Machine Learning– tag –
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MIT Researchers Uncover Critical Role of Coverage-Dependent Lateral Interactions in High-Entropy Alloy Electrocatalytic Activity for Oxygen Reduction Reaction via MLIPs
PubMed USA Overview MIT researchers developed a framework utilizing Machine Learning Interatomic Potentials (MLIPs) to model the oxygen reduction reaction (ORR) within the compositional space of Ag-Ir-Ru-Pd-Pt-Cu-Rh-Re high-entropy alloy... -
UNCC Research Group Develops ‘PyXtal_FF’ for ML Interatomic Potential Generation, Dramatically Reducing Computational Cost of Atomic Simulations
University of North Carolina at Charlotte USA Overview Researchers at the University of North Carolina at Charlotte (UNCC) have developed 'PyXtal_FF,' a package for generating Machine Learning Interatomic Potentials (MLIAP). MLIAPs enabl... -
Matmerize Launches AI-Driven PolymRize Platform to Accelerate Polymer Design for Gas Separation Membranes and More
Matmerize (Halo) USA Overview Matmerize has introduced 'PolymRize,' a cloud-based AI and machine learning polymer informatics platform. This platform instantly predicts and designs polymers with tailored gas permeability and selectivity,... -
Notre Dame University Develops AI Polymer Discovery Platform to Overcome Data Scarcity, Accelerating Novel Material Exploration
Notre Dame Research USA Overview Notre Dame University's AI & Materials Initiative developed a framework leveraging multiple information sources to overcome data scarcity in polymer informatics. They created ADEPT, an automated molec... -
ML Interatomic Potentials Uncover Order-Disorder Transition and Non-Monotonic Stiffness in (MoCrTi)$_{100-x}$Al$_x$ Refractory High-Entropy Alloys
arXiv International Overview This research leverages universal machine learning interatomic potentials combined with hybrid Monte Carlo and molecular dynamics simulations to investigate the chemical ordering and mechanical properties of ... -
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 ... -
AI-Driven Precision: LSBoost Ensemble ML Revolutionizes Defect Management in Additive Manufacturing
MDPI Switzerland Overview This research introduces a novel machine learning approach utilizing decision tree LSBoost ensembles to significantly enhance quality control and reproducibility in additively manufactured parts, particularly th... -
Functional Classification and XGBoost Integration with 10,000+ Semiconductor/Insulator Data Significantly Boosts Electronic Bandgap Prediction Accuracy
AIP Publishing USA Overview This research systematically investigates how functional classification of electronic bandgaps in over 10,000 semiconductors and insulators significantly improves machine learning model accuracy. Utilizing a P... -
Scalable Isotropic Message Passing ML Accelerates Electronic Structure and Atomistic Property Modeling by Efficiently Describing Non-Local Effects
The Journal of Chemical Physics | AIP Publishing USA Overview A new study demonstrates a scalable machine learning approach for isotropic message passing, leveraging continuous products of external potentials to model electronic structur... -
UniFFBench Reveals Critical Role of System-Specific Fine-Tuning for Universal ML Force Fields via Rigorous Experimental Benchmarking of 6 EGraFF Algorithms
ResearchGate International Overview The UniFFBench study rigorously evaluates universal machine learning interatomic potentials (uMLIPs) from six prominent EGraFF algorithms—NequIP, Allegro, BOTNet, MACE, Equiformer, and TorchMDNet—again...
