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Found 7 papers, 4 papers with code
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images
Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages.
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions.
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Recent studies suggested that these models could be useful in chemistry and materials science.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems.
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMS
Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics.
