New Technology– category –
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Fabarta AI4S&E: RSI Engine for Materials R&D and Production
Thailand Business News China Overview Fabarta has upgraded its AI for Science (AI4S) platform to AI4S&E, integrating a Recursive Self-Improvement (RSI) engine to connect materials discovery with industrial mass production. The platfo... -
Terra Quantum and Schaeffler: Hybrid QML for Materials Modeling
Quantum Computing Report USA Overview Terra Quantum and Schaeffler have published a peer-reviewed study demonstrating that a hybrid quantum-classical machine learning model outperforms classical counterparts in predicting atomic forces. ... -
Multimodal AI for Materials: MIT’s 3D Crystal Structure Model
MIT News USA Overview MIT researchers have developed a multimodal AI model that integrates 3D atomic structures with natural language descriptions to improve material design. By overcoming the limitations of text-only large language mode... -
NVIDIA CUDA-X: AI-Defined Fab Tools and Industry Adopters
NVIDIA News USA Overview NVIDIA has expanded its CUDA-X library portfolio, including the new cuPotential for atomic-scale simulation, to enable "AI-defined fabs." These tools accelerate chip design and manufacturing workflows for leaders... -
Bulk Metal Automation: Tohoku University’s AI-Driven Workflow
Tohoku University Japan Overview Researchers at Tohoku University and NIMS have developed a fully automated experimental system for bulk metallic materials that integrates heat treatment, structural characterization, and mechanical testi... -
CO2-to-Fuel Conversion: AI-Driven Reaction Network for Methanol
ScienceDaily India Overview Researchers at the Indian Institute of Science (IISc) used machine learning to identify 9,389 chemical reactions on a copper catalyst, expanding a previous model of 152 reactions. This expanded network predict... -
AI Materials Discovery: GNoME and A-Lab 2026 Technical Guide
TrackFuture.ai USA Overview AI is accelerating materials discovery by combining Graph Neural Networks for prediction with autonomous robotic synthesis. Systems like GNoME have predicted 2.2 million new crystal structures, while A-Lab has... -
AnySolv: Transferable Implicit Solvation Potential for MLIPs
arXiv (Preprint) USA Overview AnySolv is a transferable implicit solvation potential that provides energy and force corrections to existing gas-phase machine-learning interatomic potentials (MLIPs) without requiring retraining. It reprod... -
DiffGA: Steering Frozen Diffusion Models for Materials Design
arXiv (Preprint) International Overview DiffGA is a retraining-free framework that integrates evolutionary search into the sampling trajectory of pretrained diffusion models to optimize materials for black-box objectives. By coupling thi... -
MLIP Benchmarking: MACE vs ACE and HDNNP Performance Analysis
arXiv (Preprint) USA Overview MACE models demonstrate the highest accuracy in reproducing potential energy surfaces but incur significant computational costs and carbon footprints. This study evaluates machine-learning interatomic potent...
