Papers citing Machine learning material properties from the periodic table using convolutional neural networks
Explainable Attention CNN for Predicting Properties of Heusler Alloys
The Journal of Physical Chemistry C · 2025
Yuhang Lu, Yibo Sun, Cong Hou, Zimo Li, Jun Ni
Read original paper ↗ ·Machine Learning-Driven Molecular Dynamics Investigation of the Physical and Heat Transfer Properties of Magnesium
JOM · 2025
Hanqing Li, Jinlong Chen, Cheng Cheng, Li L, Ju Rong, Yudong Sui, Zhijie He, Jueyi Ye, Xiangjie Fu, Xiaohua Yu, Jing Feng
Read original paper ↗ ·Unsupervised learning techniques for clustering analysis of physicochemical properties in the periodic table elements
Results in Chemistry · 2025
Sebastián Consuegra-Jiménez, Cesar Augusto Tovio Gracia, Ricardo Vivas‐Reyes
Read original paper ↗ ·REBCO superconductors by pulsed laser deposition: Key innovations and large-scale applications
iScience · 2025
Albina Jetybayeva, Aliya Orazalinovna Mukanova, Arailym Nurpeissova, Zhumabay Bakenov, Valery Petrykin, Sergey Lee
Read original paper ↗ ·Interface Engineering and Optimization Strategies for High‐Energy‐Density Batteries Based on Polymer Composite Electrolytes
Advanced Materials · 2025
Zhencheng Huang, Zexi Wang, Xi Chen, Luyi Yang, Tao Huang, Xuanlong He, Weiyuan Huang, Jing Chen, Xuming Yang, Lin Chen, Bin Liao, Xiaoping Ouyang, Jianhong Liu, Xiangzhong Ren, Feng Pan, Qianling Zhang, Jiangtao Hu
Read original paper ↗ ·Machine learning prediction of HSE06-level band gaps in two-dimensional semiconductors with reference-guided graph neural networks
Chinese Physics B · 2025
Zhen 振 Wan 万, Shun-Bo 顺波 Jiang 姜, Yuan 圆 Li 李, Hui 辉 Wang 王, Zong‐Liang Li, Guang‐Ping Zhang
Read original paper ↗ ·Emergent properties and the multiscale characterization challenge in condensed matter, from crystals to complex materials: a review
Journal of Physics D Applied Physics · 2025
Elisabetta Nocerino
Read original paper ↗ ·Physics-informed machine learning framework for predictive control of particle size distribution in Ni1/3Fe1/3Mn1/3(OH)2 synthesis
Chemical Engineering Science · 2025
Pengsen Luo, Yunlong Zhang, Zhan Wei Shen, Yong‐Jie Hu, Hangda Chen, Zi‐Feng Ma
Read original paper ↗ ·Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII) · 2025
Wei Shan Lee, I Hang Kwok, Kam Ian Leong, Chi Kiu Althina Chau, Kei Chon Sio
Read original paper ↗ ·Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning
Advanced Intelligent Systems · 2026
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo
Read original paper ↗ ·Motif-to-spectrum learning enables interpretable discovery and inverse design of optical absorbers
Matter & Light · 2026
Xiaoyu Yang, Yuanhui Sun, Lei Shen, Lijun Zhang
Read original paper ↗ ·Data‐Driven Prediction of Block Copolymer Morphology Using Coarse‐Grained Modeling and Machine Learning
Journal of Polymer Science · 2026
Lan Xu, Zhaofan Li, Wenjie Xia
Read original paper ↗ ·Per-MOF Mechanistic Attribution Reveals Context-Dependent Stability Mechanisms in Metal–Organic Frameworks
Journal of Chemical Information and Modeling · 2026
Sanjay Singh, Vinod Gadi, Srinivasa Reddy Mothe
Read original paper ↗ ·AI-Driven Design Platforms of Next-Generation Antibody Therapeutics
Topics in Current Chemistry · 2026
Yue Wang, Afsheen Saba, Yue Ran, Kiran Shehzadi, Q Zhang, Liang Jianhua, Mingjia Yu
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 ↗ ·