Results 231 to 240 of about 76,555 (293)

Synergistically optimized electron and phonon transport in high-performance copper sulfides thermoelectric materials via one-pot modulation. [PDF]

Nat Commun
Zhang YX, Huang QY, Yan X, Wang CY, Yang TY, Wang ZY, Shi YC, Shan Q, Feng J, Ge ZH.   +9 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems

Advanced Intelligent Discovery, EarlyView.
This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Br doping-induced evolution of the electronic band structure in dimorphic and hexagonal SnSe₂ thermoelectric materials. [PDF]

RSC Adv
Kim SJ, Heo M, Kim SI, Park H, Kim JY, Seo WS, Kim HS.   +6 more
europepmc   +1 more source

Comprehensive study of nanostructured Bi₂Te₃ thermoelectric materials - insights from synchrotron radiation XRD, XAFS, and XRF techniques. [PDF]

RSC Adv
Imam NG, Elyamny S, Aquilanti G, Pollastri S, Gigli L, Kashyout AEB.   +5 more
europepmc   +1 more source

Inorganic Thermoelectric Materials

, 2022
openaire  

Nanotwinned thermoelectric materials

Materials Today Electronics
Ting-Rui Luo, Yingchao Wei, Zheng Ma, Junyou Yang   +3 more
openaire   +1 more source

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Thermoelectric materials

2023
Click on the DOI to access this article (may not be free). ; In this chapter, the chemistry of the thermoelectric materials is discussed. First a basic introduction to the thermoelectrics phenomena, fundamental materials parameters, and potential device applications is provided. Then, basic approaches to optimize thermoelectric properties are discussed
Yox, Philip, Viswanathan, Gayatri, Sarkar, Arka, Wang, Jian, Kovnir, Kirill, Reedijk, Jan, Poeppelmeier, Kenneth R.   +6 more
openaire   +2 more sources