Publications

AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries

Abdullah Bin Faheem, Zengyu Han, Dongshuang Wu, Haobo Li

Advanced Materials 38 [11], e21975–e21975

DOI: 10.1002/adma.202521975

Empowering modern power systems with thermal energy storage in China: A comprehensive review

Tonglin Jia, Yong Shuai, Fuqiang Wang, Hao Zhang, Dazhi Yang, Boxi Geng, Qingyang Li, Qianying Wu, Ying Xu

Renewable and Sustainable Energy Reviews 235, 116937–116937

DOI: 10.1016/j.rser.2026.116937

LLM-based AI agents for automated extraction of material properties and structural features

Soumya Ghosh, Abhishek Tewari

Computational Materials Science 265, 114521–114521

DOI: 10.1016/j.commatsci.2026.114521

From synthesis to machine learning validation for the thermoelectric Zintl phase Ba _{1− <i>x</i>} Eu _<i>x</i> Zn _{2− <i>y</i>} Al _<i>y</i> Sb ₂ system

Yunjeong Lee, Aziz Ahmed, Jihyun Lee, Kang Min Ok, Tae-Soo You

Journal of Materials Chemistry A 14 [11], 6267–6283

DOI: 10.1039/d5ta08713a

Applicability limits of time-domain impedance spectroscopy for comprehensive thermoelectric characterization under heat leakage conditions

Yasuhiro Hasegawa, Kotoko Kodama

Scientific Reports 16 [1], 6910–6910

DOI: 10.1038/s41598-026-35799-6

Beyond predicted zT: Machine learning strategies for the experimental discovery of thermoelectric materials

Shoeb Athar, Philippe Jund

Artificial Intelligence Chemistry 4 [1], 100113–100113

DOI: 10.1016/j.aichem.2026.100113

From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science

Hieu-Chi Dam, Hiori Kino, Takashi Miyake

Journal of the Physical Society of Japan 95 [4]

DOI: 10.7566/jpsj.95.042001

Topological thermoelectrics: analytical framework, material aspects and machine learning

Deep Mondal, Supriya Ghosal, Sujoy Datta, Debnarayan Jana

Journal of Physics Condensed Matter 38 [14], 143002–143002

DOI: 10.1088/1361-648x/ae56a9

Construction of 3D Penrose tiling from 3D stars via Minkowski addition

Alexander Polyakov

Journal of Physics Condensed Matter 38 [15], 155401–155401

DOI: 10.1088/1361-648x/ae56c7

Reverse design of non-equimolar rare-earth monosilicates: Data-driven and molecular dynamics insights into CMAS corrosion mechanism

Bin Qian, Yu Wang, Keyuan Xu, Jiahao Zu, Jiaqi Liu, Wei Liang, Fangli Yu, Yan Li, Yu Bai

Corrosion Science 268, 113908–113908

DOI: 10.1016/j.corsci.2026.113908

The Strategic Role of 2D Nanomaterials in Grid Modernization

Matthew Glasscott, James D. Burgess, Peter Byrley, Maria Fernanda Campa, Mark H. Griep, Alexander P. Kirk, Stephanie A. Mack, Birgit Schwenzer, Ryan M. Welch, Elisabeth Mansfield

ACS Energy Letters 11 [2], 972–978

DOI: 10.1021/acsenergylett.5c04047

Unraveling the Mg Loss Mechanism and Degradation Kinetics in Thermoelectric n‐Type Mg ₂ Si‐Mg ₂ Sn Solid Solutions

Aryan Sankhla, Eckhard Mueller, Johannes de Boor

Advanced Functional Materials 36 [25]

DOI: 10.1002/adfm.202518107

Advancements in thin film thermoelectric: progress and perspective on materials and devices

Nurfarhana Ahmad Musri, Angella Th’ng, Izzah Hanim Abd Aziz, Ubaidah Syafiq

Frontiers in Electronic Materials 6

DOI: 10.3389/femat.2026.1769839

Searching for high-performance thermoelectric materials via an advanced machine learning framework

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

Cell Reports Physical Science 7 [2], 103093–103093

DOI: 10.1016/j.xcrp.2025.103093

Machine learning discovery of medium-entropy thermoelectric materials with ultralow lattice thermal conductivity

Hangyu Zhang, Xianhua Nie, Xinyi Zhang, Ruiqi Wang, Shuai Deng, Li Zhao

Journal of Materials Chemistry A 14 [22], 13735–13751

DOI: 10.1039/d5ta09114d

Machine-learning prediction of thermoelectric properties using high-entropy descriptors

Novana Hutasoit

Materials Letters 411, 140314–140314

DOI: 10.1016/j.matlet.2026.140314

Systematically Verified Experimental Thermoelectric Dataset For Data-driven Approaches

Leng Ze Tang, Layla Purdy, Trupti Mohanty, Leonard W. T. Ng, Taylor D. Sparks

Integrating materials and manufacturing innovation 15 [1], 181–193

DOI: 10.1007/s40192-026-00446-5

Interpretable machine learning for thermoelectric materials design with Kolmogorov–Arnold networks

Marco Fronzi, Michael J. Ford, Kamal Singh Nayal, Olexandr Isayev, Catherine Stampfl

Scientific Reports 16 [1]

DOI: 10.1038/s41598-026-44723-x

Bridging Machine Learning and Zintl Phase Thermoelectric Materials: The Ca _{9– <i>x</i>} Eu _<i>x</i> Zn _{4.5– <i>y</i>} Cu _<i>y</i> Sb ₉ System

Juwon Lee, Juwon Lee, Aziz Ahmed, Siyeon Kim, Jihyun Lee, Jihyun Lee, Yurij Mozharivskyj, Kang Min Ok, Junsu Lee, Junsu Lee, Tae-Soo You

Chemistry of Materials

DOI: 10.1021/acs.chemmater.6c00656

AI-assisted enhancement of thermoelectric properties in the Yb _{14− <i>x</i>} Ca _<i>x</i> Mn _{1− <i>y</i>} AlySb ₁₁ Zintl system

Junsu Lee, Aziz Ahmed, Tae-Soo You

Journal of Materials Chemistry A 14 [27], 17095–17106

DOI: 10.1039/d6ta00722h

Magnetoelectric properties of nanostructured extruded samples of solid solution Bi _0.85 Sb _0.15 doped with tellurium

M.M. Tagiyev, Kh. F. Aliyeva

Molecular Crystals and Liquid Crystals 770 [7], 1199–1213

DOI: 10.1080/15421406.2026.2651923

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Chenguang Fu, Mouyang Cheng, Nguyen Tuan Hung, Eunbi Rha, Zhantao Chen, Ryotaro Okabe, Denisse Córdova Carrizales, Manasi Mandal, Yongqiang Cheng, Mingda Li

Advanced Materials 37 [35], e2505642–e2505642

DOI: 10.1002/adma.202505642

Decoupled charge and heat transport in Fe2VAl composite thermoelectrics with topological-insulating grain boundary networks

Fabian Garmroudi, Illia Serhiienko, Michael Parzer, Sanyukta Ghosh, Paweł Ziółkowski, Gregor Oppitz, Nguyễn Duy Hiếu, Cédric Bourgès, Yuya Hattori, Alexander Riss, Sebastian Steyrer, Gerda Rogl, P. Rogl, Erhard Schafler, Naoyuki Kawamoto, Eckhard Müller, E. Bauer, Johannes de Boor, Takao Mori

Nature Communications 16 [1], 2976–2976

DOI: 10.1038/s41467-025-57250-6

Machine Learning Enhanced Self‐Charging Power Sources

Rui Gu, Wei Liang, Nuo Xu, Yao Xiong, Qijun Sun, Zhong Lin Wang

Advanced Functional Materials 35 [40]

DOI: 10.1002/adfm.202505719

Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery

Haneen Alzamer, Russlan Jaafreh, Jung-Gu Kim, Kotiba Hamad

Crystals 15 [2], 114–114

DOI: 10.3390/cryst15020114

Disorder by design: high-entropy oxides as next generation thermoelectric materials

Subhra Sourav Jana, Ritwik Banerjee, Tanmoy Maiti

Journal of Materials Chemistry A 13 [33], 27050–27068

DOI: 10.1039/d5ta02713f

Revisiting Pisarenko’s formula: Effective mass estimation, thermopower–conductivity relation, and maximum power factor prediction

Andrei Novitskii, Takao Mori

Materials Today Physics 58, 101845–101845

DOI: 10.1016/j.mtphys.2025.101845

Screening thermoelectric materials for high-output performance in wearable electronics

Xinjie Yuan, Pengfei Qiu, Chuanyao Sun, Shiqi Yang, Yi Wu, Yumeng Wang, Ming Gu, Lidong Chen, Xun Shi

Energy & Environmental Science 18 [11], 5416–5423

DOI: 10.1039/d5ee00216h

Large language model-driven database for thermoelectric materials

Suman Itani, Yibo Zhang, Jiadong Zang

Computational Materials Science 253, 113855–113855

DOI: 10.1016/j.commatsci.2025.113855

Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions

Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida

npj Computational Materials 11 [1]

DOI: 10.1038/s41524-025-01606-5

Advancements in thermoelectric materials: Emerging trends in organic, inorganic systems, and material informatics

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

Journal of Alloys and Compounds 1028, 180661–180661

DOI: 10.1016/j.jallcom.2025.180661

Integrating photothermal CuS-nanowire meshes with thermoelectric modules for efficient solar energy conversion

Magnolia Pak, Subon Hwang, Hye-Ryeon Jang, Hyunju Kim, Min Gyu Lee, Younghun Kim

Solar Energy 296, 113594–113594

DOI: 10.1016/j.solener.2025.113594

Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery

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

Journal of Physics Energy 7 [2], 021001–021001

DOI: 10.1088/2515-7655/adba87

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

Junsoo Park, Alex M. Ganose, Yi Xia

Applied Physics Reviews 12 [1]

DOI: 10.1063/5.0241645

Band degeneracy and convergence in high performing thermoelectric materials

Dezhuang Ji, Natnael F. Haile, Xuan Li, Baosong Li, Moh’d Rezeq, W.J. Cantwell, Lianxi Zheng

Applied Energy 404, 127182–127182

DOI: 10.1016/j.apenergy.2025.127182

Highly Deformable Van der Waals Chalcogenides with Superior Thermoelectric Performance from Interpretable Machine Learning

Qi Ren, Bonan Zhu, Gang Tang, Jiawang Hong

Small 21 [12], e2412745–e2412745

DOI: 10.1002/smll.202412745

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Rafael Resende Assis Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, L. H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins

Advanced Functional Materials 36 [13]

DOI: 10.1002/adfm.202513924

Thermoelectric algebra made simple for thermoelectric generator module performance prediction under constant Seebeck coefficient approximation

Byungki Ryu, Jaywan Chung, SuDong Park

Journal of Applied Physics 137 [5]

DOI: 10.1063/5.0242991

Advances in oxide thermoelectric materials: strategies, applications and beyond

Qing Wang, Zhifang Zhou, Chang Liu, Yunpeng Zheng, Zongmo Shi, Bin Wei, Wenyu Zhang, Ce‐Wen Nan, Yuanhua Lin

Chemical Society Reviews 55 [1], 358–399

DOI: 10.1039/d5cs01078k

Energy-GNoME: A living database of selected materials for energy applications

Paolo De Angelis, Giulio Barletta, Giovanni Trezza, Pietro Asinari, Eliodoro Chiavazzo

Energy and AI 22, 100605–100605

DOI: 10.1016/j.egyai.2025.100605

SeeBand: a highly efficient, interactive tool for analyzing electronic transport data

Michael Parzer, Alexander Riss, Fabian Garmroudi, Johannes de Boor, Takao Mori, E. Bauer

npj Computational Materials 11 [1]

DOI: 10.1038/s41524-025-01645-y

Thermoelectric Performance Predictions Combining Experiments with Multi‐Band Modelling

Bharti Agrawal, Titas Dasgupta

Advanced Theory and Simulations 8 [5]

DOI: 10.1002/adts.202401222

Revisiting hydrogen trapping in Mg32(Al, Zn)49 approximant crystal: Influence of chemical disorder

Kazuyuki Shimizu, Masatake Yamaguchi, Satoshi Akamaru, K. Nishimura, Rion Abe, Taisuke Sasaki, Yafei Wang, Hiroyuki Toda

Scripta Materialia 265, 116730–116730

DOI: 10.1016/j.scriptamat.2025.116730

The role of lattice defects for structural, mechanical, and physical properties of HPT processed p-type skutterudite DD0.7Fe3CoSb12

Gerda Rogl, Vilma Buršíková, V.V. Romaka, Peter Cengeri, Jiřı́ Buršı́k, Erhard Schafler, M. Zehetbauer, P. Rogl

Acta Materialia 296, 121290–121290

DOI: 10.1016/j.actamat.2025.121290

Large-scale database analysis of anomalous thermal conductivity of quasicrystals and its application to thermal diodes

Takashi Kurono, Jinjia Zhang, Yasushi Kamimura, Keiichi Edagawa

Science and Technology of Advanced Materials Methods 5 [1]

DOI: 10.1080/27660400.2024.2444866

Tetrahedrite Nanocomposites for High Performance Thermoelectrics

Rodrigo Coelho, Duarte Moço, A.I. de Sá, Paulo P. da Luz, Filipe Neves, Maria de Fátima Cerqueira, Elsa B. Lopes, F. P. Brito, Panagiotis Mangelis, Theodora Kyratsi, A.P. Gonçalves

Nanomaterials 15 [5], 351–351

DOI: 10.3390/nano15050351

Advanced porous thermoelectric materials: Design, construction, and application

Ruoyan Li, Bangzhi Ge, Chongjian Zhou

Nano Research 18 [4], 94907308–94907308

DOI: 10.26599/nr.2025.94907308

Solar-assisted waste heat utilisation coupled with thermal energy storage for electricity production: technical and economic assessment

Ilia Skorniakov, Timo Laukkanen, Behnam Talebjedi, Henrik Holmberg

Applied Thermal Engineering 278, 127417–127417

DOI: 10.1016/j.applthermaleng.2025.127417

Rationally Design Thermoelectric Materials Based on Ingenious Machine Learning Methods

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

Advanced Electronic Materials 11 [14]

DOI: 10.1002/aelm.202500210

High throughput and machine learning approaches for thermoelectric materials

Eric S. Toberer, Andrew C. Novick, Elif Ertekin

MRS Bulletin 50 [8], 966–977

DOI: 10.1557/s43577-025-00956-1

Metallography of Quasicrystals in Al-Alloys

Tonica Bončina, Franc Zupanič

Materials 18 [19], 4575–4575

DOI: 10.3390/ma18194575

Few-shot learning of initial coulombic efficiency using adaptive combination kernel deep Gaussian process regression

Hai Guo, Hongcheng Zhang, Xiaofeng Lv, Xiaoxu Liu, Tianyi Ji

Journal of Energy Storage 128, 117238–117238

DOI: 10.1016/j.est.2025.117238

Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half-Heusler Alloys

Vipin Kurian Elavunkel, Prahallad Padhan

ACS Applied Energy Materials 8 [20], 15241–15257

DOI: 10.1021/acsaem.5c02223

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

Federico Ottomano, John Y. Goulermas, Vladimir V. Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa Partida Manzanera, Emma Poole, Matthew S. Dyer, John B. Claridge, Jonathan Alaria, Luke M. Daniels, Su Varma, D E Rimmer, Kevin D. Sanderson, Matthew J. Rosseinsky

Digital Discovery 4 [7], 1794–1811

DOI: 10.1039/d5dd00010f

Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data

Kentaro Fuku, Takefumi Yoshida

Journal of Chemical Information and Modeling 65 [7], 3176–3185

DOI: 10.1021/acs.jcim.4c01644

Mathematical and simulation modeling of photovoltaic systems utilizing thermoelectric modules for effective thermal management

Muhammad Sohaib Tahir, Xue Dong, Muhammad Mansoor Khan

Results in Engineering 27, 106344

DOI: 10.1016/j.rineng.2025.106344

Multi-factor design of radioisotope thermoelectric generators: an extensive review

Yongjia Wu, Zhaojun Chen, Peng Zhang, Wenqian Liu, Dongcheng Liu, Tingzhen Ming

Applied Thermal Engineering 280, 128045

DOI: 10.1016/j.applthermaleng.2025.128045

A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra

Md Tohidul Islam, Scott Broderick

Crystals 15 [5], 390–390

DOI: 10.3390/cryst15050390

Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials

Shoeb Athar, Adrien Mecibah, Philippe Jund

Materials Today Physics 59, 101948–101948

DOI: 10.1016/j.mtphys.2025.101948

Enhancing thermoelectric performance in 1D tellurium nanostructures via controlled Cu and Sn ion Bi-doping sequences

Jaehee Yeom, In Ho Kim, Eun Joo An, Hyeok‐jin Kwon, Yong Jin Jeong

Ceramics International 51 [17], 23772–23778

DOI: 10.1016/j.ceramint.2025.03.066

Thermal and Interfacial Stability of PPS-Fabricated Segmented Skutterudite Legs for Thermoelectric Applications

Mirosław J. Kruszewski

Materials 18 [13], 2923–2923

DOI: 10.3390/ma18132923

Nonlinear corrections to the thermoelectric efficiency of a nanoscale device

Ralf Hartig, I. Grosu, I. Ţifrea

Physica E Low-dimensional Systems and Nanostructures 175, 116383–116383

DOI: 10.1016/j.physe.2025.116383

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim

Advanced Materials 38 [10], e15054–e15054

DOI: 10.1002/adma.202515054

Emerging opportunities for high-temperature solid-state and gas-cycle heat pumps

Andrej Kitanovski, Katja Klinar, Ercang Luo, Miguel Muñoz Rojo, Vladimir Soldo, Luka Boban, Kaiqi Luo, Rui Yang, Xavier Moya

Nature Energy 10 [12], 1412–1426

DOI: 10.1038/s41560-025-01908-4

Wiedemann–Franz law and thermoelectric inequalities: Effective <i>ZT</i> and single-leg efficiency overestimation

Byungki Ryu, Seunghyun Oh, Wabi Demeke, Jaywan Chung, Jongho Park, Nirma Kumari, Aadil Fayaz Wani, Seunghwa Ryu, SuDong Park

Journal of Applied Physics 137 [11]

DOI: 10.1063/5.0247466

Statistical and artificial intelligence approaches towards the optimization of thermoelectric materials synthesis: a review

Wei‐Hsin Chen, K. Aishwarya, Kripasindhu Sardar

Energy Materials 5 [9]

DOI: 10.20517/energymater.2024.311

Development of a Next-Generation Thermopile Detector for Cold-Body Space Applications

Byeong Ho Eom, Brian Pepper, Ricardo Braga Nogueira Branco, B. T. Greenhagen, Matthew Kenyon

IEEE Sensors Journal 25 [19], 35819–35827

DOI: 10.1109/jsen.2025.3595487

MACHINE LEARNING IN NANOSCALE THERMAL TRANSPORT

Yuhan Liu, Sobin Alosious, Jiahang Zhou, Meng Jiang, Tengfei Luo

Annual Reviews of Heat Transfer 28 [1], 173–214

DOI: 10.1615/annualrevheattransfer.2025060156

Combined Machine Learning and Experimental Study for the Zintl Phase Thermoelectric Ba _{1− <i>x</i>} Sr _<i>x</i> Zn _{2− <i>y</i>} Cd _<i>y</i> Sb ₂ System

Junsu Lee, Min Kyoung Kim, Aziz Ahmed, Yurij Mozharivskyj, Tae‐Soo You

Zeitschrift für anorganische und allgemeine Chemie 651 [12]

DOI: 10.1002/zaac.202500055

A review of machine learning approaches for the discovery of thermoelectric materials

Övgü Ceyda Yelgel, Celal Yelgel

Advances in Physics X 10 [1]

DOI: 10.1080/23746149.2025.2536269

Cross-modality material embedding loss for transferring knowledge between heterogeneous material descriptors

Gyoung S. Na

npj Computational Materials 11 [1]

DOI: 10.1038/s41524-025-01723-1

Explainable machine learning-guided design of high-performance thermoelectric materials

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

Journal of Alloys and Compounds 1037, 182164–182164

DOI: 10.1016/j.jallcom.2025.182164

A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery

Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai

APL Machine Learning 3 [3]

DOI: 10.1063/5.0274812

Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd₁₀RuCd₃, a Neutron Absorption Material

Brook Xhabrahimi, Emil I. Jaffal, Danila Shiryaev, Nikhil K. Barua, Madison Donohoe, Natalia Pozdnyakova, Mariam Ismail, Balaranjan Selvaratnam, Ehsan Niknam, Holger Kleinke, Anton O. Oliynyk

Journal of the American Chemical Society 147 [40], 36589–36603

DOI: 10.1021/jacs.5c11646

Innovation in thermoelectric materials: From fundamental physics to practical applications

Sepideh Akhbarifar, Mona Zebarjadi

MRS Bulletin 50 [8], 894–901

DOI: 10.1557/s43577-025-00949-0

Machine learning prediction of dielectric constant in NBT–KBT composites via frequency–temperature response

Sonia Bhandari, Aarti Bhandari

Solid State Communications 409, 116306–116306

DOI: 10.1016/j.ssc.2025.116306

Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects

Guangcun Shan, Zejian Ding, Liujiang Xi, Hongbin Zhao, Jiliang Zhang, Feng Xu

Rare Metals 44 [12], 9747–9762

DOI: 10.1007/s12598-025-03617-z

Reference compositions for bismuth telluride thermoelectric materials for low-temperature power generation

Nirma Kumari, Jaywan Chung, Seunghyun Oh, Jeongin Jang, Jongho Park, Ji Hui Son, SuDong Park, Byungki Ryu

Science and Technology of Advanced Materials Methods 5 [1]

DOI: 10.1080/27660400.2025.2586299

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Tano Kim Kender, Marco Corrias, Cesare Franchini

Advanced Intelligent Discovery

DOI: 10.1002/aidi.202500043

Excellent Thermoelectric Performance of Monolayer ZrTe ₅ and Its Near‐Room Temperature Modulation by Uniaxial Strain

Rongman Gao, Liangyu Li, Zhenyu Ding, Zhiwei Wang, Miao Li, Yuqiang Liu, Gang Wu, Xiaoping Yang

Advanced Quantum Technologies 8 [11]

DOI: 10.1002/qute.202500187

Study the characterization and thermoelectrical properties of the polyvinyl alcohol / Polyaniline polymer blend thick films

M. Morad, Magdy S. Abo Ghazala, M.G. El-Shaarawy, M. E. Gouda, T. Y. Elrasasi

Discover Nano 20 [1], 195–195

DOI: 10.1186/s11671-025-04267-x

Few-Shot Learning of Initial Coulombic Efficiency Using Adaptive Combination Kernel Deep Gaussian Process Regression

Hai Guo, Hongcheng Zhang, Xiaofeng Lv, Xiaoxu Liu, Tianyi Ji

SSRN Electronic Journal

DOI: 10.2139/ssrn.5082038

Ab Initio Electronic and Thermoelectric Calculations of Atomically Thick Nanofilms

Rajnarayan De, Swarupananda Pradhan

physica status solidi (b) 262 [6]

DOI: 10.1002/pssb.202400537

Machine Learning Predictions of Thermopower for Thermoelectric Material Screening

Nikhil K. Barua, Holger Kleinke

ACS Applied Energy Materials 8 [21], 16110–16121

DOI: 10.1021/acsaem.5c02609

Exploring materials data through collaboration: 2024 KRICT ChemDX Hackathon

Su‐Hyun Yoo, Andre K. Y. Low, Jose Recatala‐Gomez, Harikrishna Sahu, Chiho Kim, Joonyoung F. Joung, Hoje Chun, Katerina A. Christofidou, Joshua Berry, Michail Minotakis, Kisung Kang, K. Kim, Gaheun Shin, Hyunwoo Jang, Sanghyuk Lee, Minkyu Park, B. S. Kim, Kihyun Shin, Jungho Shin, Aloysius Soon, Joshua Schrier, Woosun Jang

Journal of Materials Informatics 5 [4]

DOI: 10.20517/jmi.2025.65

Pyroelectric and thermoelectric biomaterials and nanomedicine

Shuangshuang Wang, Xiaoshuang Liu, Wei Feng, Xinyue Dai, Yu Chen

Coordination Chemistry Reviews 551, 217442–217442

DOI: 10.1016/j.ccr.2025.217442

An Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of Thermoelectric Materials

Mingji Liu, Wenzhao Li

Journal of Materials Engineering and Performance 35 [15], 14765–14780

DOI: 10.1007/s11665-025-12690-5

Machine learning–driven thermoelectric materials: Review on prediction, optimization, and discovery

Xinmei Zhang, Xingxing Wang, Wei Wang, Zhipeng Yuan, Jin Peng, J M Shi, Peng He, Yunfeng Chang

Journal of Alloys and Compounds 1050, 185711–185711

DOI: 10.1016/j.jallcom.2025.185711

Conditional Inverse Design Solvers for Autonomous Manufacturing Systems with System Constraints

Gyoung S. Na

, 51–58

DOI: 10.1109/aiot66900.2025.00019

Uncertainty analysis of microscopic parameters obtained from the single parabolic band modeling of thermoelectric materials

Harshita Naithani, Eckhard Müller, Johannes de Boor

Physical Review Applied 24 [4]

DOI: 10.1103/lqtf-rvlm

Optimizing data extraction from materials science literature: a study of tools using large language models

Wenkai Ning, Musen Li, Jeffrey R. Reimers, Rika Kobayashi

Digital Discovery 5 [2], 698–715

DOI: 10.1039/d5dd00482a

Advancements in thermoelectric materials: A comprehensive review

Syed Irfan, Yan Zhiyuan, Sadaf Bashir Khan

Materials Science for Energy Technologies 7, 349–373

DOI: 10.1016/j.mset.2024.06.002

Leveraging language representation for materials exploration and discovery

Jiaxing Qu, Yuxuan Richard Xie, Kamil Ciesielski, Claire E. Porter, Eric S. Toberer, Elif Ertekin

npj Computational Materials 10 [1]

DOI: 10.1038/s41524-024-01231-8

Machine learning based feature engineering for thermoelectric materials by design

U. S. Vaitesswar, Daniil Bash, Tan Huang, Jose Recatala‐Gomez, Tianqi Deng, Shuo‐Wang Yang, Xiaonan Wang, Kedar Hippalgaonkar

Digital Discovery 3 [1], 210–220

DOI: 10.1039/d3dd00131h

Development and application of Few-shot learning methods in materials science under data scarcity

Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

Journal of Materials Chemistry A 12 [44], 30249–30268

DOI: 10.1039/d4ta06452f

Dealing with the big data challenges in AI for thermoelectric materials

Xue Jia, Alex Aziz, Yusuke Hashimoto, Hao Li

Science China Materials 67 [4], 1173–1182

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