High-throughput screening of amorphous polymers with high intrinsic thermal conductivity via automated physical feature engineering

Xiang Huang, Shengluo Ma, Yunwen Wu, Chaoying Wan, C. Y. Zhao, Hong Wang and Shenghong Ju

J. Mater. Chem. A, 2023, 11, 20539
DOI: 10.1039/D3TA03370H

Ultralow thermal conductivity and high thermoelectric performance induced by multiscale lattice defects in Cu-doped BST alloys

Yaohui Liu, Yu Tang, Yonggui Tao, Ying Zhang, Lanxian Shen, Wen Ge and Shukang Deng

CrystEngComm, 2024, 26, 100
DOI: 10.1039/D3CE00951C

Digital materials ecosystem: from databases to AI agents for autonomous discovery

Di Zhang, Xue Jia, Yuhang Wang, Heng Liu, Qian Wang, Seong-Hoon Jang, Daksh Shah, Songbo Ye, Hung Ba Tran and Hao Li

Chem. Sci., 2026, 17, 5782
DOI: 10.1039/D5SC09229A

Phonon anharmonicity in binary chalcogenides for efficient energy harvesting

P. Parajuli, S. Bhattacharya, R. Rao and A. M. Rao

Mater. Horiz., 2022, 9, 1602
DOI: 10.1039/D1MH01601F

Machine learning-assisted screening of intrinsic rattling compounds with large atomic displacement

Kunpeng Yuan, Zhaoxuan Feng, Xiaoliang Zhang and Dawei Tang

J. Mater. Chem. C, 2025, 13, 2414
DOI: 10.1039/D4TC03756A

Leveraging an all-fixed transfer framework to predict the interpretable formation energy of MXenes with hybrid terminals

Zihao Song, Xiaobin Niu and Haiyuan Chen

Phys. Chem. Chem. Phys., 2024, 26, 14847
DOI: 10.1039/D4CP00386A

CrysGraphFormer: an equivariant graph transformer for prediction of lattice thermal conductivity with interpretability

Zhengyu Sun, Weiwei Sun, Shaohan Li, Zening Yang, Mutian Zhang, Yang Yang, Huayun Geng and Jin Yu

J. Mater. Chem. A, 2024, 12, 30707
DOI: 10.1039/D4TA04495A

High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models

Joshua Ojih, Chen Shen, Alejandro Rodriguez, Hongbin Zhang, Kamal Choudhary and Ming Hu

J. Mater. Chem. A, 2023, 11, 24169
DOI: 10.1039/D3TA04874H

Contrastive representation learning of inorganic materials to overcome lack of training datasets

Gyoung S. Na and Hyun Woo Kim

Chem. Commun., 2022, 58, 6729
DOI: 10.1039/D2CC01764D

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu and Ming Hu

J. Mater. Chem. A, 2024, 12, 8502
DOI: 10.1039/D3TA06190F

Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature

Zihe Li, Mengke Li, Yufeng Luo, Haibin Cao, Huijun Liu and Ying Fang

Digital Discovery, 2025, 4, 204
DOI: 10.1039/D4DD00286E

Predicting lattice thermal conductivity via machine learning: a mini review

Yufeng Luo, Mengke Li, Hongmei Yuan, Huijun Liu and Ying Fang

npj Comput Mater, 2023, 9
DOI: 10.1038/s41524-023-00964-2

Accelerating thermal conductivity prediction through machine-learning: Two orders of magnitude reduction in phonon-phonon scattering rates calculation

Yagyank Srivastava and Ankit Jain

Materials Today Physics, 2024, 41, 101345
DOI: 10.1016/j.mtphys.2024.101345

Figure of merit enhancement in thermoelectric materials based on γ‐Ln0.8Yb0.2S1.5‐y (Ln = Gd, Dy) solid solutions

Aleksandr V Sotnikov, Mikhail M Syrokvashin, Vladimir V Bakovets, Irina Yu Filatova, Evgeniy V Korotaev, Alibek Sh Agazhanov and Dmitrii A Samoshkin

J Am Ceram Soc., 2022, 105, 2813
DOI: 10.1111/jace.18292

Topological thermoelectrics: analytical framework, material aspects and machine learning

Deep Mondal, Supriya Ghosal, Sujoy Datta and Debnarayan Jana

J. Phys.: Condens. Matter, 2026, 38, 143002
DOI: 10.1088/1361-648X/ae56a9

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

Jiahao Xie, Yansong Zhou, Muhammad Faizan, Zewei Li, Tianshu Li, Yuhao Fu, Xinjiang Wang and Lijun Zhang

Nat Comput Sci, 2024, 4, 322
DOI: 10.1038/s43588-024-00632-5

High ZT Value and Ultralow Lattice Thermal Conductivity Induced by Edge Dislocations Coupled with the Superlattice Structure in Ag-Doped BST Alloy

Yaohui Liu, Yu Tang, Yonggui Tao, Ying Zhang, Lanxian Shen, Wen Ge and Shukang Deng

Crystal Growth & Design, 2023, 23, 8221
DOI: 10.1021/acs.cgd.3c00935

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
DOI: 10.20517/jmi.2024.86

Distribution of Phases and Short-Range Order Distortion in SmS@Y2O2S and Y2O2S@SmS Core–Shell Nanostructures

A. V. Sotnikov, V. V. Bakovets, M. M. Syrokvashin and I. Yu. Filatova

Inorg Mater, 2022, 58, 1105
DOI: 10.1134/S0020168522100132

Accelerating Discovery of Infrared Nonlinear Optical Materials with High Lattice Thermal Conductivity: Combining Machine Learning and First‐Principles Calculations

Qingyu Liu, Ran An, Chenxu Li, Dongdong Chu, Wang Zhao, Shilie Pan and Zhihua Yang

Advanced Optical Materials, 2025, 13
DOI: 10.1002/adom.202403292

Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

Ming Hu

Journal of Applied Physics, 2024, 135
DOI: 10.1063/5.0201778

High throughput and machine learning approaches for thermoelectric materials

Eric S. Toberer, Andrew Novick and Elif Ertekin

MRS Bulletin, 2025, 50, 966
DOI: 10.1557/s43577-025-00956-1

Ultralow and glass-like lattice thermal conductivity in crystalline BaAg2Te2: Strong fourth-order anharmonicity and crucial diffusive thermal transport

Zezhu Zeng, Cunzhi Zhang, Hulei Yu, Wen Li, Yanzhong Pei and Yue Chen

Materials Today Physics, 2021, 21, 100487
DOI: 10.1016/j.mtphys.2021.100487

Synergistic regulation of sulfur-group elements doping and strain on the thermoelectric performance of innovative material Er2Te3

Yuhong Xia, Zhenqing Yang, Lulu Zhou, Yan Sun, Fei Wang, Bingqing Luan, Chang Liu, Feiyu Liu, Mingyan Shao and Songqing Zhao

Journal of Alloys and Compounds, 2024, 984, 173960
DOI: 10.1016/j.jallcom.2024.173960

Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach

Alejandro Rodriguez, Changpeng Lin, Chen Shen, Kunpeng Yuan, Mohammed Al-Fahdi, Xiaoliang Zhang, Hongbin Zhang and Ming Hu

Commun Mater, 2023, 4
DOI: 10.1038/s43246-023-00390-3

High‐Throughput Strategies in the Discovery of Thermoelectric Materials

Tingting Deng, Pengfei Qiu, Tingwei Yin, Ze Li, Jiong Yang, Tianran Wei and Xun Shi

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202311278

Charge Distribution in Layered Lanthanide-Doped CuCr0.99Ln0.01S2 (Ln = Pr–Tb) Thermoelectric Materials

Evgeniy V. Korotaev, Mikhail M. Syrokvashin, Irina Yu. Filatova, Aleksandr V. Sotnikov and Alexandr V. Kalinkin

Materials, 2022, 15, 8747
DOI: 10.3390/ma15248747

Insights into Low Thermal Conductivity in Inorganic Materials for Thermoelectrics

Tanmoy Ghosh, Moinak Dutta, Debattam Sarkar and Kanishka Biswas

J. Am. Chem. Soc., 2022, 144, 10099
DOI: 10.1021/jacs.2c02017

Metallic grain boundary resistance in the high temperature thermoelectric La3Te4

Duncan Zavanelli, Eleonora Isotta, Ruben Bueno Villoro, Siyuan Zhang, Christina Scheu, Sabah Bux and G.Jeffrey Snyder

Journal of Materiomics, 2025, 11, 101116
DOI: 10.1016/j.jmat.2025.101116

Modulating the thermoelectric transport properties of the novel material Er2Te3 via strain

Yuhong Xia, Zhenqing Yang, Lulu Zhou and Changjin Shao

Int J of Quantum Chemistry, 2024, 124
DOI: 10.1002/qua.27290

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Jinlong Hu, Yuting Zuo, Yuzhou Hao, Guoyu Shu, Yang Wang, Minxuan Feng, Xuejie Li, Xiaoying Wang, Jun Sun, Xiangdong Ding, Zhibin Gao, Guimei Zhu and Baowen Li

Chinese Phys. B, 2023, 32, 046301
DOI: 10.1088/1674-1056/acbaf4

Tuning phonon transport via complex nanostructure design using an X+EA hybrid optimization strategy

Shengluo Ma, Nanyu Wang and Shenghong Ju

Phys. Rev. Materials, 2025, 9
DOI: 10.1103/PhysRevMaterials.9.043801

Origin of Intrinsically Low Lattice Thermal Conductivity in Solids

Yu Wu, Anqi Huang, Linxuan Ji, Jialin Ji, Yimin Ding and Liujiang Zhou

J. Phys. Chem. Lett., 2024, 15, 11525
DOI: 10.1021/acs.jpclett.4c02770

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Alejandro Rodriguez, Changpeng Lin, Hongao Yang, Mohammed Al-Fahdi, Chen Shen, Kamal Choudhary, Yong Zhao, Jianjun Hu, Bingyang Cao, Hongbin Zhang and Ming Hu

npj Comput Mater, 2023, 9
DOI: 10.1038/s41524-023-00974-0

Machine learning reconstruction of depth-dependent thermal conductivity profile from pump–probe thermoreflectance signals

Zeyu Xiang, Yu Pang, Xin Qian and Ronggui Yang

Applied Physics Letters, 2023, 122
DOI: 10.1063/5.0138060

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong, Masatoshi Hanai, Zeyu Wang, Michimasa Morita, Zhiting Tian, Ming Hu, Xiulin Ruan, Ryo Yoshida, Toyotaro Suzumura, Lucas Lindsay, Alan J. H. McGaughey, Tengfei Luo, Kedar Hippalgaonkar and Junichiro Shiomi

npj Comput Mater, 2026, 12
DOI: 10.1038/s41524-026-02033-w

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 and YongJoo Kim

Advanced Materials, 2026, 38
DOI: 10.1002/adma.202515054

A review on machine learning-guided design of energy materials

Seongmin Kim, Jiaxin Xu, Wenjie Shang, Zhihao Xu, Eungkyu Lee and Tengfei Luo

Prog. Energy, 2024, 6, 042005
DOI: 10.1088/2516-1083/ad7220

Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning

Xinpeng Zhao, Haiyou Huang, Yanjing Su, Lijie Qiao and Yu Yan

Materials & Design, 2025, 255, 114202
DOI: 10.1016/j.matdes.2025.114202

Analytical approach to structural chemistry origins of mechanical, acoustical and thermal properties

Zhiwei Chen, Wei Liu, Bing Shan and Yanzhong Pei

National Science Review, 2024, 11
DOI: 10.1093/nsr/nwae269

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

Övgü C. Yelgel and Celal Yelgel

Advances in Physics: X, 2025, 10
DOI: 10.1080/23746149.2025.2536269

Leveraging low-fidelity data to improve machine learning of sparse high-fidelity thermal conductivity data via transfer learning

Z. Liu, M. Jiang and T. Luo

Materials Today Physics, 2022, 28, 100868
DOI: 10.1016/j.mtphys.2022.100868

A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons

Jiayi Guo, Chunfeng Cui, Tao Ouyang, Juexian Cao and Xiaolin Wei

Sci Rep, 2025, 15
DOI: 10.1038/s41598-024-84074-z

Accelerating prediction of phonon thermal conductivity by an order of magnitude through machine learning assisted extraction of anharmonic force constants

Yagyank Srivastava and Ankit Jain

Phys. Rev. B, 2024, 110
DOI: 10.1103/PhysRevB.110.165202

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao-Horn and Jeffrey C. Grossman

JACS Au, 2022, 2, 1964
DOI: 10.1021/jacsau.2c00235

Advances, optical and electronic applications of functional materials based on rare earth sulfide semiconductors

Wenhao Xu, Gongxun Bai, Er Pan, Denghao Li, Junjie Zhang and Shiqing Xu

Materials & Design, 2024, 238, 112698
DOI: 10.1016/j.matdes.2024.112698

Research on the performance optimization of Y2Te3 thermoelectric materials based on strain engineering

Xinyue Zhang, Zhenqing Yang, Wansong Zhang, Changjin Shao, Yuhong Xia, Haoru Zhang and Chundan Lin

Computational and Theoretical Chemistry, 2026, 1257, 115677
DOI: 10.1016/j.comptc.2026.115677

Grain Boundary Engineering Enhances the Thermoelectric Properties of Y2Te3

Jamil Ur Rahman, Shuping Guo, Nicolás Pérez, Kyuseon Jang, Chanwon Jung, Pingjun Ying, Christina Scheu, Duncan Zavanelli, Siyuan Zhang, Andrei Sotnikov, Gerald Jeffrey Snyder, Jeroen van den Brink, Kornelius Nielsch and Ran He

Advanced Energy Materials, 2025, 15
DOI: 10.1002/aenm.202404243

Magnesium-based thermoelectric materials and modules for low-temperature applications (below 300°C)

Ran He, Pingjun Ying, Shuo Chen, Zhifeng Ren and Kornelius Nielsch

MRS Bulletin, 2025, 50, 956
DOI: 10.1557/s43577-025-00939-2

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Zhilong Wang, An Chen, Kehao Tao, Yanqiang Han and Jinjin Li

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202306733

Anharmonicity in Thermal Insulators: An Analysis from First Principles

Florian Knoop, Thomas A. R. Purcell, Matthias Scheffler and Christian Carbogno

Phys. Rev. Lett., 2023, 130
DOI: 10.1103/PhysRevLett.130.236301

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael A. Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A. Johnson, Yang Shao-Horn and Jeffrey C. Grossman

Nat Commun, 2022, 13
DOI: 10.1038/s41467-022-30994-1

Machine learning for predicting thermal transport properties of solids

Xin Qian and Ronggui Yang

Materials Science and Engineering: R: Reports, 2021, 146, 100642
DOI: 10.1016/j.mser.2021.100642

Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence

Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli and Christian Carbogno

npj Comput Mater, 2023, 9
DOI: 10.1038/s41524-023-01063-y

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Konstantinos Mira, Francesca Bugiotti and Tatiana Morosuk

Energies, 2023, 16, 7773
DOI: 10.3390/en16237773

High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning

Stephen R. Xie, Shreyas J. Honrao and John W. Lawson

Chem. Mater., 2024, 36, 9320
DOI: 10.1021/acs.chemmater.3c02841

Accelerated discovery of extreme lattice thermal conductivity by crystal graph attention networks and chemical bonding

Mohammed Al-Fahdi, Riccardo Rurali, Jianjun Hu, Christopher Wolverton and Ming Hu

npj Comput Mater, 2025, 12
DOI: 10.1038/s41524-025-01871-4

Evaluating the Energy Conversion Performance of Rare-Earth-Based Cu3RETe3 Thermoelectric Materials with Strong Electronic Correlations

Oleksandr Cherniushok, Oleksandr V. Smitiukh, Dariusz Wieczorek, Oleg V. Marchuk, Bartlomiej Wiendlocha, Taras Parashchuk and Krzysztof T. Wojciechowski

Chem. Mater., 2025, 37, 7377
DOI: 10.1021/acs.chemmater.5c01744

Computation and machine learning for materials: Past, present, and future perspectives

Sobin Alosious, Meng Jiang and Tengfei Luo

MRS Bulletin, 2025, 50, 1212
DOI: 10.1557/s43577-025-00959-y

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
DOI: 10.1088/2515-7655/adba87

Virtual node graph neural network for full phonon prediction

Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng and Mingda Li

Nat Comput Sci, 2024, 4, 522
DOI: 10.1038/s43588-024-00661-0

Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

Rasmus Tranås, Ole Martin Løvvik, Oliver Tomic and Kristian Berland

Computational Materials Science, 2022, 202, 110938
DOI: 10.1016/j.commatsci.2021.110938

Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations

Xue Jia, Honghao Yao, Zhijie Yang, Jianyang Shi, Jinxin Yu, Rongpei Shi, Haijun Zhang, Feng Cao, Xi Lin, Jun Mao, Cuiping Wang, Qian Zhang and Xingjun Liu

Applied Physics Letters, 2023, 123
DOI: 10.1063/5.0175233

Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion BatteriesviaData-Driven Approaches

Eunseong Choi, Junho Jo, Wonjin Kim and Kyoungmin Min

ACS Appl. Mater. Interfaces, 2021, 13, 42590
DOI: 10.1021/acsami.1c07999

Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling

Mohammed Al-Fahdi, Changpeng Lin, Chen Shen, Hongbin Zhang and Ming Hu

Materials Today Physics, 2025, 50, 101632
DOI: 10.1016/j.mtphys.2024.101632

Structured illumination with thermal imaging (SI-TI): A dynamically reconfigurable metrology for parallelized thermal transport characterization

Qiye Zheng, Divya Chalise, Mingxin Jia, Yuqiang Zeng, Minxiang Zeng, Mortaza Saeidi-Javash, Ali N. M. Tanvir, Gottlieb Uahengo, Sumanjeet Kaur, Javier E. Garay, Tengfei Luo, Yanliang Zhang, Ravi S. Prasher and Chris Dames

Applied Physics Reviews, 2022, 9
DOI: 10.1063/5.0079842

Comprehensive Screening of Plasma-Facing Materials for Nuclear Fusion

Andrea Fedrigucci, Nicola Marzari and Paolo Ricci

PRX Energy, 2024, 3
DOI: 10.1103/PRXEnergy.3.043002

Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity

Chia-Min Lin, Abishek Khatri, Da Yan and Cheng-Chien Chen

Materials, 2024, 17, 5372
DOI: 10.3390/ma17215372

Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning

Sheng Gong, Shuo Wang, Taishan Zhu, Xi Chen, Zhenze Yang, Markus J. Buehler, Yang Shao-Horn and Jeffrey C. Grossman

JACS Au, 2021, 1, 1904
DOI: 10.1021/jacsau.1c00260

Y2Te3: A New n-Type Thermoelectric Material

Michael Y. Toriyama, Dean Cheikh, Sabah K. Bux, G. Jeffrey Snyder and Prashun Gorai

ACS Appl. Mater. Interfaces, 2022, 14, 43517
DOI: 10.1021/acsami.2c12112

Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

Xue Jia, Yanshuai Deng, Xin Bao, Honghao Yao, Shan Li, Zhou Li, Chen Chen, Xinyu Wang, Jun Mao, Feng Cao, Jiehe Sui, Junwei Wu, Cuiping Wang, Qian Zhang and Xingjun Liu

npj Comput Mater, 2022, 8
DOI: 10.1038/s41524-022-00723-9

Unveiling bonding heterogeneity-driven anharmonicity and ultralow lattice thermal conductivity in NbSe2Br2: A machine learning accelerated discovery

Zihan Dong, Yinglin Guan, Minru Wen and Le Huang

Applied Physics Letters, 2026, 128
DOI: 10.1063/5.0304675

Exploration, Prediction, and Experimental Verification of Structure and Optoelectronic Properties in I2-Eu-IV-X4 (I = Li, Cu, Ag; IV = Si, Ge, Sn; X = S, Se) Chalcogenide Semiconductors

Tianlin Wang, Timothy M. McWhorter, Garrett C. McKeown Wessler, Yi Yao, Ruyi Song, David B. Mitzi and Volker Blum

Chem. Mater., 2024, 36, 340
DOI: 10.1021/acs.chemmater.3c02218

A Critical Review of Machine Learning Techniques on Thermoelectric Materials

Xiangdong Wang, Ye Sheng, Jinyan Ning, Jinyang Xi, Lili Xi, Di Qiu, Jiong Yang and Xuezhi Ke

J. Phys. Chem. Lett., 2023, 14, 1808
DOI: 10.1021/acs.jpclett.2c03073

A Review of Artificial Intelligence-Driven Approaches for Nanoscale Heat Conduction and Radiation

Ziqi Guo, Daniel Carne, Krutarth Khot, Dudong Feng, Guang Lin and Xiulin Ruan

Journal of Computing and Information Science in Engineering, 2025, 25
DOI: 10.1115/1.4070202

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity

Sheng Gong, Keqiang Yan, Tian Xie, Yang Shao-Horn, Rafael Gomez-Bombarelli, Shuiwang Ji and Jeffrey C. Grossman

Sci. Adv., 2023, 9
DOI: 10.1126/sciadv.adi3245

Spin-state regulation by secondary coordination sphere for improved oxygen evolution activity of LaCo1-Ni O3 perovskite

Yao Di, Mengna Chai, Yucheng Lv, Enhui Wang, Tao Yang, Zhi Fang and Xinmei Hou

Ceramics International, 2024, 50, 21937
DOI: 10.1016/j.ceramint.2024.03.306

Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone

Keisuke Makino, Yudai Yamaguchi, Naoto Tanibata, Hayami Takeda, Ryo Kobayashi, Masayuki Karasuyama and Masanobu Nakayama

J. Phys. Chem. Lett., 2026, 17, 1937
DOI: 10.1021/acs.jpclett.5c03772

Higher-Order Thermal Transport Theory for Phonon Thermal Transport in Semiconductors Using Lattice Dynamics Calculations and the Boltzmann Transport Equation

Ankit Jain, Yagyank Srivastava, Amey G. Gokhale, Nidheesh Virakante and Hardik L. Kagdada

J Indian Inst Sci, 2025
DOI: 10.1007/s41745-025-00487-3

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez-Bombarelli and Yang Shao-Horn

Nat Rev Mater, 2022, 7, 991
DOI: 10.1038/s41578-022-00466-5

MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art

Jianjun Hu, Stanislav Stefanov, Yuqi Song, Sadman Sadeed Omee, Steph-Yves Louis, Edirisuriya M. D. Siriwardane, Yong Zhao and Lai Wei

npj Comput Mater, 2022, 8
DOI: 10.1038/s41524-022-00750-6

Recent advances in the SISSO method and their implementation in the SISSO++ code

Thomas A. R. Purcell, Matthias Scheffler and Luca M. Ghiringhelli

The Journal of Chemical Physics, 2023, 159
DOI: 10.1063/5.0156620

End-to-end material thermal conductivity prediction through machine learning

Yagyank Srivastava and Ankit Jain

Journal of Applied Physics, 2023, 134
DOI: 10.1063/5.0183513

Harnessing lateral transfer learning for pioneering solid electrolyte interphase innovation

Kehao Tao, Wei He, An Chen, Yanqiang Han and Jinjin Li

Energy Storage Materials, 2025, 75, 104034
DOI: 10.1016/j.ensm.2025.104034

Low Thermal Conductivity Contributes to High Thermoelectric Performance: A Review

Haolin Ye and Chongjian Zhou

Acta Metall. Sin. (Engl. Lett.), 2025, 38, 720
DOI: 10.1007/s40195-025-01817-1

Toward Efficient Thermoelectric Materials and Devices: Advances, Challenges, and Opportunities

Xiao-Lei Shi, Nan-Hai Li, Meng Li and Zhi-Gang Chen

Chem. Rev., 2025, 125, 7525
DOI: 10.1021/acs.chemrev.5c00060

A Machine Learning Study on High Thermal Conductivity Assisted to Discover Chalcogenides with Balanced Infrared Nonlinear Optical Performance

Qingchen Wu, Lei Kang and Zheshuai Lin

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202309675

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass

Jing Gao, Zhilong Wang, Yanqiang Han, Mingyu Gao and Jinjin Li

Small, 2024, 20
DOI: 10.1002/smll.202305918

AlphaMat: a material informatics hub connecting data, features, models and applications

Zhilong Wang, An Chen, Kehao Tao, Junfei Cai, Yanqiang Han, Jing Gao, Simin Ye, Shiwei Wang, Imran Ali and Jinjin Li

npj Comput Mater, 2023, 9
DOI: 10.1038/s41524-023-01086-5

Thermoelectric properties of SmS@Y2O2S and Y2O2S@SmS compounds with a core-shell nanostructure

A.V. Sotnikov, V.V. Bakovets, E.V. Korotaev, M.M. Syrokvashin, A.Sh. Agazhanov and D.P. Pishchur

Chemical Physics Letters, 2022, 809, 140157
DOI: 10.1016/j.cplett.2022.140157

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

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

Advanced Materials, 2025, 37
DOI: 10.1002/adma.202505642

Evaluation of the corrosion inhibition performance of the phosphate derivative K2SrP2O7 on carbon steel in 1 M hydrochloric acid solution

N. Elouhabi, N. Timoudan, J. Bensalah, L. Ait Hmid, A. Bouabbadi, C. Halioui, Z. Safi, A. Benzaouak, A. Zarrouk and M. Harchrass

Inorganic Chemistry Communications, 2025, 180, 114961
DOI: 10.1016/j.inoche.2025.114961

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