Materials Informatics– category –
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ACS Publications Proposes Physics-Driven Computational Paradigm with AI-Assisted Multiscale Simulation for Low-Temperature Flexible Organic Crystals
ACS Publications USA Overview This research proposes a physics-driven computational paradigm for elucidating the properties of low-temperature flexible organic crystals. Combining AI-assisted multiscale simulation and machine-learning fo... -
arXiv Paper Demonstrates Full-Data Accuracy with Fewer Labels in ML Force Field Training Through Data Selection Strategy
arXiv International Overview This paper investigates the critical role of data selection in training and fine-tuning machine-learning force fields (MLFFs), demonstrating that active learning strategies like LLPR (Least-Likely to Predict ... -
MLIP Studio Launches Free Web App ‘MLIP Studio,’ Accelerating Molecular and Materials Simulations with Over 60 Universal MLPs
MLIP Studio Blog Unknown Overview MLIP Studio has released 'MLIP Studio,' a free web application enabling atomic calculations for molecules and materials using universal machine-learning interatomic potentials (MLIPs). This application i... -
arXiv Paper: Model-Agnostic Graph Prompt Learning Significantly Enhances GNN Crystal Property Prediction Accuracy
arXiv International Overview This paper proposes a novel model-agnostic soft prompt learning framework to improve the crystal property prediction performance of Graph Neural Networks (GNNs). By incorporating node-level and graph-level so... -
arXiv Paper: Equivariant Graph Neural Networks Revolutionize Atomic Modeling and Advance Molecular Coarse-Graining
arXiv International Overview This paper discusses how machine-learning interatomic potentials (MLIPs), including equivariant graph neural networks (EGNNs), have transformed atomic modeling by learning potential energy surfaces from quant... -
arXiv Paper Examines Limitations and Potential of MACE and CHGNet Foundation MLIPs in d4/d6/d7 Perovskite Oxide MD Simulations
arXiv International Overview This paper provides a detailed examination of the successes and limitations of foundation machine-learning interatomic potentials (MLIPs) like MACE and CHGNet in molecular dynamics (MD) simulations of d4/d6/d... -
YouTube Channel yaavikmaterials: ML Interatomic Potentials Enable Large-Scale MD with DFT Accuracy, Resolving System Size Trade-offs
yaavikmaterials (YouTube) Unknown Overview A YouTube video by yaavikmaterials explains how machine-learning interatomic potentials (MLIPs) like MACE, NequIP, and CHGNet are resolving the system size and simulation time trade-offs in mole... -
EngineerMD: Generative AI in Product Design Significantly Cuts Development Time and Cost by Auto-Generating Thousands of Design Options
EngineerMD Unknown Overview An EngineerMD article explores how generative AI is transforming product design in 2026, highlighting its ability to automate design space exploration, simulate performance, and generate thousands of design va... -
IntuitionLabs Compares AlphaFold 3 and ESM3, Highlighting Evolution of AI Biology Foundation Models and Generative Diffusion Superiority
IntuitionLabs Unknown Overview An IntuitionLabs analysis provides a detailed comparison of leading AI biology foundation models like AlphaFold 3 and ESM3. The article focuses on models such as AlphaFold 3, a generative diffusion model fo... -
GitHub Repository ‘awesome-ai-for-science’ Launched, Featuring Google GNoME, Microsoft MatterGen, and Other Curated AI Tools Accelerating Scientific Discovery
GitHub (ai-boost) International Overview The 'awesome-ai-for-science' repository has been launched on GitHub, providing a curated list of AI tools and frameworks that accelerate discovery across diverse scientific fields, including physi...
