Machine learning in thermoelectric materials identification: Feature selection and analysis

Xu, Yaqian; Jiang, Lu; Qi, Xiang

doi:10.1016/j.commatsci.2021.110625

Cited by 25 publications

(10 citation statements)

References 42 publications

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“…For example, several of these models were evaluated in search of intermetallic alloys with high TE performance, and RF was chosen as the best predictor that screened over 130 000 materials and narrowed them down to 6476 candidates. 210 In another study, BO was used to iteratively optimize the output of first-principles calculations and screened over 50 000 compounds to predict ones with low thermal conductivities. 211 ML algorithms have also been used for screening various TE properties such as Seebeck coefficients and power factors for various traditional bulk (3D) and 2D TE materials such as oxides, nitrides, and half-Heuslers.…”

Section: Discussionmentioning

confidence: 99%

“…(b) Schematic of applying the machine learning model method to predict efficient thermoelectric materials. 210 (c) Thermopower of the Fe-Pt-Sm system as a function of composition. 226,227 This journal is © The Royal Society of Chemistry 2022 expensive, high-throughput screening using ML is crucial for increasing the efficiency of the materials discovery process.…”

Section: Machine Learningmentioning

confidence: 99%

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Phonon anharmonicity in binary chalcogenides for efficient energy harvesting

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Phonon anharmonicity in binary chalcogenides for efficient energy harvesting

et al. 2022

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“…187 However, the family of thermoelectric materials is huge, as illustrated in Figure 11, requiring more general material descriptors for ZT values, either physics-inspired or data-driven. 188,189 Xu et al applied the random forest method to train thermoelectric data from 204 materials and obtained coefficients of determination higher than 0.9 by using four descriptors from the information entropy evaluation of an ExtraTree-based model. 189 Some better descriptors could be found from the several visualization databases for properties relevant to thermoelectrics.…”

Section: ■ Thermal Energy Materials Genealogymentioning

confidence: 99%

“…188,189 Xu et al applied the random forest method to train thermoelectric data from 204 materials and obtained coefficients of determination higher than 0.9 by using four descriptors from the information entropy evaluation of an ExtraTree-based model. 189 Some better descriptors could be found from the several visualization databases for properties relevant to thermoelectrics. 190−192 Moreover, structural design and material manufacturing methods such as chemical mixing and thermal processing can also be optimized with ML.…”

Section: ■ Thermal Energy Materials Genealogymentioning

confidence: 99%

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Dai

2022

ACS Energy Lett.

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Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

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“…Material informatics integrated with informatics algorithms for material structure optimization has been demonstrated superior to traditional empirical trialand-error methods in the design of multi-degree-of-freedom thermal functional materials [41,42]. It has high efficiency in thermal transport design [43][44][45], thermoelectric optimization [46][47][48] and thermal radiation design [49][50][51]. Moreover, some optimal structures or devices such as aperiodic GaAs/AlAs superlattice structure with low coherent phonon heat conduction [52] and highly wavelength-selective, multilayer nanocomposite selective thermophotovoltaic emitter [53] have been experimentally fabricated, which demonstrates the applicability and efficiency of the informatics algorithms.…”

Section: Introductionmentioning

confidence: 99%