New Technology– category –
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Hessian-Based Molecular Conformation Augmentation Improves Scalability and Efficiency of Machine Learning Interatomic Potentials
arXiv (Preprint Server) International Overview This preprint proposes two Hessian-derived data augmentation schemes, isotropic Gaussian displacement (UniAug) and normal mode-weighted displacement (ModeAug), for machine-learning interatom... -
Faster Quantum Simulation Via Randomization Boosts Scalability & Precision for Markovian Open Quantum Systems
Quantum (Scientific Journal) International Overview Researchers have introduced novel non-probabilistic algorithms for simulating Markovian open quantum systems using randomization, aiming for faster and more accurate quantum simulations... -
Autonomous Labs & Closed-Loop AI Transform Discovery Economics, Significantly Reducing R&D Cost and Time
Exponential Industry International Overview The integration of autonomous laboratories and closed-loop AI is fundamentally transforming the economics of scientific discovery, particularly in chemistry and materials science. Initiatives l... -
Geometry-Guided Model CDSM Achieves DFT-Comparable Accuracy in Collagen Structure Prediction, Cutting Computational Cost by 400-790x
bioRxiv USA Overview CDSM (Empirical Geometry-Guided Model) enables robust, high-throughput collagen structure prediction by explicitly encoding empirical geometric constraints. The model significantly compresses the structure-prediction... -
AI-Proposed Superconductor Generation Is Not Discovery: Physics Judge Highlights GNoME and MatterGen Limitations
arXiv USA Overview This paper introduces a 'physics judge' to assess whether AI-proposed superconductors possess necessary physical properties beyond mere thermodynamic stability. It critically evaluates generative models like GNoME and ... -
Physics-Aware AI Indispensable for Materials Research: Performance Gaps Highlighted in Thermal Conductivity Prediction
Columbia Engineering USA Overview Columbia Engineering emphasizes the critical need for physics-aware AI in materials science, particularly for machine learning interatomic potentials (MLPs) that predict macroscopic properties from quant... -
National University of Singapore Builds ‘AI Foundry’ to Transform Materials Discovery via Continuous Prediction, Experimentation, and Learning Cycle
EurekAlert! Singapore Overview Professor Ong is leading the Materialyze.AI Lab at NUS to establish an 'AI Foundry' for transforming materials discovery, aiming to connect various AI capabilities to create a continuous cycle of prediction... -
Generative AI Brings New Vision to High Fashion, Proposing ‘Impossible’ Materials Like Metallic Foams and Transparent Knits
Observer USA Overview Generative AI is bringing new creativity to the high fashion industry, envisioning 'impossible' materials like metallic foams and transparent knits. AI accelerates the divergent frontend of the design process, allow... -
Large Language Models Autonomously Generate Chemical Synthesis Protocols, Bridging ‘Lab-to-Fab’ Gap in Materials Manufacturing
MDPI Switzerland Overview This review focuses on AI-driven strategies to bridge the 'lab-to-fab' gap in materials synthesis and manufacturing, moving beyond property prediction to address manufacturability. It discusses the use of Large ... -
AI Generative Design and Robotic Fabrication Integrate to Revolutionize Architectural Workflows, Achieving Sustainable and High-Precision Construction
designedbyai.io Unknown Overview Integration of AI generative design with robotic fabrication is transforming modern architectural workflows, enabling lightweight and high-performance components with reduced material usage (concrete, ste...
