Recent advances in designing thermoelectric materials

Madhubanti Mukherjee, Ashutosh Srivastava and Abhishek K. Singh

J. Mater. Chem. C, 2022, 10, 12524
DOI: 10.1039/D2TC02448A

Electrochemical reduction of carbon dioxide to multicarbon (C2+) products: challenges and perspectives

Bin Chang, Hong Pang, Fazal Raziq, Sibo Wang, Kuo-Wei Huang, Jinhua Ye and Huabin Zhang

Energy Environ. Sci., 2023, 16, 4714
DOI: 10.1039/D3EE00964E

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

Subhra Sourav Jana, Ritwik Banerjee and Tanmoy Maiti

J. Mater. Chem. A, 2025, 13, 27050
DOI: 10.1039/D5TA02713F

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

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

Prediction of superior thermoelectric performance in unexplored doped-BiCuSeO via machine learning

Zhijian He, Jinlin Peng, Chihou Lei, Shuhong Xie, Daifeng Zou and Yunya Liu

Materials & Design, 2023, 229, 111868
DOI: 10.1016/j.matdes.2023.111868

Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction

Ning Zhang, Baopeng Yang, Kang Liu, Hongmei Li, Gen Chen, Xiaoqing Qiu, Wenzhang Li, Junhua Hu, Junwei Fu, Yong Jiang, Min Liu and Jinhua Ye

Small Methods, 2021, 5
DOI: 10.1002/smtd.202100987

Integrating Machine Learning and Molecular Simulation for Material Design and Discovery

Priyanka Sinha, D. Roshini, Varad Daoo, B. Moses Abraham and Jayant K. Singh

Trans Indian Natl. Acad. Eng., 2023, 8, 325
DOI: 10.1007/s41403-023-00412-z

Machine learning approaches to identify and design low thermal conductivity oxides for thermoelectric applications

Abhishek Tewari, Siddharth Dixit, Niteesh Sahni and Stéphane P.A. Bordas

DCE, 2020, 1
DOI: 10.1017/dce.2020.7

Machine learning assisted interpretation of creep and fatigue life in titanium alloys

Sucheta Swetlana, Ashish Rout and Abhishek Kumar Singh

APL Machine Learning, 2023, 1
DOI: 10.1063/5.0129037

Multielement Magnesium-Based Alloys via Machine Learning Screening for Fuel Cell Bipolar Plates

Xue Bai, Qi Jiang, Sen Lu, Pei Song, Zepeng Jia, Pengyue Shan, Yunjian Chen, Hong Cui, Rong Feng, Zhiyong Liang, Qin Kang, Yazhou Wang, Naigen Zhou and Hongkuan Yuan

J. Phys. Chem. C, 2023, 127, 16162
DOI: 10.1021/acs.jpcc.3c03569

Decoupled atomic contribution boosted high thermoelectric performance in mixed cation spinel oxides ACo2O4

Ashutosh Srivastava, Madhubanti Mukherjee and Abhishek Kumar Singh

Applied Physics Letters, 2022, 120
DOI: 10.1063/5.0099452

Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds

Dipanwita Bhattacharjee, Krishnaraj Kundavu, Deepanshi Saraswat, Parul R. Raghuvanshi and Amrita Bhattacharya

ACS Appl. Energy Mater., 2022, 5, 8913
DOI: 10.1021/acsaem.2c01400

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
DOI: 10.1002/aelm.202500210

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

An Informatics Approach for Designing Conducting Polymers

Harikrishna Sahu, Hongmo Li, Lihua Chen, Arunkumar Chitteth Rajan, Chiho Kim, Natalie Stingelin and Rampi Ramprasad

ACS Appl. Mater. Interfaces, 2021, 13, 53314
DOI: 10.1021/acsami.1c04017

Deep dive into machine learning density functional theory for materials science and chemistry

L. Fiedler, K. Shah, M. Bussmann and A. Cangi

Phys. Rev. Materials, 2022, 6
DOI: 10.1103/PhysRevMaterials.6.040301

Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction

Swanti Satsangi, Avanish Mishra and Abhishek K. Singh

ACS Phys. Chem Au, 2022, 2, 16
DOI: 10.1021/acsphyschemau.1c00017

A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives

Madhubanti Mukherjee, Swanti Satsangi and Abhishek K. Singh

Chem. Mater., 2020, 32, 6507
DOI: 10.1021/acs.chemmater.0c01778

Recommendation System to Predict the d‐band Center of Core‐Shell Bimetallic Nanoparticles Catalysts

Sakshi Agarwal and Abhishek Singh

Advcd Theory and Sims, 2025, 8
DOI: 10.1002/adts.202401460

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

Wei-Hsin Chen, K. Aishwarya and Kripasindhu Sardar

Energy Mater., 2025, 5
DOI: 10.20517/energymater.2024.311

Predicting the thermoelectric figure of merit in p-type BiSbTe-based alloys using artificial neural network modeling

Babu Madavali, Cheenepalli Nagarjuna, Sheetal Kumar Dewangan, Byungmin Ahn and Soon-Jik Hong

Materials Today Communications, 2024, 40, 109396
DOI: 10.1016/j.mtcomm.2024.109396