Papers citing Accelerating the prediction of stable materials with machine learning
Transition metal single-atom doped MoS2 for gas adsorption: A combined density functional theory and machine learning study
Vacuum · 2025
Jingcheng Xu, Jin Zhang, Z JIANG, Ding Wang, Huijun Li, Qiaobo Liao, Huan Shang, Huiyan Xu
Read original paper ↗ ·Interpretable machine learning framework: Predicting catalytic activities of complex photocatalysts towards CO2 conversion
Materials Today Physics · 2025
Chen‐Chen Er, Lutfi Kurnianditia Putri, Yee Sin Ang, Hui Ying Yang, Siang‐Piao Chai
Read original paper ↗ ·High entropic engineering for sustainable energy and environmental applications
Journal of environmental chemical engineering · 2025
Monika Singh, Iram Malik, Fawwaz Hazzazi, Anuj Kumar
Read original paper ↗ ·Review: machine learning in high-entropy alloys-transformative potential and innovative application
Journal of Materials Science · 2025
Xudong Hu
Read original paper ↗ ·Breaking the Ion Ordering in the Perovskite Anode for Enhanced High-Temperature Oxygen Evolution Reaction Activity
Journal of the American Chemical Society · 2025
Lina Yu, Xueyu Hu, Yige Guo, Shaowei Zhang, Jingcheng Yu, Geng Zou, Xiaomin Zhang, Mingrun Li, Zhentao Ma, Xusheng Zheng, Yuefeng Song, Meilin Liu, Guoxiong Wang, Xinhe Bao
Read original paper ↗ ·Streamlining Coding Assignments and Grading on the Cloud: A Preconfigured JupyterHub Image for Chemistry Education
Journal of Chemical Education · 2025
Lechen Dong, Fang Liu
Read original paper ↗ ·First-Principles Insights into Pressure-Induced Structural Transformations in Molecular Solids
Journal of the Indian Institute of Science · 2025
Pranab Gain, Shovan Das, Ayan Datta
Read original paper ↗ ·Quantifying morphology for dynamic grain evolution modeling and property correlation analysis in Ni-rich cathode materials
Chinese Chemical Letters · 2025
Ning Chen, Yining Wang, Chunhua Yang, Muyan Xie, Weihua Gui, Zhixing Wang, Jiayao Chen, Jiexi Wang
Read original paper ↗ ·Artificial intelligence for post-moore field-effect transistors: a review and perspective
Materials Today Electronics · 2025
Xiang Li, Nan Jiang, Wenhan Zhou, Xiaoyi Zhang, Yang Hu, Shuo Wang, Huipu Wang, Hengze Qu, Haibo Zeng, Shengli Zhang
Read original paper ↗ ·Exploring novel Ni-Mn-X based magnetocaloric materials via machine learning with physical descriptors
Applied Materials Today · 2025
Hongxing Li, Xiaohua Tian, Wenbin Zhao, Jie Yang, Xiaochuan Wang, Kun Zhang, Jian Li, Changlong Tan
Read original paper ↗ ·Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review
Polymers · 2025
Ivan Pavlovich Malashin, Dmitry Martysyuk, В С Тынченко, Andrei Gantimurov, Vladimir Nelyub, А. С. Бородулин
Read original paper ↗ ·Interpretable machine learning for solvent prediction and mechanistic insights in multi-component crystal screening
Chemical Engineering Journal · 2025
Qi Zeng, Yiqun Zhang, Yuting Peng, Qun Zeng, Guangxu Sun, Minshan Guo, Ting Cai
Read original paper ↗ ·Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency
Advanced Energy Materials · 2025
Kwang S. Kim
Read original paper ↗ ·Bandgap prediction and design of halide double perovskites via ensemble machine learning
Chemical Engineering Journal · 2025
Dongwen Yang, Shulin Luo, Fahai Zhong, Lingrui Wang, Sanjun Wang, Wenyuan Zhang, Chunyao Niu, Chong Li, Fei Wang
Read original paper ↗ ·Rescaled Schwarz-Christoffel Transformations for Isotropic, Polygon, and Multiphysics Metamaterials
Physical Review Letters · 2025
Pengfei Zhuang, Chengmeng Wang, Fubao Yang, Gaole Dai, Liujun Xu, Peng Tan, Fabio Marchesoni, Jiping Huang
Read original paper ↗ ·Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials
Journal of Electronic Materials · 2025
Xuexue Pan, Yuanlin Xie, Chujun Li, Yangyang He, Yitong Zhang, Yilin Wang, Zhichun Li, Peiyu Peng, Jun Wang
Read original paper ↗ ·An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
npj Computational Materials · 2025
Jisu Kim, Jiho Lee, Sangmin Oh, Yutack Park, Seungwoo Hwang, Seungwu Han, Sungwoo Kang, Youngho Kang
Read original paper ↗ ·Machine learning-guided design of confinement space carbon materials for enhanced oxygen reduction catalysis
Applied Surface Science · 2025
Kezhi Yang, Xinxin Pi, Yanhui Li, Guangshun Chai, Lingtao Ran, Hancheng Yu, Jingwen He, Mulin Wang, Yan Bing He
Read original paper ↗ ·Machine Learning Approaches for Factor-Based Stock Price Prediction
International Conference on Big Data Economy and Information Management (BDEIM) · 2025
Zhengkai Sun
Read original paper ↗ ·AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology
Journal of Nanobiotechnology · 2025
Lin Zhao, Xinglong Liu, Xiangying Deng
Read original paper ↗ ·AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries
Chemical Society Reviews · 2026
Yu Zhong Zhan, Nan Chen, Li Li, Feng Wu, Renjie Chen
Read original paper ↗ ·High-performance interpretable model for exfoliation energy prediction of 2D nanomaterials
Chinese Chemical Letters · 2026
Jizhuo Duan, Liying Cui, Qian Wang, Yanchang Zhang, Miao Du, Danning Sun, Xinrong Li, Bing Zheng
Read original paper ↗ ·Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning Algorithm
SSRN Electronic Journal · 2026
Nan Ni, Peng Sun
Read original paper ↗ ·Identifying Water-Containing Genes of Electric Vehicle Cell Using Machine Learning Algorithm
SSRN Electronic Journal · 2026
Peng Sun, Nan Ni
Read original paper ↗ ·Informatics for thermodynamics: Stability to synthesizability
Elsevier eBooks · 2026
Krishna Rajan
Read original paper ↗ ·The synergy of geometric tolerance factor and machine learning in discovering stable materials
Journal of Materials Informatics · 2026
Zhilong Wang, Fengqi You
Read original paper ↗ ·Artificial intelligence films for dynamic and sustainable preservation of high-value fruits
Food Research International · 2026
Zhaoxuan He, Shenggui Lin, Xingyu Liu, Wenqian Li, Yanyong Mao, Hao Shan Shi
Read original paper ↗ ·Combining Compositional Screening via Hydride Route, Machine Learning, and In Situ Diffraction to Discover Antimonides in the K–Cd–Sb System
Chemistry of Materials · 2026
Tori Cox, Volodymyr Gvozdetskyi, Zeina Miari, Kaden Osborn, Arka Sarkar, Philip Yox, Balaranjan Selvaratnam, Shiya Chen, Yang Sun, Arthur Mar, Julia V. Zaikina
Read original paper ↗ ·Electrocatalysis Informatics Driven Design of Formic Acid‐Producing Alloy Catalysts for CO 2 Electroreduction
ChemPhysChem · 2026
Qianzhuo Lei, Yihan Zhang, Pengcheng Liu, Xihui Bian, Xijun Liu, Jia He
Read original paper ↗ ·Leveraging transfer learning for accurate estimation of ionic migration barriers in solids
npj Computational Materials · 2026
Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam
Read original paper ↗ ·Simulation-guided AI-driven digital twin for plasma etching in semiconductor fabrication
Device · 2026
Sanghoon Myung, Kyeyeop Kim, Chanyoung Lee, Jinwoo Kim, Yunji Choi, Gijae Kang, Namhyun Kim, Bae Eun Jung, Jaejun Lee, Jiseong Doh, Kyu Baik Chang, Jaehyun Bae, Sun-Taek Lim, Seungmin Lee, Kwang-Yong Yang, Dohaing Lee, Keun Hee Bai, Dae Sin Kim, Jaehoon Jeong
Read original paper ↗ ·Can Intermediate Temperatures be a “Goldilocks Zone” for Green Hydrogen Production?
Journal of the American Chemical Society · 2026
Subhajit Bhattacharjee, Kuldeep Mamtani, Harshal D. Chaudhari, Henner Heyen, Sridevi Govindarajan, Sagar D. Ghadage, Sajanikumari Sadasivan
Read original paper ↗ ·Machine learning acceleration of granular and solid-fluid flow simulations: A review
Powder Technology · 2026
Michael Castro, Shuo Li, Hiroki Imai, Kai-En Yang, Mikio Sakai
Read original paper ↗ ·Systematically Verified Experimental Thermoelectric Dataset For Data-driven Approaches
Integrating materials and manufacturing innovation · 2026
Leng Ze Tang, Layla Purdy, Trupti Mohanty, Leonard W. T. Ng, Taylor D. Sparks
Read original paper ↗ ·Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction
Chemical Reviews · 2026
Wei Guo, Chaochao Dun, Jinghua Guo, Jeffrey J. Urban, Chang Yu, Qiuyu Zhang, Jieshan Qiu
Read original paper ↗ ·Predicting sliding ferroelectricity in heterobilayers via physical descriptors without explicit bilayer modeling
Physical review. B./Physical review. B · 2026
Xian Wang, Qun Zeng, Jun Zhou, Xuesen Wang, Mingli Yang, Lei Shen, Yuan Ping Feng
Read original paper ↗ ·Chemistry-wise transfer active learning accelerated investigation of high-entropy alloys towards CO2 reduction
Electrochimica Acta · 2026
Hao Wu, Peiheng Lai, Chenhang Zheng, Hao Cheng, Yang Zhang, Andy Pau Chen, T. Yang, Tong Yang, Minggang Zeng, Bing Li, Ming Yang
Read original paper ↗ ·Multiscale computational design of hydrogen-compatible integrated structural-functional alloys
Progress in Materials Science · 2026
Renhao Wu, Zaigham Saeed Toor, Muhammad Raihan Hashmi, Katsuaki Tanabe, Hyojin Park, Hyojeong Ha, Shin‐ichi Orimo, Hyoung Seop Kim
Read original paper ↗ ·Multimodal fusion and transfer learning enable cross-material life prediction of defect dominated fatigue in additively manufactured metals
International Journal of Fatigue · 2026
Haijie Wang, Bo Li, Qimin Wu, Jianrui Zhang, Fuzhen Xuan
Read original paper ↗ ·Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials
Nanomaterials · 2026
Jie Yang, Lingli Tang, Yunlong Wang, Jie Wen, Wenyuan Chen
Read original paper ↗ ·Recent advances in electronic structure learning
Modelling and Simulation in Materials Science and Engineering · 2026
Tim Sandro Hindges, Wenhao He, Ju Li
Read original paper ↗ ·Improving crystal material property prediction with multi-view geometric graph transformer
Nature Communications · 2026
Liang Zhang, Ziyue Wang, Xin Wang (91924), Xinyu Zhang (14029), Yu Mao, Kong Chen, Yuen Wu
Read original paper ↗ ·Non-negative matrix factorization analysis of spatially-resolved photoemission spectra for epitaxially grown graphene on SiC
Science and Technology of Advanced Materials Methods · 2026
Masaki Imamura, Kazutoshi Takahashi
Read original paper ↗ ·Composition-dependent electronic, optical, and thermoelectric properties of CsIn1-xGaxBr3 from DFT and machine learning
Results in Engineering · 2026
Marwan Suleiman Mousa, Anas Y. Al‐Reyahi, Saber Sâad Essaoud, Said M. Al Azar, Mufeed Maghrabi, Watheq Al‐Basheer, Issam Trrad, Enas A. Arrasheed, Ammar Alsoud, Alexandr Knápek, Dinara S. Sobola
Read original paper ↗ ·Machine learning-enabled performance prediction and design for flexible strain sensors
Materials Today Nano · 2026
Yingnan Wu, Zijing Dong, Zhiyuan Sun, Chao Zhi, Z H Liu, Xingzhong Gao, Y W Sun, Qi Wang, Xiaolin Zhang, Siman Li, Chenxi Yang, Jindi Chen, Lingjie Yu
Read original paper ↗ ·A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions
The Journal of Physical Chemistry Letters · 2026
S M Chen, J J Lu, Yucheng Fu, Yuping Huang, Z-F Yu, Yixiao Zhang, Chenji Hu, Guozhong Lu, Zhiwei Yu, Hong Min Zhu, Liwei Chen
Read original paper ↗ ·Engineered interfaces in electronic materials for energy-efficient computing
Nature Reviews Materials · 2026
Sathvik Ajay Iyengar, Pulickel Madhavapanicker Ajayan, Vincent C. Meunier, Tahir Ghani, Sayeef Salahuddin, Asir Intisar Khan
Read original paper ↗ ·Generative Models in Inorganic Crystals Discovery and Inverse Design
Energy & environment materials · 2026
Tao Li, Xiaolin Liu, Fei Wang, Yan Xiong, Yu Chen, Yan Zhao
Read original paper ↗ ·A comprehensive review of perovskite materials for electronic device applications
Journal of Materials Science Materials in Energy · 2026
Md Abdul Kuddus Sheikh, Md Shazarul Islam, Daniel Drane, Hasina F. Huq
Read original paper ↗ ·High-entropy alloy catalysts: from foundational thermodynamics to machine learning-enabled discovery and closed-loop optimization for electrocatalytic applications
Coordination Chemistry Reviews · 2026
Sumayya Mumtaz, John David Rodney, T. Ratnaji, Byung Chul Kim
Read original paper ↗ ·