Results 41 to 50 of about 88,634 (286)

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

Design and prediction of high optical density photovoltaic polymers using machine learning-DFT studies

Open Physics
This research provides a machine learning (ML) method to predict the optical density of photovoltaic (PV) polymers. The results show that extra gradient boosting regressor and random forest regressor are the best-performing models among all the tested ML
Aljaafreh Mamduh J., Hassan Abrar U.
doaj   +1 more source

Data‐Driven Discovery of Quaternary Ammonium Interlayers for Efficient and Thermally Stable Perovskite Solar Cells

Advanced Materials, EarlyView.
An active learning framework, grounded in independently generated in‐house experimental data, enables reliable discovery of high‐performance interfacial materials for perovskite solar cells. Iterative model refinement autonomously converges toward structurally robust quaternary ammonium architectures, establishing a new design principle for interfacial
Jongbeom Kim, Yang Jeong Park, Chaehoon Jeon, Nahye Shin, Jaewang Park, Seungun Lee, Jino Im, Sungroh Yoon, Sang Il Seok   +8 more
wiley   +1 more source

OSL investigations at Hardisty, Alberta, Canada [PDF]

, 2015
This report is concerned with optically stimulated luminescence (OSL) dating investigations of sediments associated with, and enclosing artefacts of First Nations historic significance in the Battle River Valley area, near Hardisty, east central ...
Kinnaird, Tim C., Munyikwa, Ken, Sanderson, David C.W.   +2 more
core   +4 more sources

Machine learning-guided synthesis of advanced inorganic materials

, 2019
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.
Chouhan, Tushar, Golani, Prafful, Guan, Cuntai, Liu, Zheng, Lu, Yuhao, Tang, Bijun, Wang, Han, Xu, Manzhang, Xu, Quan, Zhou, Jiadong   +9 more
core   +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

Single‐Cell Metabolic Imaging and Digital Scoring of Fat Tissue Remodeling by Label‐Free Metabolic Microscopy

Advanced Science, EarlyView.
Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim, Constantin Berger, Alexander Wolf, Alina Peteranderl, Martin Klingenspor, Vasilis Ntziachristos, Yongguo Li, Miguel A. Pleitez   +7 more
wiley   +1 more source

Characterization of Spatial and Temporal Coupling of Digital Economy and Carbon Emission in Yangtze River Delta Urban Agglomerations and the Influence Factors by Integrating GWRF and SHAP

Redai dili
Against the strategic backdrop of "Digital-China" and the "Dual-Carbon" goals, the synergistic advancement of digital economy and carbon emission reduction is crucial for achieving high-quality, sustainable development.
Zhang Qianwei, Xi Guangliang
doaj   +1 more source

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

, 2020
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed.
Denman, Simon, Dissanayake, Theekshana, Fernando, Tharindu, Fookes, Clinton, Ghaemmaghami, Houman, Sridharan, Sridha   +5 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source