density functional theory
PulseAugur coverage of density functional theory — every cluster mentioning density functional theory across labs, papers, and developer communities, ranked by signal.
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New research explores weight-space geometry of AI reasoning distillation methods
A new research paper analyzes the geometric properties of weight updates across various offline reinforcement learning methods used for distilling reasoning capabilities into smaller AI models. The study trained six dif…
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AdsMind system uses AI agents to accelerate catalyst discovery
Researchers have developed AdsMind, a novel multi-agent system designed to accelerate the discovery of adsorption configurations on heterogeneous catalyst surfaces. This closed-loop framework integrates machine learning…
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New method extracts electrostatics from AI potentials
Researchers have developed a method called Latent Ewald Summation (LES) to extract electrostatic properties from foundation machine learning interatomic potentials (MLIPs). This technique allows for the creation of more…
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New framework enhances reliability in AI-driven materials discovery
Researchers have developed InvDesMobility, a novel framework designed to enhance the reliability and auditability of closed-loop materials discovery. This system integrates automated density functional theory (DFT) calc…
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New neural operator accelerates density functional theory calculations
Researchers have developed HamEvo, a novel neural operator designed to accelerate density functional theory (DFT) calculations by predicting Kohn-Sham Hamiltonians. This method achieves significant error reductions of 3…
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New Benchmark Reveals AI Models Struggle with Crystal Stability
Researchers have introduced PhononBench, a new benchmark designed to evaluate the dynamical stability of AI-generated crystalline materials. This benchmark utilizes the MatterSim interatomic potential for efficient phon…
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New transform learning method achieves state-of-the-art results
Researchers have developed a novel method for learning doubly sparse explicitly conditioned transforms, aiming to improve data compression, noise reduction, and feature extraction. This approach combines a fixed canonic…
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AI Models Accelerate Electronic Structure Calculations
Researchers have developed a novel Large Electron Model (LEM) capable of predicting the ground state wavefunctions of interacting electrons across a wide range of Hamiltonian parameters. This model, utilizing the Fermi …
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AI model DGLD discovers novel energetic materials with high performance
Researchers have developed Domain-Gated Latent Diffusion (DGLD), a novel AI approach for discovering energetic materials. DGLD addresses the challenge of limited labeled data by using a label-quality gate during trainin…
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New AI Framework Automates Complex Materials Science Calculations
Researchers have developed AutoDFT, a novel closed-loop multi-agent framework designed to automate Density Functional Theory (DFT) calculations in materials science. Unlike previous LLM-based agents that only plan upfro…
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New MLIP method uses orbital charges for better accuracy
Researchers have developed a new method for training machine learning interatomic potentials (MLIPs) that significantly improves sample efficiency and accuracy. By incorporating semiempirical orbital charges, the model …
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Meta FAIR releases large inorganic materials dataset and AI models
Meta FAIR has released the Open Materials 2024 (OMat24) dataset, comprising over 110 million density functional theory calculations for inorganic materials. This release also includes accompanying pre-trained Equiformer…
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Deep learning framework slashes pilot overhead in mmWave MIMO systems
Researchers have developed a novel deep learning framework called Multi-Block Attention (MBA) to improve channel estimation in millimeter-wave MIMO systems assisted by Intelligent Reflecting Surfaces (IRSs). This framew…
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SemiConLens visual analytics tool aids 2D semiconductor discovery
Researchers have developed SemiConLens, a visual analytics system designed to aid in the discovery of new two-dimensional (2D) semiconductor materials. This approach combines human expertise with machine learning to ove…
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New theory unifies spectral estimation with group theory for AI applications
Researchers have introduced a new framework called Algebraic Diversity, which leverages group-theoretic spectral estimation for analyzing data from single observations. This method generalizes temporal averaging and dem…
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AI expands Alexandria database with 1.3M new stable compounds
Researchers have developed a new multi-stage workflow for computational materials discovery, achieving a 99% success rate in identifying stable compounds. This process utilized the Matra-Genoa generative model, Orb-v2 p…
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AI agent XDFT diagnoses DFT-experiment band-gap mismatch with 78% accuracy
Researchers have developed XDFT, a self-evolving agent designed to automatically diagnose mismatches between theoretical density functional theory (DFT) predictions and experimental results for material band gaps. This …
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New benchmark RealMat-BaG assesses AI bandgap prediction for semiconductors
Researchers have developed a new benchmark called RealMat-BaG to evaluate the reliability of machine learning models for predicting semiconductor bandgaps. Current models trained on computational data often fail to gene…
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New EFT approach reconciles KS eigenvalues with photoemission data
Researchers have developed an effective field theory to explain discrepancies between calculated and measured electronic band structures in certain metals. Their work shows that Kohn-Sham eigenvalues can represent quasi…