Results 121 to 130 of about 6,810,610 (247)

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

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

LRGCL: LLM-refined graph contrastive learning for review-based recommendation

Journal of King Saud University: Computer and Information Sciences
Review-based recommendations commonly construct user-item interaction graphs in which textual review semantics serve as edge attributes. However, two practical limitations persist.
Liye Shi, Pengfei Shao, Shaozhong Zhang, Jiayi Chen   +3 more
doaj   +1 more source

Boosting Patient Representation Learning via Graph Contrastive Learning

Building deep neural network models for clinical prediction tasks is an increasingly active area of research. While existing approaches show promising performance, the learned patient representations from deep neural networks are often task-specific and ...
Liu, Y, Salim, FD, Yepes, AJ, Bian, J, Long, G, Shen, J, Li, F, Zhang, Z   +7 more
core   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models

Advanced Materials, EarlyView.
Leveraging literature data, we build a large‐language‐model‐driven workflow that extracts synthesis steps from 4407 papers, retrieves similar precedents, and generates candidate solid‐state synthesis recipes. The system benchmarks against ground‐truth and then operates in a closed loop with experiments to synthesize oxy‐selenide electrolyte materials ...
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon, Joo Yeong Na, Heegyu Kim, Hyunsouk Cho, Dae Soo Jung, Ju Li, Jin‐Sung Park, Sung Beom Cho   +9 more
wiley   +1 more source

ArieL: Adversarial Graph Contrastive Learning. [PDF]

ACM Trans Knowl Discov Data, 2023
Feng S, Jing B, Zhu Y, Tong H.
europepmc   +1 more source

Adversarial Contrastive Graph Masked AutoEncoder Against Graph Structure and Feature Dual Attacks

Graph Neural Networks (GNNs) have been shown vulnerable to graph adversarial attacks. Current robust graph representation learning methods mainly defend against graph structure attack, and improves performance of GNNs.
Shen, Xiaobo, Pan, Shirui, Shen, X, Shen, W, Shen, Weixuan, Pan, S   +5 more
core   +1 more source

Polyphenol‐Inspired Materials for Agricultural Applications

Advanced Materials, EarlyView.
This review outlines the use of polyphenol‐inspired materials for sustainable agriculture, highlighting their molecular design, interfacial assembly, structure–property relationships, and prospects toward precision agriculture, climate resilience, ecosystem protection, and circular bioeconomy strategies.
Haofu Liu, Omid Mazaheri, Zhixing Lin, Tianzheng Wang, Jingqu Chen, Yuang Gu, Xia Liang, Deli Chen, Frank Caruso   +8 more
wiley   +1 more source