Density Functional Theory– tag –
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arXiv Releases High-Information Dataset ‘MAD-1.6’ for Universal Atomistic Machine Learning, Boosting Predictive Accuracy
arXiv USA Overview The high-quality, high-information dataset 'MAD-1.6' has been released on arXiv to accelerate universal atomistic machine learning. Comprising 362,646 atomic structures across 102 chemical elements, it covers diverse m... -
Scilight Press Introduces ‘Harness’ Framework Integrating LLM Agents and Materials Project to Enhance Perovskite Bandgap Prediction
Scilight Press Unknown Overview Scilight Press has proposed "Harness," a novel framework to integrate Large Language Model (LLM) agents with existing material databases in materials science. This framework translates LLM agent proposals ... -
Machine Learning Potentials Grapple with Long-Range Interactions in Atmospheric Modeling: A DTU Deep Dive
DTU Research Database Denmark Overview Researchers at the Technical University of Denmark (DTU) evaluated AIMNet2 and PaiNN, two machine-learned interatomic potentials (MLIPs), for modeling molecular collisions critical to atmospheric cl... -
Geometry-Guided Model CDSM Achieves DFT-Comparable Accuracy in Collagen Structure Prediction, Cutting Computational Cost by 400-790x
bioRxiv USA Overview CDSM (Empirical Geometry-Guided Model) enables robust, high-throughput collagen structure prediction by explicitly encoding empirical geometric constraints. The model significantly compresses the structure-prediction... -
Multi-Agent LLM ‘MAESTRO’ Designs Single-Atom Catalysts via Reasoning, Breaking Conventional Limits
ACS Publications USA Overview MAESTRO (Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization) is a reasoning-driven framework where multiple LLMs collaboratively design high-performance single-atom catalysts. LLM ag... -
Equivariant Graph Neural Network Interatomic Potentials Accelerate Nanomaterials Simulation by 100x
nano-matter.com International Overview Equivariant Graph Neural Network (GNN) interatomic potentials are accelerating nanomaterials research by predicting atomic forces and energies with DFT-comparable accuracy, while reducing computatio... -
GRACE-OFF Achieves High-Precision Machine-Learned Interatomic Potentials for Organic Liquids via GRACE Architecture
Journal of Chemical Theory and Computation | ACS Publications USA Overview This study introduces GRACE-OFF, a machine-learned interatomic potential (MLIP) built on the Graph Atomic Cluster Expansion (GRACE) neural network architecture, d... -
LAMMPS Integrates DeePMD-kit, NequIP for Quantum-Accurate ML Potentials, Enhancing Molecular Dynamics Simulations
LAMMPS Molecular Dynamics Simulator USA Overview LAMMPS, a widely used open-source molecular dynamics simulator, now supports cross-code frameworks and interoperability tools for machine learning interatomic potentials (MLPs), including ... -
ORNL Develops AI-Guided Autonomous System for Inverse Design of Functional Materials from Single Molecules
Facebook (Oak Ridge National Laboratory) USA Overview Researchers at Oak Ridge National Laboratory (ORNL) have developed an AI-guided system that autonomously arranges individual molecules to construct functional materials. This system a... -
LLM Agents Unleash Autonomous Materials Discovery and Design
Unknown International Overview Large language model (LLM)-powered agents are now autonomously executing complex scientific workflows in computational materials science, significantly enhancing research efficiency and reliability. These a...
