Results 91 to 100 of about 28,332 (249)

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

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

What Really Drives Agri‐Environment‐Climate Measures' Adoption? Mandatory Requirements, Behavioral Traits, and Structural Factors in the CAP Green Architecture

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT To increase farmer adoption of green practices, the EU Common Agricultural Policy includes both mandatory (conditionality) and voluntary instruments (eco‐schemes–ECS– and more demanding, multi‐annual agri‐environment‐climate measures –AECM–). Building on the experiment of Barreiro‐Hurle et al.
L. Sanchez‐Mata, M. Rodríguez‐Entrena, M. Espinosa‐Goded, F. J. Dessart, J. Barreiro‐Hurlé   +4 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

DetDSHAP: Explainable Object Detection for Uncrewed and Autonomous Drones With Shapley Values

IET Radar, Sonar & Navigation
Automatic object detection onboard drones is essential for facilitating autonomous operations. The advent of deep learning techniques has significantly enhanced the efficacy of object detection and recognition systems. However, the implementation of deep
Maxwell Hogan, Nabil Aouf
doaj   +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

A three-step machine learning strategy for reproducible biomarker screening in ovarian cancer

Machine Learning: Health
Biomarkers have a significant impact in health and medicine, including cancer research, screening, and diagnosis. Traditional statistical tests, however, often fail to provide consistent and reproducible quantitative measures of biomarker effects which ...
W Huang, H Suominen, T Liu, G Rice, C Salomon, A S Barnard   +5 more
doaj   +1 more source

Exploring Database Quality Through Shapley Values: Application to Dynamic Soil Parameters Databases

Geotechnics
Geotechnical engineering faces challenges related to data, especially the ones related to dynamic soil behavior (i.e., shear modulus reduction and damping ratio curves with strain), with only a few datasets in open-access format and a slow transition to ...
Julien Borderon, Nathalie Dufour, Julie Régnier   +2 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. [PDF]

J Biomed Inform, 2023
Nayebi A, Tipirneni S, Reddy CK, Foreman B, Subbian V.   +4 more
europepmc   +1 more source