Machine Learning– tag –
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Kolmogorov–Arnold Networks Revolutionize Thermoelectric Materials Design: Achieving High-Accuracy and Interpretable Property Prediction
PMC USA Overview This research introduced Kolmogorov–Arnold Networks (KANs) for thermoelectric property prediction to provide accurate and interpretable models for high-performance thermoelectric materials design. KANs achieved predictiv... -
Northwestern University Develops Novel AI-Driven Computational Method to Unravel Complex Atomic Structures at Material Interfaces
McCormick School of Engineering (Northwestern University) USA Overview Northwestern University researchers developed a new AI-driven computational method to reveal the complex atomic structures at material interfaces. This approach combi... -
Massive Discovery of 9,139 Low-Dimensional Materials from Materials Project via Universal Computational Strategy, Including 887 Exfoliable 2D Materials
ACS Chemistry of Materials USA Overview This research combined universal machine learning interatomic potentials (UMLIPs) with an advanced force constant (FC)-based dimensionality classification method to massively discover new low-dimen... -
Transferable Machine Learning Interatomic Potential Accurately Predicts Thermodynamics, Structure, and Dynamics of Entangled Polymers from Oligomer Training
arXiv USA Overview This paper investigates machine learning interatomic potential (MLIP) development for polymers, identifying Atomic Cluster Expansion (ACE) as the most effective descriptor for polyethylene. It demonstrates that ACE pot... -
ACS Materials Au Publishes Comprehensive Tutorial on Machine Learning Tools for Electrocatalysis Simulations
ACS Materials Au USA Overview ACS Materials Au has released a tutorial on machine learning tools for electrocatalysis simulations, showcasing instruments like ML exchange-correlation functionals, Gaussian process optimizers, and ML inter... -
Deletion Method Outperforms Generative AI Approaches for Extracting Atomic Environments in MLIP-Driven MD Simulations
arXiv USA Overview A systematic analysis of optimal methods for extracting small atomic environments suitable for DFT calculations from large structures, a challenge in applying machine learning interatomic potentials (MLIPs) to large-sc... -
Atomic Representations from Pretrained MLIPs Prove Effective for Materials Generative Model Evaluation
arXiv USA Overview This paper demonstrates that atomic average features derived from pretrained machine learning interatomic potentials (MLIPs) like MACE are effective for evaluating the outputs of materials generative models. The resear... -
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... -
AI Revolutionizes Diagnostics and Patient Care: Early Disease Detection and Personalized Treatment via Wearables and Predictive Analytics
Medriva Unknown Overview Artificial Intelligence (AI) is fundamentally transforming medical diagnostics and patient care, ushering in a new era of medicine. AI platforms aggregate and interpret vast physiological data from wearable devic... -
AI Transforms Medical Devices and Healthcare: Wearables and Intelligent Systems Enhance Diagnostic Accuracy and Patient Monitoring
Hindawi (Journal of Healthcare Engineering) International Overview Artificial Intelligence (AI) is transforming medical devices into intelligent systems, significantly improving diagnostic accuracy, enhancing patient monitoring, and prov...
