New Technology– category –
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U.S. Department of Energy (DOE) Partners with Microsoft to Leverage AI for Battery Electrolyte and Clean Energy Material Discovery
OSTI (Office of Scientific and Technical Information) USA Overview The U.S. Department of Energy (DOE) is actively advancing its AI innovation ecosystem, collaborating with Microsoft to identify new battery electrolyte materials through ... -
SpinQ Launches Gate-Model Quantum Computing Platform to Enhance Quantum Simulation, Accelerating Materials Science and Drug Discovery
SpinQ China Overview SpinQ has unveiled a gate-model quantum computing platform designed to enhance quantum simulation for chemistry and materials science. The platform emphasizes its ability to more accurately model molecules and conden... -
ACS Publications Reveals MACE-QEq Potential, Addressing MLIP Challenges in Long-Range Electrostatics and Charge Transfer, Boosting Accuracy for ZnO and Water Systems
Journal of Chemical Theory and Computation (ACS Publications) USA Overview This research augments the equivariant Multi-Atomic Cluster Expansion (MACE) potential with a charge equilibration (QEq) framework to address challenges in machin... -
Major Manufacturers Invest Billions in Quantum Computing to Accelerate Atomic-Level Materials R&D
Forbes USA Overview Leading manufacturers are aggressively adopting quantum computing for materials R&D, investing billions in atomic-level material simulations. Quantum computers offer a unique advantage in modeling complex atomic i... -
Reliability-Gated First-Principles Feedback Framework ‘InvDesMobility’ Accelerates Closed-Loop Materials Discovery with Carrier Mobility Prediction
ResearchGate USA Overview This paper introduces 'InvDesMobility,' a reliability-gated first-principles feedback framework for closed-loop inverse materials design. Focusing on discovering structures based on target functionality, InvDesM... -
arXiv Introduces MMGNN: Multi-level, Multi-color Graph Neural Networks Decompose Molecular Graphs for Enhanced Property Prediction
arXiv (via ResearchGate) USA Overview A new Multi-level, Multi-color Graph Neural Network (MMGNN) has been introduced on arXiv, a hierarchical framework that decomposes molecular graphs into overlapping atom-type-pair-specific subgraphs.... -
Information Theory and Machine Learning Fusion Achieves High-Precision Alloy MLP Models for Stacking-Fault Energy and Phase Diagram Prediction
Science Advances (PubMed) USA Overview This research introduces a novel approach that combines information theory with machine learning to optimize the design of machine learning potentials (MLPs) for metallic alloys. The method effectiv... -
NVIDIA Unveils ALCHEMI NIM Microservices to Significantly Accelerate Chemistry and Materials Science Discoveries with New AI Software
NVIDIA News USA Overview NVIDIA has introduced new AI software, including ALCHEMI NIM microservices, poised to significantly accelerate scientific discovery in chemistry and materials. ALCHEMI specifically targets batched geometry relaxa... -
Argonne National Lab Launches $2.77 Million ‘Accelerated Catalyst Design Foundry’ to Hasten Catalyst Discovery with AI and Automation
Lab Manager USA Overview Argonne National Laboratory is spearheading the $2.77 million 'Accelerated Catalyst Design Foundry' project, integrating AI, automation, and advanced experimental workflows to significantly accelerate catalyst di... -
Hugging Face Spotlights CrystalCLR and CHGNet for Enhanced Materials Property Prediction via Machine Learning
Hugging Face USA Overview Hugging Face highlights significant advancements in materials property prediction with the CrystalCLR framework and CHGNet machine learning interatomic potential. CrystalCLR improves material representations thr...
