Advances in theory and computational methods for next-generation thermoelectric materials

Applied Physics Reviews, Год журнала: 2025, Номер 12(1)

Опубликована: Март 1, 2025

This is a review of theoretical and methodological development over the past decade pertaining to computational characterization thermoelectric materials from first principles. Primary focus on electronic thermal transport in solids. Particular attention given relationships between various methods terms hierarchy as well tradeoff physical accuracy efficiency each. Further covered are up-and-coming for modeling defect formation dopability, keys realizing material's potential. We present discuss all these close connection with parallel developments high-throughput infrastructure code implementation that enable large-scale computing screening. In all, it demonstrated advances tools now ripe efficient accurate targeting needles haystack, which “next-generation” materials.

Язык: Английский

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Large language model-driven database for thermoelectric materials

Computational Materials Science, Год журнала: 2025, Номер 253, С. 113855 - 113855

Опубликована: Март 30, 2025

Язык: Английский

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Synergistically optimize thermoelectric and mechanical properties of cubic SnSe-based alloys via nanocomposite engineering utilizing SiC nanoparticles as the dispersed phase

Rare Metals, Год журнала: 2025, Номер unknown

Опубликована: Апрель 4, 2025

Язык: Английский

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Thermoelectric performance enhancement of copper iodide pellets through potassium iodide doping

Journal of Power Sources, Год журнала: 2025, Номер 643, С. 237043 - 237043

Опубликована: Апрель 17, 2025

Язык: Английский

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Research Progress of Thermoelectric Materials—A Review

Energies, Год журнала: 2025, Номер 18(8), С. 2122 - 2122

Опубликована: Апрель 21, 2025

Thermoelectric materials are functional that directly convert thermal energy into electrical or vice versa, and due to their inherent properties, they hold significant potential in the field of conversion. In this review, we examine several fundamental strategies aimed at enhancing conversion efficiency, classification, preparation methods, applications thermoelectric materials. First, introduce an important parameter for evaluating performance materials, dimensionless quality factor ZT, present theory electroacoustic transport which provides foundation Second, optimizing carrier concentration, band engineering, phonon entropy engineering summarized, emphasizing presents numerous possibilities material by tuning effective mass, convergence, resonance. By analyzing importance various optimization strategies, it is concluded co-optimization primary method improving future. addition, overview currently available provided, including two categories, classical novel along with a highlight techniques. Finally, principles technology illustrated, its fields discussed, problems current research analyzed, future trends outlined. Overall, paper comprehensive summary classifications, applications, offering valuable references insights researchers field, aim further advancing development science.

Язык: Английский

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Machine learning-enabled optoelectronic material discovery: a comprehensive review

Journal of Materials Informatics, Год журнала: 2025, Номер 5(3)

Опубликована: Май 28, 2025

The development of advanced optoelectronic materials constitutes a pivotal frontier in modern energy and communication technologies, facilitating critical energy-photon-electron interconversion processes that underpin sustainable infrastructures high-performance electronic devices. However, the discovery optimization novel face substantial hurdles arising from complicated structure-property interdependencies, prohibitive costs, protracted innovation cycles. Conventional empirical approaches computational simulations usually exhibit limited efficacy addressing escalating demands for with superior stability, economic viability, customizable properties. integration machine learning (ML) high-throughput screening has emerged as transformative strategy to address these challenges. By rapidly processing large multidimensional datasets predicting material properties such structure, thermodynamic charge transport behaviors, ML offers unprecedented capabilities efficient rational design materials. This review provides comprehensive overview cutting-edge ML-driven methodologies emphasis on workflows, data strategies, model frameworks. We also discuss challenges prospects applications, particularly standardization, interpretability closed-loop experimental validation. further propose potential artificial intelligence autonomous laboratories build powerful pipeline advance

Язык: Английский

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Universality of optoelectronic and thermoelectric properties of novel two-dimensional CrSiM2N (M=As, P and N Se, Te) monolayers and their vdWHs with MoTe2

Solid State Communications, Год журнала: 2024, Номер 389, С. 115581 - 115581

Опубликована: Июнь 4, 2024

Язык: Английский

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Rational triple optimizations boost near-room-temperature thermoelectric performance of BiSe

Acta Materialia, Год журнала: 2024, Номер 280, С. 120343 - 120343

Опубликована: Авг. 30, 2024

Язык: Английский

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Enhanced service stability and thermoelectric performance in Cu2−δSe-based composites

Science China Materials, Год журнала: 2024, Номер 67(7), С. 2369 - 2370

Опубликована: Май 22, 2024

Язык: Английский

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Size and surface-dependent phase transition temperature in Cu2Se nanobridges

Nano Today, Год журнала: 2024, Номер 58, С. 102460 - 102460

Опубликована: Авг. 29, 2024

Язык: Английский

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