Deep Learning– tag –
-
U.S. Department of Energy Declares AI-Curated Data Convergence a Materials Discovery, Design, and Qualification Turning Point
Department of Energy USA Overview The U.S. Department of Energy asserts that the convergence of AI technology with curated datasets is a critical turning point for materials discovery, design, and qualification. They advocate for physics... -
Scilight Press Publishes Review on AI-Driven Rational Design of Solid-State Electrolytes: Paving the Way for Next-Gen Batteries
Scilight Press Unknown Overview Scilight Press published a review paper on AI's role in advancing the rational design of solid-state electrolytes (SSEs) for high energy density and safety in next-generation rechargeable batteries. The pa... -
CataCon Introduces Contrastive Graph Representation Learning for Catalyst Prediction, Accelerating Optimal Catalyst Identification via AI
PMC USA Overview This research introduces 'CataCon,' a novel contrastive graph representation learning framework designed to predict optimal catalysts for chemical reactions. CataCon generates rich structural embeddings for all reaction ... -
Quantum Machine Learning Interatomic Potential Achieves Enhanced Molecular Energy Prediction with Variational Quantum Algorithm
arXiv USA Overview This study applied quantum circuit learning to machine learning interatomic potentials (MLIPs), improving molecular dataset energy predictions. Using a quantum transfer learning architecture, the ANI model was retraine... -
Energy-Constrained MLIP Embedding Improves Prediction Accuracy for Hydrogen Evolution Electrocatalysts
ACS Publications (Journal of Chemical Information and Modeling) Overview Researchers developed an "energy-constrained" machine learning framework integrating MLIP-derived energy descriptors with pre-trained Crystal Hamiltonian Graph Neur... -
Machine Learning Interatomic Potentials: Foundation Models Reshape Materials Discovery
Review of Peer-Reviewed Articles Overview A new comprehensive review critically examines Machine Learning Interatomic Potentials (MLIPs), a transformative technology bridging the accuracy of quantum mechanics with the efficiency of class... -
AI Revolutionizes Biomaterial Design for Tissue Engineering, Overcoming Empirical Limitations in Property Prediction, Inverse Design, and Manufacturing Optimization
IntechOpen Croatia Overview This comprehensive review highlights AI's transformative applications in biomaterial design for tissue engineering, encompassing machine learning, deep learning, and generative models. AI provides data-driven ... -
Pauli Charges Dramatically Enhance Accuracy and High-Pressure, High-Temperature Stability of Machine-Learned Interatomic Potentials
ChemRxiv International Overview This research significantly improves machine-learned interatomic potentials (MLIPs) by integrating Pauli repulsion into a combination of machine-learned pair potentials and short-range atomic neural networ... -
arXiv: New ML Potential ‘TWIN’ Achieves Ab Initio Accuracy and Transferability in Biomolecular Simulations
arXiv Global Overview A new arXiv preprint introduces TWIN (Transferable Water Implicit Network), a highly transferable implicit solvent machine-learning potential (MLP) for biomolecular systems. TWIN, trained solely on ab initio and exp... -
Machine Learning Transforms Materials Science: From Property Prediction to Structural Design, Emphasizing LLM Validation
Academic Review / Journal Global Overview A new academic review highlights the transformative role of machine learning (ML) in materials science, detailing its impact on property prediction, novel material discovery, and process optimiza...
