Results 91 to 100 of about 8,272,458 (291)

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

AUTOMATED CRYPTOCURRENCIES PRICES PREDICTION USING MACHINE LEARNING

ICTACT Journal on Soft Computing, 2018
Currently, Cryptocurrency is one of the trending areas of research among researchers. Many researchers may analyze the cryptocurrency features in several ways such as market price prediction, the impact of cryptocurrency in real life and so on.
Ruchi Mittal, Shefali Arora, M.P.S Bhatia   +2 more
doaj   +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

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

Accelerated Materials Discovery Through In Situ X‐ray Diffraction: Solvothermal Formation of PdxMy (M═Si, Ge, Sn, Pb) Intermetallic Nanoparticles

Advanced Materials, EarlyView.
In situ PXRD data on solvothermal reactions accelerate materials discovery while simultaneously providing fundamental insights into complex crystal formation processes. Using PdxMy (M = Si, Ge, Sn, Pb) as an example, a general formation mechanism is identified, and specific synthesis strategies for ex situ preparation of SnPd2, Sn13Pd20, and SnPd ...
Anders Bæk Borup, Bo Brummerstedt Iversen   +1 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

A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics

Methods in Ecology and Evolution
Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract.
Meghan A. Balk, John Bradley, M. Maruf, Bahadir Altintaş, Yasin Bakiş, Henry L. Bart Jr, David Breen, Christopher R. Florian, Jane Greenberg, Anuj Karpatne, Kevin Karnani, Paula Mabee, Joel Pepper, Dom Jebbia, Thibault Tabarin, Xiaojun Wang, Hilmar Lapp   +16 more
doaj   +1 more source

Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep Learning-Based Applications

Sensors
This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques.
Gulsipat Abisheva, Nikolaj Goranin, Bibigul Razakhova, Tolegen Aidynov, Dina Satybaldina   +4 more
doaj   +1 more source

Beyond Passivation: Designing Structurally Tailored Artificial Interphases for High‐Energy Lithium‐Based Anodes

Advanced Materials Interfaces, EarlyView.
This review outlines critical engineering design rules for building artificial protective coatings on next‐generation lithium metal and silicon battery anodes. It highlights how balancing structural flexibility with strict electronic and ionic transport limits prevents early battery failure.
Surin Gupte, Vilas G. Pol
wiley   +1 more source

Genetic programming and deductive-inductive learning: a multistrategy approach [PDF]

, 1998
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24-27, 1998.Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions ...
Aler, Ricardo, Borrajo Millán, Daniel, Isasi, Pedro   +2 more
core   +1 more source