Machine Learning– tag –
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Biomanufacturing Quality Control Transforms with Real-time PAT and AI Integration Under FDA ICH Q13 Guidance
Technology Networks UK Overview Process Analytical Technology (PAT) in biomanufacturing is undergoing a paradigm shift from endpoint testing to real-time, in-line measurement, driven by FDA's ICH Q13 guidance on continuous manufacturing,... -
Machine Learning Unveils Hidden Nanophotonic Resonances in Silicon-Gold Nanopillars, Accelerating Complex Material Analysis
npj Computational Materials Global Overview Machine learning (ML) has revealed previously undetectable hidden nanophotonic resonances in silicon-gold nanopillars. A new ML workflow decodes low-loss EELS data, transforming noisy nanoscale... -
Urgency for Biopolymer Data Standardization to Enhance Machine Learning in Materials Discovery
Biomacromolecules - ACS Publications USA Overview The integration of machine learning with biopolymer datasets critically requires robust data standardization to maximize its potential. Researchers, journals, and data providers share a r... -
Fermilab Builds Large-Scale Data Infrastructure for AI-Driven Scientific Discovery, Supporting DOE’s Genesis Mission
Fermilab USA Overview Fermilab provides the Fermi Data Platform, a secure and large-scale data infrastructure, to support the U.S. Department of Energy's (DOE) Genesis Mission, aimed at fostering AI-driven scientific discovery. This plat... -
Google DeepMind’s GNoME Discovers 2.2 Million New Crystal Structures, Propelling Clean Energy Material Design into a New Era
DEV Community 多国籍 Overview Google DeepMind's GNoME AI has identified an unprecedented 2.2 million new stable crystal structures, including 380,000 deemed practical, surpassing all previously known inorganic materials. This monumental ... -
America Makes Awards $2M to Advance AI-Based Material Qualification for Additive Manufacturing
America Makes / NCDMM USA Overview America Makes and NCDMM awarded $2 million to the 'Artificial Intelligence for Material Allowables in Additive Manufacturing (AIM-4AM)' project. This initiative will leverage machine learning to map pro... -
arXiv Paper Presents ‘AutoPot’: Automated, Massively Parallel Workflow for Constructing Machine-Learning Potentials
arXiv USA Overview A new preprint on arXiv introduces 'AutoPot,' an automated and massively parallelized workflow for constructing Machine Learning Interatomic Potentials (MLIPs). MLIPs bring quantum accuracy to atomic modeling, enabling... -
arXiv Paper Identifies Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
arXiv USA Overview A new arXiv paper reviews the rapid advancements of machine learning (ML) in atomic modeling and the growing centrality of interatomic potentials in materials science. It discusses six open questions as key challenges ... -
IJCRT.org Paper Argues AI and Mathematical Modeling Fusion Revolutionizes Materials Engineering, Accelerating Discovery
IJCRT.org India Overview A paper published on IJCRT.org argues that the combination of mathematical modeling and AI is revolutionizing materials engineering by providing predictive insights and accelerating materials discovery. Machine l... -
Researchers Develop ‘React-OT’ AI for Fast and Accurate Prediction of Chemical Reaction Transition States
The Research Code Unknown Overview Researchers have developed 'React-OT,' a machine learning approach capable of predicting chemical reaction transition states with unprecedented speed and accuracy. This technology will significantly acc...
