Results 141 to 150 of about 8,331,707 (294)

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Sharing Knowledge in Multi-Task Deep Reinforcement Learning [PDF]

, 2020
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to ...
A. Bonarini, D`Eramo, C., M. Restelli, Bonarini, A., D. Tateo, Tateo, D., J. Peters, Restelli, M., Peters, J., C. D'Eramo   +9 more
core  

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

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

Azobenzene's Cross‐Scale Optics and Photonics: Molecular Photoswitching, Mesoscopic Material Motions, and Adaptive Devices

Advanced Materials, EarlyView.
Azobenzene photoswitches translate molecular‐scale E/Z photoisomerization into macroscopic material responses and device‐level photonic functions. This Review highlights how azobenzene research has evolved from molecular photochemistry to photoalignment, mass migration, photomechanics, and heat release, ultimately enabling holography, reconfigurable ...
Heeju Son, Soyul Kwak, Heerin Noh, Minsang Kim, Doyeon Noh, Subhayan Chakraborty, Jaewon Lee, YongDeok Cho, Kyeongsoo Kim, Taewoong Eom, Moonyoung Kim, Jangwon Kim, Hoyoung Lee, Soyoung Lee, Jaehun Kim, Grace G. D. Han, Shin‐Tson Wu, Stefano Oscurato, Arri Priimagi, Marina Saphiannikova, Seungwoo Lee   +20 more
wiley   +1 more source

Thermodynamic Limits to Molecular Doping in Conjugated Polymers: A Perspective on Phase Behavior and Miscibility

Advanced Materials, EarlyView.
Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani, Justin Neu, Sung‐Joo Kwon, Runqio Song, Zixuan Chen, Tajah Trapier, Paschalis Gkoupidenis, David Ginger, Wei You, Jasper J. Michels, Harald Ade   +10 more
wiley   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation [PDF]

, 2017
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies ...
Narasimhan, Karthik Rajagopal, Saeedi, Ardavan, Tenenbaum, Joshua B, Kulkarni, Tejas D.   +3 more
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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

3D Deep Learning Course

The course includes the full framework for coming up with AI processing workflows, understanding why and how 3D can extend 2D approaches, developing and testing 3D Deep Learning Models, getting quickly working models on various OS, building your own ...
Poux, Florent
core