Results 31 to 40 of about 36,209 (267)

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP

Yantu gongcheng xuebao, 2023
In order to establish a prediction method for the ground peak acceleration (PGA) that does not depend on the soil constitutive model but only on the ground motion and site characteristics, six characteristic parameters are chosen through the feature ...
QI Wanwan 1, 2, SUN Rui 1, 2, ZHENG Tong 1, 2, QI Jinlei 1, 2
doaj   +1 more source

Fool SHAP with Stealthily Biased Sampling

CoRR, 2022
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations.
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara 0001, Mario Marchand, Foutse Khomh   +4 more
openaire   +3 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

On the Suitability of SHAP Explanations for Refining Classifications

Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 2022
In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in an ML pipeline is paramount to reduce the workload of experts while increasing customers’ trust.
Yusuf Arslan, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Clément Lefebvre, Andrey Boytsov, Anne Goujon, Tegawendé F. Bissyandé, Jacques Klein   +8 more
openaire   +2 more sources

Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts

Advanced Materials, EarlyView.
Mechanistically interpretable artificial intelligence screens nearly seven million perovskite compositions and identifies key descriptors—d‐p hybridization and densification resistance—that govern oxygen electrocatalysis. The discovered BaCo0.8Nb0.1Zr0.1O3‐δ achieves a record 2.68 W cm−2 peak power density at 600°C with over 500 h of durable operation ...
Xueyu Hu, Yucun Zhou, Haoyu Li, Zheyu Luo, Nai Shi, Yong Ding, Weining Wang, Weilin Zhang, Doyeub Kim, Chanho Kim, Yoojin Ahn, Nikhil Govindarajan, Minda Zou, Zhijun Liu, Yuefeng Song, Meilin Liu   +15 more
wiley   +1 more source

Personalized Network‐Guided Neuromodulation Enhances Human Working Memory

Advanced Science, EarlyView.
A personalized neuromodulation framework combining individualized functional brain network targeting with real‐time neural decoding is introduced. Using concurrent TMS–fMRI, participant‐specific stimulation targets and optimal frequencies are identified. Only optimal‐frequency stimulation improves working memory across sessions.
Ahsan Khan, Hongming Li, Camille Blaine, Julie Grier, Ethan Hammett, Almaris Figueroa‐Gonzalez, Sarai Garcia, Romain Duprat, Justin Reber, Joseph Deluisi, Christos Davatzikos, Theodore D. Satterthwaite, Yong Fan, Desmond J. Oathes   +13 more
wiley   +1 more source

Towards Trustable SHAP Scores

Proceedings of the AAAI Conference on Artificial Intelligence
SHAP scores represent the proposed use of the well-known Shapley values in eXplainable Artificial Intelligence (XAI). Recent work has shown that the exact computation of SHAP scores can produce unsatisfactory results. Concretely, for some ML models, SHAP scores will mislead with respect to relative feature influence.
Olivier Létoffé, Xuanxiang Huang, João Marques-Silva 0001   +2 more
openaire   +2 more sources

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source