Results 51 to 60 of about 4,489 (189)

Research on GPR-MPC Intelligent Control System for Paddy Rice Drying in Cross-Flow Circulating Grain Dryer

Agriculture
In order to improve the control accuracy and adaptability of drying control systems in complex paddy rice drying processes, the Gaussian process regression model predictive (GPR-MPC) drying process control strategy is designed.
Qi Song, Yongjie Zhang, Weihong Sun, Dongdong Du, Shaochen Zhang, Anzhe Wang, Wenming Chen, Xinhua Wei   +7 more
doaj   +1 more source

Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating

Results in Surfaces and Interfaces, 2023
The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using regression machine learning technique. A pot tester helped to examine the erosion rate of WC-10Co4Cr thermal spray coatings. WC-10Co4Cr thermal spray powder was sprayed onto
Jashanpreet Singh, Satish Kumar, Ranvijay Kumar, S.K. Mohapatra   +3 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics

Scientific Reports
A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression
Heyder Mhohamdi, Usama S. Altimari, Krunal Vaghela, V. Vivek, Sarbeswara Hota, Devendra Singh, Mahesh Manchanda, Shirin Shomurotova, Prakhar Tomar, Mohammad Mahtab Alam   +9 more
doaj   +1 more source

Boosting battery health prediction in electric vehicles via extended quantum optimized model with gaussian process regression

Next Materials
Accurate forecasting of battery state of health is essential for ensuring the reliability of electric vehicles. Conventional Quantum Particle Swarm Optimized Neural Network struggles with capturing non-linear temporal dependencies in battery degradation ...
Kian Lun Soon, Lam Tatt Soon, Nai Shyan Lai, Raveendran Paramesran, Wen Xun Lian, Chee Yong Lau   +5 more
doaj   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source