Density Functional Theory– tag –
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X2DB: Unifying Experimental and Computational 2D Materials Data to Accelerate AI-Driven Discovery
DTU Research Database Denmark Overview A new open infrastructure, X2DB, has been established to integrate experimental and computational data for 2D materials on a massive scale, centralizing previously dispersed knowledge. This robust d... -
Machine Learning Revolutionizes Magnetic Functional Alloy Design: Generative GNNs and High-Throughput DFT Discover 2D Magnets
AIP Advances USA Overview This article details recent advancements in the design of magnetic functional alloys (MFAs) leveraging machine learning (ML), highlighting how ML has contributed to the design of magnetocaloric, magnetostrictive... -
Berkeley Lab Revolutionizes Solid Electrolyte Design with Hybrid ML and Experimental Data, Discovering Ion Transport Dominated by Dissociation Mechanisms, Not Viscosity
Facebook (Berkeley Lab Energy Storage Center) USA Overview A Berkeley Lab team adopted a hybrid machine learning approach combining experimental data and computational descriptors, revealing that ion transport in ionic liquids is primari... -
SandboxAQ’s AQCat AI Model on Claude Science Accelerates Catalyst Screening 20,000x, Considers Magnetic Behavior, Trained on High-Fidelity Data
Quantum Zeitgeist USA Overview SandboxAQ has released AQCat, an AI model running on Claude Science, capable of accelerating potential catalyst screening by up to 20,000 times faster than traditional lab methods. AQCat predicts catalyst e... -
LLNL Optimizes Photoelectrochemical Devices via Hybrid DFT Simulations and AI, Diagnosing Point Defect Effects, Doping, and Alloying for Performance Enhancement
Lawrence Livermore National Laboratory (LLNL) USA Overview Lawrence Livermore National Laboratory (LLNL) has significantly advanced photoelectrochemical (PEC) device optimization by integrating advanced hybrid density functional theory (... -
MIT Unveils CrysVCD Framework, Boosting AI-Designed Material Stability by 70% and Dramatically Reducing Computational Costs
MIT News USA Overview MIT researchers have developed 'CrysVCD' (crystal generator with valence-constrained design), a framework that significantly improves the stability rate of AI-generated materials. This approach ensures designs satis... -
Lawrence Berkeley National Laboratory Unveils Quantum Accuracy Scale Transition from DFT to MLIP and Plans for Two Autonomous Labs at 2026 Annual Meeting
Lawrence Berkeley National Laboratory USA Overview Lawrence Berkeley National Laboratory's Molecular Foundry highlighted the quantum accuracy scale transition from DFT to MLIP in a keynote at its 2026 Annual Meeting. They also announced ... -
DeepH-pack Unites Ab Initio Calculations and Deep Learning to Accelerate AI-Driven Electronic Structure Modeling
Facebook (reposting about DeepH-pack) USA Overview DeepH-pack has been introduced as a general-purpose neural network package that combines ab initio calculations with deep learning to accelerate electronic structure modeling. This tool ... -
Meta AI Releases OMat24, a Massive Inorganic Materials Dataset with Over 110 Million DFT Calculations, Alongside High-Performance EquiformerV2 GNN Model
Meta Fundamental AI Research (FAIR) USA Overview Meta Fundamental AI Research (FAIR) has unveiled Open Materials 2024 (OMat24), a monumental inorganic materials dataset comprising over 110 million DFT calculations, positioning it as one ... -
AI-Assisted Multiobjective High-Throughput Screening of GPE Accelerates Safer Lithium Battery Development
ACS Publications USA Overview An ACS Publications paper reports a method accelerating the development of safer lithium batteries through AI-assisted multiobjective high-throughput screening and prioritization of gel polymer electrolytes ...
