Results 211 to 220 of about 27,241 (297)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Neutral point clamped inverter for enhanced grid connected PV system performance based on hexagonal space vector modulation. [PDF]

Sci Rep
Palanisamy R, Vijayakumar K, Gulbarga MI, Al Awadh M, Hui LT.   +4 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Fast Growth of Centimeter-Scale Molybdenum Disulfide Single Crystal for Energy-Efficient Logic Circuits. [PDF]

Research (Wash D C)
Zheng B, Wang H, Wang Y, Zheng W, Liu Y, Wu G, Li M, Wang S, Sun X, Zhu C, Yang X, Xu Z, Zhu M, Xiang L, Li D, Pan A.   +15 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

A novel data-driven NLMPC strategy for techno-economic microgrid management with battery energy storage under uncertainty. [PDF]

Sci Rep
Yaghoubi E, Yaghoubi E, Jahromi MZ, Maghami MR, Paşaoğlu A, Chamorro HR.   +5 more
europepmc   +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

Evaluation of electric vehicle performance using driving cycle clustering based on motor-inverter losses and efficiency. [PDF]

Sci Rep
Abdelali K, Bendjedia B, Rizoug N, Kraiem H, Flah A.   +4 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

A new 37- level inverter with reduced switches for renewable energy applications. [PDF]

Sci Rep
Shukla S, Goel V, Dhanamjayulu C.
europepmc   +1 more source