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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Machine learning models achieve 92% accuracy in classifying magnetic order in materials
Researchers have developed machine-learning classifiers capable of identifying magnetic order in materials with over 92% accuracy. These models, trained on experimental data and utilizing descriptors from the Materials …
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AI and LLMs applied to materials science and vehicle components · 2 sources tracked
Two new arXiv papers explore the application of AI and large language models (LLMs) in materials science. The first paper introduces robust AI frameworks for accelerating crystalline materials discovery, focusing on pro…
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Machine learning models accelerate semiconductor defect analysis
Researchers have developed machine learning models to predict defect formation energies and zero-phonon lines in semiconductors, specifically for 4H-SiC. These models aim to accelerate high-throughput workflows by actin…
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AI platform QuantaMind simulates molecular dynamics with near-DFT accuracy
QuantaMind, an AI platform developed by Molecular Mind, has achieved a significant breakthrough in molecular dynamics simulation, as reported in Science Advances. This platform can now simulate reactive molecular dynami…
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AI models advance 3D molecular generation for drug discovery
Two new research papers introduce advanced AI models for 3D molecular generation. The first, Equivariant-Free Transformer-Autoencoded Latent Flow Matching (EF-TALFM), uses a fixed-dimensional latent representation to ge…
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AI agents discover new crystal structure laws, improving screening efficiency
Researchers have developed a new set of eight Plausibility Rules for Inorganic Structures (PRIS) discovered by autonomous agents that can rapidly screen potential crystal structures. These rules encode five key mechanis…
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New ML approach accelerates semiconductor device simulation by 10,000x
Researchers have developed a novel approach for simulating semiconductor devices by combining machine-learned electronic structure models with a quantum transport solver. This new framework offers a significant speedup,…
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Neural operators learn Kohn-Sham map for faster DFT calculations
Researchers have developed a novel approach to density functional theory (DFT) by using neural operators to learn the Kohn-Sham map, which bypasses the computationally intensive orbital diagonalization step. This method…
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New AI models tackle writing quality with novel training and personalization
A new AI model called Deft, co-founded by Justin Murphy and AI researcher Rosmine, aims to address the perceived stagnation in AI writing quality. Deft's approach, termed 'distribution fine-tuning' (DFT), focuses on mat…
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New AI model predicts molecular properties with coupled-cluster accuracy
Researchers have developed MEHnet-MG, an equivariant network designed to predict molecular electronic-structure properties with coupled-cluster accuracy at a significantly lower computational cost. This model is trained…
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New AI model BDIP-Net predicts bilayer material properties
Researchers have developed BDIP-Net, a novel graph neural network designed to predict the properties of stacked bilayer materials. This framework utilizes a MatterSim-D3 workflow for efficient structure generation, mimi…
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New DREAMS framework enhances trust in AI-driven materials simulation
Researchers have developed DREAMS, a new framework for agentic materials simulation using density functional theory (DFT). This system incorporates a multi-tier safety guard to ensure the numerical outputs of large lang…
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New framework improves MLIPs for electrostatic interactions
Researchers have developed a new framework for machine learning interatomic potentials (MLIPs) that better accounts for electrostatic effects. This framework views existing models as coarse-grained approximations of den…
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New ontology standardizes machine learning interatomic potentials
Researchers have developed a new ontology, the MLIPs ontology, to standardize the description of machine learning interatomic potentials (MLIPs). This OWL 2 DL ontology aims to address the scattered metadata issue in th…
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New AI framework accelerates catalyst design for targeted properties
Researchers have developed a new framework called Catalyst Diffusion Transformer (CatDiT) for designing heterogeneous catalysts. This model can generate novel and valid catalyst structures, including intermetallic alloy…
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Flash EQ-Linear accelerates equivariant neural networks with Fourier transforms
Researchers have developed Flash EQ-Linear, a novel algorithm designed to significantly accelerate equivariant linear layers in neural networks. By leveraging the Fourier convolution theorem and the conjugate symmetry o…
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New AI system deciphers organic structures from spectroscopic data
Researchers have developed a new hypothesis-refinement paradigm to determine organic molecular structures from spectroscopic data. This approach integrates spectral evidence with large-scale molecular priors, addressing…
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OLEDLM: New Language Model for OLED Molecular Design
Researchers have developed OLEDLM, a novel language model specifically designed for the design of organic light-emitting diode (OLED) molecules. This model utilizes a LLaMA-style transformer architecture as a foundation…
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Graph Neural Networks accelerate catalyst design for graphene quantum dots
Researchers have developed a novel framework utilizing graph neural networks (GNNs) to significantly accelerate the exploration of transition metal adsorption on graphene quantum dots (GQDs). This GNN-based model, named…
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Machine learning poised to revolutionize quantum chemistry research
A new position paper argues that machine learning is the most promising direction for advancing quantum chemistry. The paper posits that traditional methods like density functional theory and wavefunction methods are re…