Zaheer Ud Din Babar, Vincenzo Iannotti, Giulio Rosati, Ayesha Zaheer, Raffaele Velotta, Bartolomeo Della Ventura, Ruslan Álvarez-Diduk and Arben Merkoçi

Chem. Soc. Rev., 2025, 54, 3387
DOI: 10.1039/D3CS01024D

Two-dimensional carbon/boron nitrides: modification, machine learning and beyond

Qi Wu, YangRui Lin, Yangwenting Ou, Changhua Wang, He Ma, Rui Wang, Yuanyuan Li and Xintong Zhang

J. Mater. Chem. A, 2024, 12, 14302
DOI: 10.1039/D4TA02059F

Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions

Zeeshan Haider Jaffari, Ather Abbas, Muhammed Umer, Eun-Sik Kim and Kyung Hwa Cho

J. Mater. Chem. A, 2023, 11, 9009
DOI: 10.1039/D3TA00019B

Microstructures of layered Ni-rich cathodes for lithium-ion batteries

Jingyu Lu, Chao Xu, Wesley Dose, Sunita Dey, Xihao Wang, Yehui Wu, Deping Li and Lijie Ci

Chem. Soc. Rev., 2024, 53, 4707
DOI: 10.1039/D3CS00741C

Recent advances in graphene-based electroanalytical devices for healthcare applications

Vinay Kammarchedu, Heshmat Asgharian, Keren Zhou, Pouya Soltan Khamsi and Aida Ebrahimi

Nanoscale, 2024, 16, 12857
DOI: 10.1039/D3NR06137J

AI and ML for selecting viable electrocatalysts: progress and perspectives

Anastasiia P. Dmitrieva, Anna S. Fomkina, Chantal T. Tracey, Elizaveta A. Romanenko, Ali Ayati, Pavel V. Krivoshapkin and Elena F. Krivoshapkina

J. Mater. Chem. A, 2024, 12, 31074
DOI: 10.1039/D4TA04991H

MaskTerial: a foundation model for automated 2D material flake detection

Jan-Lucas Uslu, Alexey Nekrasov, Alexander Hermans, Bernd Beschoten, Bastian Leibe, Lutz Waldecker and Christoph Stampfer

Digital Discovery, 2025, 4, 3744
DOI: 10.1039/D5DD00156K

A mechanism review of metal phthalocyanines as single-atomic catalysts in electrochemical energy conversion

Zikang Li, Ziqi Zhou, Mingzi Sun, Tong Wu, Qiuyang Lu, Lu Lu, Baian Chen, Cheuk Hei Chan, Hon Ho Wong and Bolong Huang

Chem. Sci., 2025, 16, 14019
DOI: 10.1039/D5SC03210E

Insights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning

Elham Rahmanian, Rasoul Malekfar and Ali Sajedi-Moghaddam

J. Mater. Chem. A, 2024, 12, 32318
DOI: 10.1039/D4TA05780E

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Vera Kuznetsova, Áine Coogan, Dmitry Botov, Yulia Gromova, Elena V. Ushakova and Yurii K. Gun'ko

Advanced Materials, 2024, 36
DOI: 10.1002/adma.202308912

Catalysis in the digital age: Unlocking the power of data with machine learning

Bokinala Moses Abraham, Mullapudi V. Jyothirmai, Priyanka Sinha, Francesc Viñes, Jayant K. Singh and Francesc Illas

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1730

Water transport through a graphene channel with different cross-sectional shapes

Zi Wang, Shuang Li, Xinke Zhang and Jiaye Su

Journal of Molecular Liquids, 2022, 366, 120241
DOI: 10.1016/j.molliq.2022.120241

Biomass-Derived Carbonaceous Materials with Graphene/Graphene-Like Structures: Definition, Classification, and Environmental Applications

Shi-Jie Yuan, Jing-Jing Wang, Bin Dong and Xiao-Hu Dai

Environ. Sci. Technol., 2023, 57, 17169
DOI: 10.1021/acs.est.3c04203

Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: A review

Chloe Isabella Tsang, Haihui Pu and Junhong Chen

APL Machine Learning, 2025, 3
DOI: 10.1063/5.0240004

Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene

Apiphu Chenwittayakhachon, Kulpavee Jitapunkul, Bunyanuch Nakpalad, Phanit Worrayotkovit, Supawadee Namuangruk, Pichamon Sirisinudomkit and Pawin Iamprasertkun

2D Mater., 2023, 10, 025003
DOI: 10.1088/2053-1583/acaf8d

Accelerating quasiparticle physics: a review on the synergy of machine learning and density functional theory for excitons, polaritons, and plasmons

Hosein Alavi-Rad, Mahyar Hassani-Vasmejani and Meysam Bagheri Tagani

J. Phys.: Condens. Matter, 2026, 38, 163003
DOI: 10.1088/1361-648X/ae4ce8

Discovery of Bimetallic Hexagonal MBene Mo2ErB3T2.5 (T = O, F, and Cl)

Zhiqi Wang, Jianan Su, Duo Feng, Yufang Yao, Yujing Yan, Yanjie Cui, Gian‐Marco Rignanese, Hideo Hosono and Junjie Wang

Small, 2024, 20
DOI: 10.1002/smll.202407100

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

Yu Mao, Lei Wang, Chenduan Chen, Zhan Yang and Jun Wang

Laser & Photonics Reviews, 2023, 17
DOI: 10.1002/lpor.202200357

Phyllosilicates as earth-abundant layered materials for electronics and optoelectronics: Prospects and challenges in their ultrathin limit

Ingrid D. Barcelos, Raphaela de Oliveira, Gabriel R. Schleder, Matheus J. S. Matos, Raphael Longuinhos, Jenaina Ribeiro-Soares, Ana Paula M. Barboza, Mariana C. Prado, Elisângela S. Pinto, Yara Galvão Gobato, Hélio Chacham, Bernardo R. A. Neves and Alisson R. Cadore

Journal of Applied Physics, 2023, 134
DOI: 10.1063/5.0161736

Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

Krittapong Deshsorn, Panwad Chavalekvirat, Somrudee Deepaisarn, Ho-Chiao Chuang and Pawin Iamprasertkun

ACS Mater. Au, 2026, 6, 28
DOI: 10.1021/acsmaterialsau.5c00030

A2BB’X6/ABX3-type High-performance perovskites screening based on ensemble learning and high throughput screening

Xu Fang, Zhengxin Chen, Qian Ma, Jiang Wu, Jia Lin, Jiawei Li, Wenhao Li, Chaofan Liu, Hongtao Shen and Linhong You

Solar Energy, 2023, 262, 111795
DOI: 10.1016/j.solener.2023.111795

Janus Halogenated Silicene for Photocatalytic and Photovoltaic Applications: A Machine Learning Combined with First-Principles Study

Wei Hong, Jianmei Yuan and Yuliang Mao

J. Phys. Chem. C, 2024, 128, 14282
DOI: 10.1021/acs.jpcc.4c02939

Predicting sliding ferroelectricity in heterobilayers via physical descriptors without explicit bilayer modeling

Xian Wang, Qun Zeng, Jun Zhou, Xuesen Wang, Mingli Yang, Lei Shen and Yuan Ping Feng

Phys. Rev. B, 2026, 113
DOI: 10.1103/d6bc-52rr

RETRACTED: Two-Dimensional Violet Phosphorus P11: A Large Band Gap Phosphorus Allotrope

Gary Cicirello, Mengjing Wang, Quynh P. Sam, James L. Hart, Natalie L. Williams, Huabing Yin, Judy J. Cha and Jian Wang

J. Am. Chem. Soc., 2023, 145, 8218
DOI: 10.1021/jacs.3c01766

Enhanced DFT predictions of the structural and optoelectronic properties of MoTe2 for high performance photodetection: Application to GW-based functionals and Hubbard U and V corrections

Shehu Aminu Yamusa, Amiruddin Shaari, Norah A.M. Alsaif, Najeh Rekik, G. Lakshminarayana, Ibrahim Isah, Magaji Ismail and Razif Razali

Chemical Physics, 2023, 573, 112018
DOI: 10.1016/j.chemphys.2023.112018

A critical review on electronic materials properties and multifunctional applications

Wubshet Getachew Mengesha and Kaveer Nagessar

Discov Mater, 2025, 6
DOI: 10.1007/s43939-025-00517-y

First-principles investigation of two-dimensional magnesium chloride: Environmental stability and fundamental properties

Stepan A. Shcherbinin, Svetlana V. Ustiuzhanina, Elena A. Korznikova and Andrey A. Kistanov

Physica E: Low-dimensional Systems and Nanostructures, 2023, 151, 115715
DOI: 10.1016/j.physe.2023.115715

Recent advances in machine learning and deep learning-enabled studies on transition metal dichalcogenides

Shivani Bhawsar and Eui-Hyeok Yang

J. Phys. D: Appl. Phys., 2025, 58, 073005
DOI: 10.1088/1361-6463/ad9933

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

An effective method for generating crystal structures based on the variational autoencoder and the diffusion model

Chen Chen, Jinzhou Zheng, Chaoqin Chu, Qinkun Xiao, Chaozheng He and Xi Fu

Chinese Chemical Letters, 2025, 36, 109739
DOI: 10.1016/j.cclet.2024.109739

MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration

Vimukthi Dananjaya, Nethmi Hansika, Sathish Marimuthu, Venkata Chevali, Yogendra Kumar Mishra, Andrews Nirmala Grace, Nisa Salim and Chamil Abeykoon

Progress in Materials Science, 2025, 152, 101433
DOI: 10.1016/j.pmatsci.2025.101433

Unlocking NIR‐II Photoluminescence in 2D Copper Tetrasilicate Nanosheets through Flame Spray Synthesis

Robert Nißler, Quanyu Zhou, Björn Hill, Sabrina L.J. Thomä, Lukas R.H. Gerken, Aurelio Borzi, Kevin Roost, Benjamin Mächler, Xosé Luís Deán‐Ben, Antonia Neels, Sebastian Kruss, Daniel Razansky and Inge K. Herrmann

Advanced Materials, 2025, 37
DOI: 10.1002/adma.202503159

The Future of MXene Fibers

Ken Aldren S. Usman, Jizhen Zhang, Lingyi Bi, Shayan Seyedin, Xungai Wang, Yury Gogotsi and Joselito M. Razal

Advanced Materials, 2025, 37
DOI: 10.1002/adma.202506437

Integrating Theory and Experiment with Graph Neural Networks to Classify Molecular Self-Assembly on Metal Surfaces

Linbin Zhu, Shaochen Hu, Xianpeng Wang, Haiming Zhang, Lizhen Huang, Youyong Li, Lu Wang and Lifeng Chi

Nano Lett., 2026
DOI: 10.1021/acs.nanolett.6c00359

Metallene: Ångström‐Scale 2D Metals

Fengzhu Ren, Zhaoyang Han, Lingfeng Zhu, Zhihao Lei, Guozheng Shi, Zhixuan Li, Chun‐Ho Lin, Long Hu, Hui Li, Xinwei Guan, Baohua Jia, Prashant Kumar and Tianyi Ma

Advanced Materials, 2026, 38
DOI: 10.1002/adma.202512683

From Formability to Bandgap: Machine Learning Accelerates the Discovery and Application of Perovskite Materials

Shiyan Wang, Chaopeng Liu, Weiyao Hao, Yanling Zhuang, Xianjun Zhu, Longlu Wang, Xianghong Niu, Shujuan Liu, Bing Chen and Qiang Zhao

ACS Nano, 2025, 19, 29049
DOI: 10.1021/acsnano.5c07494

Two-dimensional field-effect transistor empowering multifunctional application

Rongxin Li, Sushmita Chandra, Changjiang Yi and Claudia Felser

J. Phys. D: Appl. Phys., 2025, 58, 493001
DOI: 10.1088/1361-6463/ae21e9

Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment

François Nkinahamira, Anqi Feng, Lijie Zhang, Hongwei Rong, Pamphile Ndagijimana, Dabin Guo, Baihui Cui and Huichun Zhang

TrAC Trends in Analytical Chemistry, 2024, 180, 117980
DOI: 10.1016/j.trac.2024.117980

Machine learning studies for magnetic compositionally complex alloys: A critical review

Xin Li, Chan-Hung Shek, Peter K. Liaw and Guangcun Shan

Progress in Materials Science, 2024, 146, 101332
DOI: 10.1016/j.pmatsci.2024.101332

Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion

Tianyi Wang, Qilong Wu, Yun Han, Zhongyuan Guo, Jun Chen and Chuangwei Liu

Applied Physics Reviews, 2025, 12
DOI: 10.1063/5.0235572

Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach

Faiza Qayyum, Murad Ali Khan, Do-Hyeun Kim, Hyunseok Ko and Ga-Ae Ryu

Materials, 2023, 16, 7322
DOI: 10.3390/ma16237322

Deep learning in two-dimensional materials: Characterization, prediction, and design

Xinqin Meng, Chengbing Qin, Xilong Liang, Guofeng Zhang, Ruiyun Chen, Jianyong Hu, Zhichun Yang, Jianzhong Huo, Liantuan Xiao and Suotang Jia

Front. Phys., 2024, 19
DOI: 10.1007/s11467-024-1394-7

Novel Strain Engineering Combined with a Microscopic Pore Synergistic Modulated Strategy for Designing Lattice Tensile-Strained Porous V2C-MXene for High-Performance Overall Water Splitting

Yu Zhou, Yousen Wu, Dongxuan Guo, Jinlong Li, Yue Li, Xue Yang, Shanshan Fu, Guozhe Sui and Dong-Feng Chai

ACS Appl. Mater. Interfaces, 2023, 15, 15797
DOI: 10.1021/acsami.2c19729

Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects

Bowen Hou, Jinyuan Wu and Diana Y. Qiu

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-53748-7

Application of Machine Learning in Nano Research — A Review

Fatema Tuz Zohura, Mariam Akter Mimona, Md Mir Shakib Ahmed, Md Israfil Hossain Rimon, Md Aminul Islam, Nayem Hossain and Md Hosne Mobarak

Nano LIFE, 2026, 16
DOI: 10.1142/S1793984425300067

Graphene-Family Lubricant Additives: Recent Developments and Future Perspectives

Yanfei Liu, Shengtao Yu, Qiuyu Shi, Xiangyu Ge and Wenzhong Wang

Lubricants, 2022, 10, 215
DOI: 10.3390/lubricants10090215

Prediction of Bandgap in Lithium-Ion Battery Materials Based on Explainable Boosting Machine Learning Techniques

Haobo Qin, Yanchao Zhang, Zhaofeng Guo, Shuhuan Wang, Dingguo Zhao and Yuekai Xue

Materials, 2024, 17, 6217
DOI: 10.3390/ma17246217

Partnerships and collaboration drive innovative graduate training in materials informatics

Angela M. Slates, Shana L. McAlexander, Jennifer Nolan, Juan de Pablo, Junhong Chen, Harley T. Johnson and L. Catherine Brinson

Sci. Adv., 2024, 10
DOI: 10.1126/sciadv.adp7446

Novel Tellurium Oxide‐Based Electronic Devices: Preparation, Characterization and Applications

Xiangxiang Gao, Yufeng Chen, Yuelong Feng, Hong Zhang, Miao Zhang, Zhenhua Lin, Yue Hao and Jingjing Chang

Adv Funct Materials, 2026, 36
DOI: 10.1002/adfm.202526894

Intercalation-driven tunability in two-dimensional layered materials: Synthesis, properties, and applications

Bixuan Li, Lei Zheng, Yongji Gong and Peng Kang

Materials Today, 2024, 81, 118
DOI: 10.1016/j.mattod.2024.10.002

Electrolyte hydration energy as a universal descriptor for ion-specific capacitance: insights from interpretable machine learning

Elham Rahmanian, Ali Sajedi-Moghaddam, Mohammad Taha Hoveizavi and Seyed Hamed Aboutalebi

Advanced Powder Materials, 2026, 5, 100361
DOI: 10.1016/j.apmate.2025.100361

Accelerated discovery of MM′XT2 MXenes for catalysis, electronics, and energy storage using supervised machine learning

Umair Haider, Gul Rahman, Imran Shakir, M.S. Al-Buriahi, Norah Alomayrah and Imen Kebaili

Materials Today Physics, 2026, 61, 102022
DOI: 10.1016/j.mtphys.2026.102022

Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study

Sung Woo Lee, Marcel Jonathan Hidajat, Seung Hyeok Cha, Gwang-Nam Yun and Dong Won Hwang

Fuel Processing Technology, 2025, 267, 108164
DOI: 10.1016/j.fuproc.2024.108164

How Amorphous Nanomaterials Enhanced Electrocatalytic, SERS, and Mechanical Properties

Jianxin Kang, Fengshi Li, Ziyan Xu, Xiangyu Chen, Mingke Sun, Yanhong Li, Xiuyi Yang and Lin Guo

JACS Au, 2023, 3, 2660
DOI: 10.1021/jacsau.3c00418

Artificial Intelligence‐Guided Design of Fluorescent Probes for Biomedical Applications

Pan Tao, Pengzhan Wang, Dushuo Feng, Qiyue Wang, Fangyuan Li and Daishun Ling

Chemistry A European J, 2026, 32
DOI: 10.1002/chem.202503666

Machine-learning assisted 2D field-effect transistors for intelligent biosensing and analysis

Yulin Ye, Hongwei Hou, Lei Yang and Jinghong Li

TrAC Trends in Analytical Chemistry, 2026, 197, 118703
DOI: 10.1016/j.trac.2026.118703

Advanced synthesis and manufacturing integration of tellurium-based nanomaterials for electronics and energy devices

Yiyun Wang, Yi-Chen Wang, Hong-Bo Cui, Jiayi Guo, Guijian Guan and Ming-Yong Han

Materials Science and Engineering: R: Reports, 2026, 170, 101236
DOI: 10.1016/j.mser.2026.101236

Fracture at the two-dimensional limit

Bo Ni, Doug Steinbach, Zhenze Yang, Andrew Lew, Boyu Zhang, Qiyi Fang, Markus J. Buehler and Jun Lou

MRS Bulletin, 2022, 47, 848
DOI: 10.1557/s43577-022-00385-4

Machine learning-assisted shape prediction for two-dimensional transition metal dichalcogenides growth

Zihan Hu, Zongyu Huang, Biwen He, Siwei Luo and Xiang Qi

J Mater Sci, 2025, 60, 17038
DOI: 10.1007/s10853-025-11398-0

Moiré-pattern-tuned interlayer friction of two-dimensional α- and β-tellurene via first-principles calculations and experimental validation

Guoliang Ru, Xingwang Zhang, Weihong Qi, Chengfeng Du, Xuqing Liu, Qian Ye and Weimin Liu

Tribology International, 2025, 201, 110261
DOI: 10.1016/j.triboint.2024.110261

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou and Hao Li

Angewandte Chemie, 2026, 138
DOI: 10.1002/ange.202526150

Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

Liqianyun Xu, Feng Wu, Renjie Chen and Li Li

Energy Storage Materials, 2023, 59, 102785
DOI: 10.1016/j.ensm.2023.102785

Machine learning investigation of chemical disorder effects in 24-valence-electron full-Heusler alloys with single magnetic element

Yanqi Guo, Chaokun Guo, Yuanji Xu, Bing Zheng, Xiaodong Ni and Fuyang Tian

Journal of Magnetism and Magnetic Materials, 2025, 629, 173322
DOI: 10.1016/j.jmmm.2025.173322

Deep learning-assisted methods for accelerating the intelligent screening of novel 2D materials: New perspectives focusing on data collection and description

Yuandong Lin, Ji Ma, Yong-Guang Jia, Chongchong Yu and Jun-Hu Cheng

Coordination Chemistry Reviews, 2025, 529, 216436
DOI: 10.1016/j.ccr.2025.216436

Transition of electrochemical measurement to machine learning in the perspective of two-dimensional materials

Kulpavee Jitapunkul, Apiphu Chenwittayakhachon and Pawin Iamprasertkun

Front. Mater., 2022, 9
DOI: 10.3389/fmats.2022.1070608

2D Materials Beyond Graphene for Energy Storage and Conversion: Recent Advances and Future Perspectives

Anupama S and Senthilkumar D

NanoNEXT, 2026, 7, 21
DOI: 10.54392/nnxt2613

Accelerating transferable screening of hydrogen storage materials in single- and bimetallic-doped graphene

Weizhi Tian, Tiren Peng, Zhikai Gao, Hang Zhang, Xi Sun, Yuhang Zhou, Zishan Luo, Gongchang Ren, Hong Cui and Hongkuan Yuan

Journal of Alloys and Compounds, 2026, 1051, 185983
DOI: 10.1016/j.jallcom.2026.185983

Advances in machine learning (ML)-driven design of two-dimensional materials for energy and environmental applications

Tao Jian, Zengjian Zhao, Wangyang Ye, Xiang Cai, Hongjun Lin, Jiaheng Teng, Bisheng Li, Leihong Zhao and Banglin Chen

Nano Energy, 2026, 151, 111845
DOI: 10.1016/j.nanoen.2026.111845

Chalcogenide Glass Fibers and Their Advanced Optoelectronic Applications: Developments, Challenges, and Innovations

Yanqing Fu, Shiliang Kang, Chengwei Gao, Linling Tan, Peiqing Zhang, Shixun Dai and Changgui Lin

Laser & Photonics Reviews, 2026, 20
DOI: 10.1002/lpor.202502947

Prediction of Quantum Yields of Monolayer WS2 by Machine Learning

Jolene W. P. Khor, Trang Thu Tran, Anir S. Sharbirin, Sammy X. B. Yap, Hyunseung Choo and Jeongyong Kim

Advanced Optical Materials, 2024, 12
DOI: 10.1002/adom.202302195

Machine Learning‐Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data‐Driven Methodologies to Closed‐Loop Optimization

Tianyi Gao, Honghao Huang and Yang Liu

Advanced Materials, 2025
DOI: 10.1002/adma.202508263

Boosting flexible electronics with integration of two‐dimensional materials

Chongyang Hou, Shuye Zhang, Rui Liu, Thomas Gemming, Alicja Bachmatiuk, Hongbin Zhao, Hao Jia, Shirong Huang, Weijia Zhou, Jian‐Bin Xu, Jinbo Pang, Mark H. Rümmeli, Jinshun Bi, Hong Liu and Gianaurelio Cuniberti

InfoMat, 2024, 6
DOI: 10.1002/inf2.12555

Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites

Vimukthi Dananjaya, Sathish Marimuthu, Richard (Chunhui) Yang, Andrews Nirmala Grace and Chamil Abeykoon

Progress in Materials Science, 2024, 144, 101282
DOI: 10.1016/j.pmatsci.2024.101282

Advancements in MXene transformation: Nanoarchitectonics for biomedical and energy applications

Muhammad Altaf Nazir, Kinza Maqsood, Sami Ullah, Abdul Rauf, Ibrahim A. Shaaban, Rashid Iqbal, Awais Ahmad, Muhammad Sufyan Javed, Tayyaba Najam, Md Rezaul Karim and Syed Shoaib Ahmad Shah

Journal of Alloys and Compounds, 2025, 1030, 180852
DOI: 10.1016/j.jallcom.2025.180852

Machine and quantum learning for diamond-based quantum applications

Dylan G Stone and Carlo Bradac

Mater. Quantum. Technol., 2023, 3, 012001
DOI: 10.1088/2633-4356/acb30a

Function-oriented design principles for adsorbent materials of uranium extraction from seawater

Shaoqing Liu, Binbin Tao, Bin Zuo, Kaidan Zheng, Walid Abdelfattah, Jinyang Bao, Guoze Yan, Tao Wang, Ruoyu Wang, Zhengtong Li, Saad Melhi, Wafa A. Alshahrani and Xingtao Xu

Chemical Engineering Journal, 2024, 500, 156783
DOI: 10.1016/j.cej.2024.156783

Collaborative Enhancement of Humidity Sensing Performance by KCl-Doped CuO/SnO2 p–n Heterostructures for Monitoring Human Activities

Lei Wang, Feng Huang, Xinqi Yao, Shuaishuai Yuan, Xinhai Yu, Shan-Tung Tu and Shijian Chen

ACS Omega, 2023, 8, 4878
DOI: 10.1021/acsomega.2c07098

Understanding epitaxy of graphene: From experimental observation to density functional theory and machine learning

Ming-Sheng Zheng, Shaojie Zhou, Xinmo Wang and Lei Gao

Journal of Applied Physics, 2023, 134
DOI: 10.1063/5.0163580

When Machine Learning Meets 2D Materials: A Review

Bin Lu, Yuze Xia, Yuqian Ren, Miaomiao Xie, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T. S. Wee, Wilfred G. van der Wiel, Wen Zhang and Ping Kwan Johnny Wong

Advanced Science, 2024, 11
DOI: 10.1002/advs.202305277

Machine learning-assisted advances in graphene and 2D materials

Sunuk Choe, Taegeun Song, Donghun Lee, Ara Go and Nojoon Myoung

Current Applied Physics, 2026, 87, 116
DOI: 10.1016/j.cap.2026.04.004

Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

Zhi Jiang, Marco Peres, Carlo Bradac and Gil Gonçalves

Advanced Intelligent Systems, 2026, 8
DOI: 10.1002/aisy.202501082

Machine Learning Interatomic Potential for Modeling the Mechanical and Thermal Properties of Naphthyl-Based Nanotubes

Hugo X. Rodrigues, Hudson R. Armando, Daniel A. da Silva, João Paulo J. da Costa, Luiz A. Ribeiro and Marcelo L. Pereira

J. Chem. Theory Comput., 2025, 21, 2612
DOI: 10.1021/acs.jctc.4c01578

Optimizing density-functional simulations for two-dimensional metals

Kameyab Raza Abidi and Pekka Koskinen

Phys. Rev. Materials, 2022, 6
DOI: 10.1103/PhysRevMaterials.6.124004

Tentative exploration of artificial intelligence in developing and optimizing fuel cell catalysts

Qiuru Hua, Yiming Gu, Xianbin Guo, Chu Wu, Gang Li, Yihuang Zhang and Shudong Wang

Sci. Sin.-Chim, 2024, 54, 2055
DOI: 10.1360/SSC-2024-0128

Strategy for transferring van der Waals materials and heterostructures

Sidi Fan, Xianxu Li, Ashok Mondal, Wenjie Wang and Young Hee Lee

2D Mater., 2024, 11, 033002
DOI: 10.1088/2053-1583/ad4044

Brittleness index prediction using modified random forest based on particle swarm optimization of Upper Ordovician Wufeng to Lower Silurian Longmaxi shale gas reservoir in the Weiyuan Shale Gas Field, Sichuan Basin, China

Mbula Ngoy Nadege, Shu Jiang, Grant Charles Mwakipunda, Allou Koffi Franck Kouassi, Paulin Kavuba Harold and Konan Yao Hugues Roland

Geoenergy Science and Engineering, 2024, 233, 212518
DOI: 10.1016/j.geoen.2023.212518

Prediction of material stability of two-dimensional semiconductors: An interpretable machine learning perspective

Yuheng Chen, Sa Zhang, Yuhua Wen, Zhongyuan Lai and Tundong Liu

APL Materials, 2024, 12
DOI: 10.1063/5.0219418

Recent developments in 2D materials for gas separation membranes

Maria-Chiara Ferrari

Current Opinion in Chemical Engineering, 2023, 40, 100905
DOI: 10.1016/j.coche.2023.100905

Extracting and reconstructing knowledge in materials science literature using large language models

Shuyuan Li, Shihao Wei, Chenyu Huang, Yunjiang Zhang, Guizhen Zhang and Shaorui Sun

Commun Mater, 2026, 7
DOI: 10.1038/s43246-025-01043-3

Exploring the Potential of 2D TMD‐Based Optical Biosensors: Bridging Nanotechnology and Smart Diagnostics

Seema Thapa, Himshikha Malviya, Ranjana Verma and Jay Singh

The Chemical Record, 2026, 26
DOI: 10.1002/tcr.202500149

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li and Lado Filipovic

adv. intell. discov., 2025
DOI: 10.1002/aidi.202500074

Applications of density functional theory and machine learning in nanomaterials: A review

Nangamso Nathaniel Nyangiwe

Next Materials, 2025, 8, 100683
DOI: 10.1016/j.nxmate.2025.100683

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou and Hao Li

Angew Chem Int Ed, 2026, 65
DOI: 10.1002/anie.202526150

Advancing PFAS Remediation through Physics-Based Modeling of 2D Materials: Recent Progress, Challenges, and Opportunities

Monzure-Khoda Kazi, Sunith Varghese, Nahid Sarker, Nirupam Aich and Venkataramana Gadhamshetty

Ind. Eng. Chem. Res., 2025, 64, 1894
DOI: 10.1021/acs.iecr.4c03715

Computational Approaches for Designing Heterostructured Electrocatalysts

Miyeon Kim, Kyu In Shim and Jeong Woo Han

Small Science, 2025, 5
DOI: 10.1002/smsc.202400544

Descriptor engineering in machine learning regression of electronic structure properties for 2D materials

Minh Tuan Dau, Mohamed Al Khalfioui, Adrien Michon, Antoine Reserbat-Plantey, Stéphane Vézian and Philippe Boucaud

Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-31928-7

An open-source robust machine learning platform for real-time detection and classification of 2D material flakes

Jan-Lucas Uslu, Taoufiq Ouaj, David Tebbe, Alexey Nekrasov, Jo Henri Bertram, Marc Schütte, Kenji Watanabe, Takashi Taniguchi, Bernd Beschoten, Lutz Waldecker and Christoph Stampfer

Mach. Learn.: Sci. Technol., 2024, 5, 015027
DOI: 10.1088/2632-2153/ad2287

Ferroelectric MInP2X6 (M= Cu, Ag, Au; X= S, Se and Te) monolayers for selective gas capture: A combined DFT and machine learning approach

Parisa Mohammadi, Hamid Reza Shahdoosti, Shoeib Babaee Touski and Alireza Kokabi

Physica B: Condensed Matter, 2026, 724, 418155
DOI: 10.1016/j.physb.2025.418155

Design and screening of transition-metal doped chalcogenides as CO2-to-CO electrocatalysts

Junzhuo Wei, Yingju Yang, Jing Liu and Bo Xiong

Journal of CO2 Utilization, 2022, 64, 102165
DOI: 10.1016/j.jcou.2022.102165

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing

Zhehong Ai, Longhan Zhang, Yangguan Chen, Yu Meng, Yifan Long, Julin Xiao, Yao Yang, Wei Guo, Yueming Wang and Jing Jiang

ACS Sens., 2024, 9, 4143
DOI: 10.1021/acssensors.4c01083

Electrostatic potential distribution image-transfer learning method for highly accurate prediction of lithium diffusion barriers on transition metal dichalcogenide surfaces

Jian Chen, Yao Kang, Xudong Wang, Hao Huang and Man Yao

J. Zhejiang Univ. Sci. A, 2025, 26, 1010
DOI: 10.1631/jzus.A2400503

Novel octa-graphene-like structures based on GaP and GaAs

José A. S. Laranjeira, Nicolas F. Martins, Sérgio A. Azevedo, Guilherme S. L. Fabris and Julio R. Sambrano

J Mol Model, 2023, 29
DOI: 10.1007/s00894-023-05608-2

Synergistic Machine Learning Guided Discovery of ABa3(BSe3)2X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials

Yihan Yun, Mengfan Wu, Zhihua Yang, Guangmao Li and Shilie Pan

Advanced Science, 2025, 12
DOI: 10.1002/advs.202417851

Interpretable Machine Learning of Two‐Photon Absorption

Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou, Pavlo O. Dral and Cheng Wang

Advanced Science, 2023, 10
DOI: 10.1002/advs.202204902

Deterministic Localization of Strain-Induced Single-Photon Emitters in Multilayer GaSe

Weijun Luo, Alexander A. Puretzky, Benjamin J. Lawrie, Qishuo Tan, Hongze Gao, Zhuofa Chen, Alexander V. Sergienko, Anna K. Swan, Liangbo Liang and Xi Ling

ACS Photonics, 2023, 10, 2530
DOI: 10.1021/acsphotonics.3c00052

Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning

Wei Gu, Bin Yang, Dengfeng Li, Xunzhong Shang, Zhiyong Zhou and Jinming Guo

Journal of Advanced Ceramics, 2023, 12, 1389
DOI: 10.26599/JAC.2023.9220762

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Maninder Kaur, Nicolas T. Sandino, Jason P. Terry, Mahdi Ghafariasl and Yohannes Abate

Advanced Optical Materials, 2026, 14
DOI: 10.1002/adom.202503674

Machine Learning-Accelerated Discovery of A2BC2 Ternary Electrides with Diverse Anionic Electron Densities

Zhiqi Wang, Yutong Gong, Matthew L. Evans, Yujing Yan, Shiyao Wang, Nanxi Miao, Ruiheng Zheng, Gian-Marco Rignanese and Junjie Wang

J. Am. Chem. Soc., 2023, 145, 26412
DOI: 10.1021/jacs.3c10538

Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments

Sujeong Lee, Juhwan Noh and Ho Jin Ryu

Journal of Hazardous Materials, 2025, 494, 138735
DOI: 10.1016/j.jhazmat.2025.138735

2D MXenes as electrode materials for metal-sulfur batteries: A review

Irfan Ali Soomro, Muhammad Nazim Lakhan, Abdul Hanan, Hamad Almujibah, Altaf Hussain, Abdul Hameed Pato, Mukhtiar Ahmed, Imran Ali Chandio, Saeed Ahmed Memon, Muhammad Umer, Faiza Bibi and Ming Lei

Materials Today Physics, 2024, 45, 101453
DOI: 10.1016/j.mtphys.2024.101453

AI-Driven Inverse Design of Materials: Past, Present, and Future

Xiao-Qi 小琪 Han 韩, Xin-De 馨德 Wang 王, Meng-Yuan 孟圆 Xu 徐, Zhen 祯 Feng 冯, Bo-Wen 博文 Yao 姚, Peng-Jie 朋杰 Guo 郭, Ze-Feng 泽峰 Gao 高 and Zhong-Yi 仲毅 Lu 卢

Chinese Phys. Lett., 2025, 42, 027403
DOI: 10.1088/0256-307X/42/2/027403

Designing desalination MXene membranes by machine learning and global optimization algorithm

Xuanchao Ma, Chengyi Lan, Haoran Lin, Yiran Peng, Tianyu Li, Jiadong Wang, Jafar Azamat and Lijun Liang

Journal of Membrane Science, 2024, 702, 122803
DOI: 10.1016/j.memsci.2024.122803

Recent Progress in Emerging 2-D Transition-Metal Dichalcogenide-Based Gas Sensors: A Review

Ashok Kumar and Mahesh Kumar

IEEE Sensors J., 2024, 24, 25221
DOI: 10.1109/JSEN.2024.3422972

Homoatomic flatlands beyond graphene: A new avenue for gas sensors

Julaiba Tahsina Mazumder, Shubham Pandey and Ravindra Kumar Jha

Coordination Chemistry Reviews, 2024, 507, 215747
DOI: 10.1016/j.ccr.2024.215747

Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials

Jia Yang, Lingli Tang, Yunlong Wang, Jie Wen and Wenyuan Chen

Nanomaterials, 2026, 16, 650
DOI: 10.3390/nano16110650

Innovative Heating for the Nano Age: Exploring the Potentials of Carbothermal Shock

Jingyun Zou, Lei Tang and Lixing Kang

ACS Nano, 2025, 19, 152
DOI: 10.1021/acsnano.4c12350

Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials

Runkai Liu, Shu Lin, Jing Wan, Le Li, Guoqiang Zhang, Huasong Qin and Yilun Liu

Thin-Walled Structures, 2025, 213, 113261
DOI: 10.1016/j.tws.2025.113261

Machine learning for gas–solid interaction materials and devices

Peiyuan Liu, Xiaoyang Zhu, Xu Ran, Hengchang Bi, Xiao Huang and Ning Gu

Coordination Chemistry Reviews, 2025, 524, 216329
DOI: 10.1016/j.ccr.2024.216329

Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy

Isaiah A Moses and Wesley F Reinhart

Mach. Learn.: Sci. Technol., 2024, 5, 045081
DOI: 10.1088/2632-2153/ada2da

Inert-Atmosphere Microfabrication Technology for 2D Materials and Heterostructures

Aliaksandr Duleba, Mikhail Pugachev, Mark Blumenau, Sergey Martanov, Mark Naumov, Aleksey Shupletsov and Aleksandr Kuntsevich

Micromachines, 2023, 15, 94
DOI: 10.3390/mi15010094

Machine Learning Assisted Enhancement in a Two-Dimensional Material’s Sensing Performance

Suparna Das, Hirak Mazumdar, Kamil Reza Khondakar and Ajeet Kaushik

ACS Appl. Nano Mater., 2024, 7, 13893
DOI: 10.1021/acsanm.4c02127

Quantitative analysis of MoS2 thin film micrographs with machine learning

Isaiah A. Moses and Wesley F. Reinhart

Materials Characterization, 2024, 209, 113701
DOI: 10.1016/j.matchar.2024.113701

Discovery of Two-Dimensional Multinary Component Photocatalysts Accelerated by Machine Learning

Hao Jin, Xiaoxing Tan, Tao Wang, Yunjin Yu and Yadong Wei

J. Phys. Chem. Lett., 2022, 13, 7228
DOI: 10.1021/acs.jpclett.2c01862

From Discovery to Deliverables: Bridging the Machine Learning, Synthesis, and Characterization Gap in Nanoscale Materials

Ruyun Cai, Tianyi Gao, Fang Fang and Yang Liu

Chem. Mater., 2026, 38, 4385
DOI: 10.1021/acs.chemmater.5c03513

Atomic Interface Engineering in Two‐Dimensional Materials: A Pathway to High‐Performance Flexible Thermoelectrics

Samira Saddique, Inaam Ullah, Salamat Ali, Anand Parkash, Syed Zuhair Abbas Shah, Muhammad Irfan and Guiying Xu

The Chemical Record, 2026
DOI: 10.1002/tcr.70160

Advancing SERS Applications of 2D Materials through the Interplay of Rational Design and Structure‐Property Relationships

Aditya Thakur, Ruchi Singh, Vikas Yadav, Soumik Siddhanta and Kolleboyina Jayaramulu

Small Methods, 2025, 9
DOI: 10.1002/smtd.202402056

Controlling interfacial adhesion during the transfer of large-area 2D materials: mechanisms, strategies, and research advances

Rong HU, Jia SONG, Wei HUANG, An-na ZHOU, Jia-long LIN, Yang CAO and Sheng HU

New Carbon Materials, 2025, 40, 553
DOI: 10.1016/S1872-5805(25)60988-7

Boron and nitrogen doping engineering of biphenylene electronic and optical properties using deep learning

Alireza Kokabi and Mina Fayazi

Materials Today Communications, 2024, 40, 109847
DOI: 10.1016/j.mtcomm.2024.109847

Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration

Hao Zou, Haochen Zhao, Mingming Lu, Jiong Wang, Zeyu Deng and Jianxin Wang

Nat Commun, 2025, 16
DOI: 10.1038/s41467-024-55525-y

Nanocellulose functional materials for advanced adsorption and removal of emerging Contaminants: Green strategies and analytical perspectives

Yan Liu, Miao He, Luyao Zhao, Charles Q. Jia, Donald W. Kirk, Dianwei Zhang, Huilin Liu and Baoguo Sun

TrAC Trends in Analytical Chemistry, 2025, 192, 118424
DOI: 10.1016/j.trac.2025.118424

Large Language Models as Molecular Design Engines

Debjyoti Bhattacharya, Harrison J. Cassady, Michael A. Hickner and Wesley F. Reinhart

J. Chem. Inf. Model., 2024, 64, 7086
DOI: 10.1021/acs.jcim.4c01396

Enhanced HER activity in Pd/Pt-decorated Janus XSeI (X = Sb, Bi) monolayers

Utsav P. Prajapati, Rishit S. Shukla, Vidit B. Zala, Sanjeev K. Gupta and P.N. Gajjar

International Journal of Hydrogen Energy, 2024, 85, 881
DOI: 10.1016/j.ijhydene.2024.08.240

Design of Thermally Activated Delayed Fluorescence Materials with High Intersystem Crossing Efficiencies by Machine Learning-Assisted Virtual Screening

Ping Li, Zijie Wang, Wenjing Li, Jie Yuan and Runfeng Chen

J. Phys. Chem. Lett., 2022, 13, 9910
DOI: 10.1021/acs.jpclett.2c02735

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Yaping Qi, Dan Hu, Yucheng Jiang, Zhenping Wu, Ming Zheng, Esther Xinyi Chen, Yong Liang, Mohammad A. Sadi, Kang Zhang and Yong P. Chen

Advanced Optical Materials, 2023, 11
DOI: 10.1002/adom.202203104

Computational exploration of two-dimensional vacancy-free boridene sheet and its derivatives: high stabilities and the promise for hydrogen evolution reaction

Yuying Zhao, Jincan Zhang, Fengxian Ma, Hongbo Wu, Weizhen Meng, Ying Liu, Yalong Jiao and Aijun Du

Microstructures, 2024, 4
DOI: 10.20517/microstructures.2023.80

AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges

Yafeng Liu, Xiaohui Liu, Xuemei Wang and Hui Jiang

Biosensors, 2025, 15, 487
DOI: 10.3390/bios15080487

Recent advances in nano/microfabricated substrate platforms and artificial intelligence for practical surface-enhanced Raman scattering-based bioanalysis

Panangattukara Prabhakaran Praveen Kumar, Shimayali Kaushal and Dong-Kwon Lim

TrAC Trends in Analytical Chemistry, 2023, 168, 117341
DOI: 10.1016/j.trac.2023.117341

Methods, progresses, and opportunities of materials informatics

Chen Li and Kun Zheng

InfoMat, 2023, 5
DOI: 10.1002/inf2.12425

Automated Crack Detection in 2D Hexagonal Boron Nitride Coatings Using Machine Learning

Md Hasan-Ur Rahman, Bichar Dip Shrestha Gurung, Bharat K. Jasthi, Etienne Z. Gnimpieba and Venkataramana Gadhamshetty

Coatings, 2024, 14, 726
DOI: 10.3390/coatings14060726

Prediction of COF Synthesis Reaction Conditions Based on Data Mining and Machine Learning

Jinjin Zhao, Mengwen Yan, Zeyu Shi, Chao Zhao, Lei Qi, Qing Hao and Hong Liu

ACS Appl. Polym. Mater., 2025, 7, 16973
DOI: 10.1021/acsapm.5c03780

Defect‐Elimination Strategies for Fabricating High‐Strength and Highly Conductive MXene Fibers

Cheng Liang, Feiyu Tai, Zishuo Wang, Tianzhu Zhou and Qunfeng Cheng

Advanced Materials, 2026, 38
DOI: 10.1002/adma.202514754

Insights into Thermal Conductivity of Pnictogen Chalcogenides: Machine Learning Stereochemically Active Lone Pairs and Hybridization

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

Chem. Mater., 2025, 37, 198
DOI: 10.1021/acs.chemmater.4c02294

Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review

Chao Xin, Bingqian Song, Guangyong Jin, Yongli Song and Feng Pan

Advcd Theory and Sims, 2023, 6
DOI: 10.1002/adts.202300475

Statistics in enabling 2D materials: Optimization, predictive modelling, and data-driven discovery

Johnson Kehinde Abifarin and Yuerui Lu

Materials Today Physics, 2025, 57, 101814
DOI: 10.1016/j.mtphys.2025.101814

Large-Scale Integration of Experimental and Computational Data for 2D Materials

M. Amin Akhound, Tara M. Boland, Mikkel O. Sauer, Matthias Batzill, B. Moses Abraham, Stela Canulescu, Yury Gogotsi, Philip Hofmann, Andras Kis, Jiong Lu, Thomas Michely, Søren Raza, Wencai Ren, Joshua A. Robinson, Zdenek Sofer, Jinghua Teng, Søren Ulstrup, Meng Zhao, Xiaoxu Zhao, Jens J. Mortensen, Thomas Olsen and Kristian S. Thygesen

ACS Nano, 2026, 20, 12008
DOI: 10.1021/acsnano.6c01514