Composition and structure analyzer/featurizer for explainable machine-learning models to predict solid state structures

Emil I. Jaffal, Sangjoon Lee, Danila Shiryaev, Alex Vtorov, Nikhil Kumar Barua, Holger Kleinke and Anton O. Oliynyk

Digital Discovery, 2025, 4, 548
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Feature mining for thermoelectric materials based on interpretable machine learning

Yiyu Liu, Zilong Mu, Peichao Hong, Yun Yang and Changxu Lin

Nanoscale, 2025, 17, 2200
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Explainable machine learning-guided design of high-performance thermoelectric materials

Song Li, Songli Dai, Shiyu Xiao, Zhigang Yu, Heng Wang and Zean Tian

Journal of Alloys and Compounds, 2025, 1037, 182164
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Combined Machine Learning and Experimental Study for the Zintl Phase Thermoelectric Ba1−xSrxZn2−yCdySb2 System

Junsu Lee, Minkyoung Kim, Aziz Ahmed, Yurij Mozharivskyj and Tae‐Soo You

Zeitschrift anorg allge chemie, 2025, 651
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Advances and challenges in inorganic bulk-based flexible thermoelectric devices

Qing-Yi Liu, Xiao-Lei Shi, Tian-Yi Cao, Wen-Yi Chen, Lan Li and Zhi-Gang Chen

Progress in Materials Science, 2025, 150, 101420
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PINK: physical-informed machine learning for lattice thermal conductivity

Yujie Liu, Xiaoying Wang, Yuzhou Hao, Xuejie Li, Jun Sun, Turab Lookman, Xiangdong Ding and Zhibin Gao

J. Mater. Inf., 2025, 5
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Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review

Yuelin Wang, Chengquan Zhong, Jingzi Zhang, Jiakai Liu, Kailong Hu, Junjie Chen and Xi Lin

Journal of materials Informatics, 2025, 5
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Recent progress in thermoelectric devices and applications

Pavithra Baskaran and Mani Rajasekar

Chemical Engineering Journal, 2025, 506, 159929
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A review of machine learning approaches for the discovery of thermoelectric materials

Övgü C. Yelgel and Celal Yelgel

Advances in Physics: X, 2025, 10
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Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery

Nikhil K Barua, Sangjoon Lee, Anton O Oliynyk and Holger Kleinke

J. Phys. Energy, 2025, 7, 021001
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Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

Odysseas Sierepeklis and Jacqueline M. Cole

J. Chem. Inf. Model., 2025
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Rationally Design Thermoelectric Materials Based on Ingenious Machine Learning Methods

Yuqing Sun, Xiaorui Chen, Jianzhi Gao, Wenliang Zhu and Minghu Pan

Adv Elect Materials, 2025
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Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

Myeonghun Lee, Taehyun Park and Kyoungmin Min

J. Chem. Inf. Model., 2024, 64, 8770
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Electrodeposition of Thermoelectric Materials

Samuel C. Perry, Samina Akbar, Robert Clarke, Syed Z. H. Shah and Iris Nandhakumar

ChemElectroChem, 2025, 12
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Machine Learning-Driven Inverse Design and Role of Dopant for Tuning Thermoelectric Efficiency

Harpriya Minhas, Milan Kumar Jena, Rahul Kumar Sharma and Biswarup Pathak

ACS Appl. Electron. Mater., 2024
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Advancements in thermoelectric materials: Emerging trends in organic, inorganic systems, and material informatics

Satheesh Soumya, K.S. Fatima, S. Lekshmi, S. Govindan Namboothiri, P.K. Krishnapriya, Varsha Arun Shreya, V.S. Harikrishnan, A. Chithra Mohan, Hyunjin Joh, Janardhanan R. Rani, Varij Panwar, K.M. Sreedhar, Jayakumar P, Shibnath Samanta, Ji Young Jo and Gopinathan Anoop

Journal of Alloys and Compounds, 2025, 1028, 180661
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Predicting High‐Performance Thermoelectric Materials With StarryData2

Nuttawat Parse, Jose Recatala‐Gomez, Ruiming Zhu, Andre KY Low, Kedar Hippalgaonkar, Tomoya Mato, Yukari Katsura and Supree Pinitsoontorn

Advcd Theory and Sims, 2024, 7
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