Results 171 to 180 of about 16,838,211 (334)

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   +1 more source

Measuring the Hall Effect in Hysteretic Materials

Advanced Materials, EarlyView.
The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya, Anthony Voyemant, Sudipta Chatterjee, Scott B. Lee, Grigorii Skorupskii, Connor J. Pollak, Leslie M. Schoop   +6 more
wiley   +1 more source

Energy-Efficient Deep Learning Models for Cyber Threat Prediction in Constrained IoT Infrastructure

As the Internet of Things (IoT) continues to grow, the need for efficient and effective cybersecurity solutions becomes increasingly critical. This paper presents a novel approach to cyber threat prediction in constrained IoT environments through the ...
Garba Sani
core  

Large‐Scale Determination of Frontier Orbital Energies of Disordered Small‐Molecule Organic Semiconductors Using Exciplex Emission Spectra

Advanced Materials, EarlyView.
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald, Hiroki Tomita, Giacomo Cotelli, Louka L. Brüll, Nanno G. de Rooij, Lucas R.L. Norg, Milo Clément, Reinder Coehoorn   +7 more
wiley   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source

Deep Reinforcement Learning for Energy Efficiency in Smart Grids

International Journal for Research Publication and Seminar
This paper presents a deep reinforcement learning model that optimizes energy consumption in smart grids, taking into account realtime user demand and renewable energy availability. 
openaire   +1 more source

Demagnetization‐Driven Nanoscale Chirality‐Selective Phonon Transport

Advanced Materials, EarlyView.
Chiral heat flow is switched in a ferromagnet/chiral α‐SiO2 heterostructure by controlling the angular momentum of demagnetization‐driven chiral phonons. When phonon handedness matches the crystal chirality, nanoscale thermal transport is enhanced; the opposite handedness suppresses transport, establishing a magnetization‐tunable chiral phonon diode ...
In Hyeok Choi, Daeheon Kim, Yeon Jong Jin, Seungmo Yang, Tae‐Seong Ju, Changsoo Kim, Chanyong Hwang, Dongbin Shin, Jong Seok Lee   +8 more
wiley   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

Watt For What: Rethinking Deep Learning\u27s Energy-Performance Relationship [PDF]

Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy.
Jin, Xiaobo, Gowda, Shreyank N, Hao, Xinyue, Gowda, Shashank Narayana, Sevilla-Lara, Laura, Li, Gen   +5 more
core   +1 more source