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Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials | OpenReview
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials | OpenReview
Modern generative Machine Learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to...
Ternary materials discovery using human-in-the-loop generative machine learning - RSC Advances (RSC Publishing)
Ternary materials discovery using human-in-the-loop generative machine learning - RSC Advances (RSC Publishing)
Machine learning (ML) approaches to materials discovery are limited by data curation, availability, and bias. These issues can be addressed through the generation of new data points representing novel material compositions and/or structures. We demonstrate the implementation of this process to produce and subsequen
Closed-loop superconducting materials discovery - npj Computational Materials
Closed-loop superconducting materials discovery - npj Computational Materials
Self-supervised learning for crystal property prediction via denoising
Self-supervised learning for crystal property prediction via denoising
Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of...
A statistical perspective for predicting the strength of metals: Revisiting the Hall–Petch relationship using machine learning
A statistical perspective for predicting the strength of metals: Revisiting the Hall–Petch relationship using machine learning
The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavio…
Evaluating the diversity and utility of materials proposed by generative models
Evaluating the diversity and utility of materials proposed by generative models
Deep Learning Models to Identify Common Phases across Material Systems from X-ray Diffraction
Deep Learning Models to Identify Common Phases across Material Systems from X-ray Diffraction
Curvature-informed multi-task learning for graph networks
Curvature-informed multi-task learning for graph networks
Evaluating AI-guided Design for Scientific Discovery
Evaluating AI-guided Design for Scientific Discovery
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