Density Functional Theory– tag –
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Cen-Online.org Details Breakthroughs and Industrial Applications in Catalyst Discovery Driven by Digital Tools and AI
Cen-Online.org Unknown Overview Cen-Online.org's article details recent advancements in catalysis and their industrial applications, highlighting how digital tools and AI are transforming catalyst discovery. Computational chemistry, mach... -
Frontiers Launches Research Topic on “High-Throughput AI-Driven Materials Discovery for High-Rate Batteries,” Accelerating Next-Gen Energy Storage
Frontiers Switzerland Overview Frontiers has initiated a new research topic, "High-Throughput AI-Driven Materials Discovery and Design for High-Rate Batteries," aiming to revolutionize battery material development through machine learnin... -
AI-Physics Collaboration Achieves Breakthrough in Hydrogen Storage Material Design, Pinpointing Optimal Lattice Architectures
Phys.org USA Overview An innovative collaboration between AI and physics has yielded a breakthrough in hydrogen storage material design. Research revealed a strong correlation between hydrogen storage capacity and the atomic-scale geomet... -
Machine Learning Potentials Accelerate Quantum Chemistry by Up to 1 Million-Fold, Revolutionizing Materials Science
ACS Central Science USA Overview Rapid advancements in machine learning interatomic potentials (MLIPs) are poised to accelerate quantum chemistry calculations by up to a million times, fundamentally transforming chemical and materials sc... -
arXiv Paper Evaluates Universal MLIPs, Finds DFT Fine-Tuning Essential for Accuracy in Reactive Processes
arXiv USA Overview A new arXiv study evaluated five universal machine-learning interatomic potentials (MLIPs) for quantitative materials modeling involving reactive events. It revealed that while universal MLIPs are becoming general-purp... -
Purdue University Seeks Postdoctoral Researchers in Computational Materials Design and Materials Informatics, Bolstering DFT and MLIPs Research
ApplyKite USA Overview Purdue University has announced a postdoctoral researcher opening in computational materials design and materials informatics. This strategic hire targets researchers with strong expertise in atomistic simulation m... -
AtomGPT.org Launches Open-Access Agentic AI Platform ‘AGAPI-Agents’ to Accelerate Materials Design
The Journal of Physical Chemistry Letters USA Overview AtomGPT.org has launched 'AGAPI-Agents,' an open-access agentic AI platform integrating open-source Large Language Models (LLMs) with scientific tools and databases for accelerated m... -
MDPI Paper Develops Machine-Learned MTPs for Low-Cost, High-Accuracy Prediction of Heat Transport in AlGaN and Related Materials
MDPI Switzerland Overview A study published in MDPI reports the development of Machine-Learned Moment Tensor Potentials (MTPs) for simulating static and dynamic structural properties and heat transport in AlGaN and related materials. Tra... -
ResearchGate Paper: Unconstrained MLIPs Achieve Superior Accuracy and Speed with Large Datasets, Enhancing Static Simulations
ResearchGate (Machine Learning: Science and Technology) Unknown Overview A paper published via ResearchGate demonstrates that unconstrained Machine Learning Interatomic Potentials (MLIPs), when trained on sufficiently large datasets, can... -
University of Washington Leverages AI and Quantum Computing for Scaled Quantum Material Simulations, Uncovering New Phenomena
EurekAlert! USA Overview University of Washington scientists have successfully integrated AI and quantum computing to enable large-scale quantum material simulations previously deemed impossible. This innovative approach has led to the d...
