Materials Informatics– category –
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U.S. DOE Announces AI Solutions to Innovate Critical Minerals Supply Chain and Accelerate Alternative Materials Development
Department of Energy USA Overview The U.S. Department of Energy (DOE) has unveiled a strategy to innovate the critical minerals supply chain and accelerate alternative materials development through AI solutions. This strategy promotes th... -
EurekAlert! Review: AI Reshapes Chemical Engineering, Accelerating R&D from Reaction Design to Smart Manufacturing
EurekAlert! USA Overview A review published on EurekAlert! highlights the fundamental transformation artificial intelligence (AI) is bringing to chemical engineering. AI is significantly accelerating R&D cycles through diverse applic... -
GPUMD 4.0 from Chalmers University Unleashes Machine Learning Power for Advanced Materials Simulations
Computational Materials Group @ Chalmers Sweden Overview Chalmers University of Technology's Computational Materials Group has released GPUMD 4.0, a high-performance molecular dynamics (MD) software that integrates cutting-edge machine l... -
arXiv Paper Benchmarks 6 Universal ML Interatomic Potentials, Evaluating Structural Fidelity and Performance for Lunar Regolith MD Simulations
arXiv International Overview Six universal machine-learning interatomic potentials (MLIPs)—MACE-MH, MatterSim, SevenNet-0, UPET, UMA, and NequIP-OAM-L—were comprehensively benchmarked for molecular dynamics (MD) simulations of lunar rego... -
arXiv Paper: Vilya-1 Achieves High Geometric Accuracy as All-Atom Foundation Model for Macrocycle Structure Prediction and Design
arXiv International Overview Vilya-1 has been introduced as a pioneering all-atom foundation model for macrocycle structure prediction and design. This model aims to resolve existing challenges in sampling biologically relevant conformat... -
ACS Publications Reviews LLM-Based Autonomous Agents in Heterogeneous Catalysis, Addressing Tool Integration and Outlook
ACS Publications USA Overview A review paper in ACS Publications systematically analyzes the current state and prospects of Large Language Model (LLM)-based autonomous agent systems for heterogeneous catalysis research. The paper identif... -
arXiv Paper: Hypothesis Evolution Protocol (HEP) Establishes Auditable Scientific Discovery Capabilities for LLM Agents
arXiv International Overview A Hypothesis Evolution Protocol (HEP) has been proposed as a novel agent harness for Large Language Model (LLM)-based scientific discovery. This protocol enables explicit and auditable hypothesis generation, ... -
Springer Nature Launches Call for Papers on ML Methods for Crystalline Defects, Emphasizing Integration with Atomic Simulations
Research Communities (Springer Nature) International Overview Research Communities by Springer Nature has initiated a call for papers focusing on machine learning (ML) methods for modeling and predicting crystalline defects. The call enc... -
Oxford Academic Paper: DeepMeso (DeepFerro) Enables Rational Multi-Scale Design for Ferroelectrics
Oxford Academic International Overview The DeepMeso framework, specifically DeepFerro for ferroelectric materials, has been introduced to address existing challenges in the rational design of mesoscopic heterogeneous materials. This inno... -
LAMMPS Integrates Diverse Machine Learning Potentials Like MACE and CHGNet, Enhancing Atomic Simulation Versatility
LAMMPS Molecular Dynamics Simulator Unknown Overview The LAMMPS molecular dynamics simulator has significantly enhanced its interoperability with leading machine learning potential (MLP) frameworks, including MACE, CHGNet, DeePMD-kit, Ne...
