Materials Informatics– category –

AZoM UK Overview The integration of AI and automation is revolutionizing materials discovery, notably with Google DeepMind's GNoME and Microsoft's MatterGen. GNoME has identified over 10 million novel crystal structures, while MatterGen ...

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DocShare Unknown Overview A study published on DocShare identified a consistent 'systematic softening' in the potential energy surfaces (PES) of universal machine-learned interatomic potentials (uMLIPs) such as M3GNet, CHGNet, and MACE-M...

Ouro Unknown Overview Recent analysis reveals that AI foundation models for materials science, such as CHGNet and MACE-MP-0, exhibit limitations in accurately predicting d-electron site preferences in spinel structures. Specifically, for...

arXiv International Overview A new paper reformulates acquisition function selection in constrained multi-objective Bayesian optimization (CMOBO) for materials design as an adaptive policy selection problem. Two novel controllers, UCB-Ba...

arXiv Unknown Overview This study introduces a physics-guided machine learning framework for the inverse design of Co-based superalloys, successfully identifying new alloy compositions that enhance the γ' solvus temperature by up to 15% ...

arXiv Unknown Overview This arXiv study introduces UFO-MGen, a flow-based universal generative model that learns topological features of Wyckoff representations, achieving over 90% success rate in crystal generation. UFO-MGen demonstrate...

arXiv (Preprint) Unknown Overview A preprint on arXiv proposes a novel training scheme for machine-learned interatomic potentials (MLIPs) to accelerate point defect simulations. This efficient method trains MLIPs using limited DFT data, ...

Bits to Atoms (MIT CDFAM Washington DC 2026 presentation) USA Overview MIT's Alexander Htet Kyaw has unveiled a pioneering research pipeline that combines 3D generative AI, vision-language models, and robotic assembly. This system can ge...

Cool Papers (Preprint/Research Highlight) Unknown Overview Calculating high-order derivatives, such as Hessians, for machine learning interatomic potentials (MLIPs) has been computationally intensive for large systems. This research prop...