Advancing the science of
material intelligence
Ainuric conducts applied research at the intersection of materials science, machine learning, and computational modelling — publishing openly to accelerate progress across the field.
Where we focus our research
Our research is grounded in real engineering problems — always aimed at outcomes that can be deployed in practice.
Machine Learning for Materials
Developing and benchmarking ML models for predicting mechanical, thermal, and corrosion properties from composition and processing data — with rigorous uncertainty quantification.
Material Data Infrastructure
Building open standards and tools for structuring, sharing, and querying material data at scale — enabling reproducible research and interoperability between platforms.
Alloy Design & Optimisation
Applying generative AI and optimisation algorithms to accelerate the discovery of new alloy compositions with targeted property profiles — reducing the search space by orders of magnitude.
Publications & white papers
Methods notes, white papers, and peer-reviewed publications from Ainuric research.
Graph-Based Material Similarity for Engineering Shortlisting
An explainable method for ranking related materials from structured evidence graphs, designed for early engineering shortlist discovery and review.
Download PDFRuntime-Safe Synthesis of Engineering Stress-Strain Curves
A staged method for generating plausible engineering stress-strain curves from runtime-safe material metadata, helper-property inference, and constrained empirical reconstruction.
Download PDFCollaborate with us on materials research
We actively collaborate with universities, research institutes, and national labs. If you are working on material data, AI for materials, or related computational problems, we would love to explore joint projects, shared datasets, or co-authored publications.
We particularly welcome partnerships focused on open science and reproducible research.
Get in touch