh
2024 · Article
Ionic species representations for materials informatics
APL Machine Learning 2(3)
Anthony Onwuli, Keith T. Butler, Aron Walsh
2023 · Article
Element similarity in high-dimensional materials representations
Digital Discovery 2, 1558–1564
Anthony Onwuli, Ashish V. Hegde, Kevin V. T. Nguyen, Keith T. Butler, Aron Walsh
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Open source
Scientific software
Public tools I develop or contribute to for materials informatics, chemical screening and computational materials design.
Maintainer
SMACT
Chemistry-aware screening and materials-informatics tools for computational materials design.
Materials discoveryChemical screeningPython
Author · maintainer
ElementEmbeddings
Tools for analysing, comparing and visualising high-dimensional representations of chemical elements and ionic species.
Materials informaticsRepresentation learningPython
Contributor
SkipSpecies
Distributed representations of ionic species for learning chemical similarity from materials data.
Representation learningIonic speciesPython