Papers citing Opportunities and Challenges for Machine Learning in Materials Science
Application of machine learning to mechanical properties of copper-graphene composites
MRS Communications · 2023
Milan Rohatgi, Amir Kordijazi
Read original paper ↗ ·Emerging Trends in Machine Learning: A Polymer Perspective
ACS Polymers Au · 2023
Tyler B. Martin, Debra J. Audus
Read original paper ↗ ·Analysis and evaluation of machine learning applications in materials design and discovery
Materials Today Communications · 2023
Mahsa Golmohammadi, Masoud Aryanpour
Read original paper ↗ ·A butt shear joint (BSJ) specimen for high throughput testing of adhesive bonds
The Journal of Adhesion · 2023
Chao Kang, José J. M. Machado, Yu Sekiguchi, Ming Ji, Chiaki Sato, Masanobu Naito
Read original paper ↗ ·Predicting Model Training Time to Optimize Distributed Machine Learning Applications
Electronics · 2023
Miguel Guimarães, Davide Rua Carneiro, Guilherme Palumbo, Filipe Oliveira, Óscar Oliveira, Victor Alves, Paulo Nováis
Read original paper ↗ ·Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Advanced Functional Materials · 2023
Yiming Liu, Xinyu Tan, Jie Liang, Hongwei Han, Peng Xiang, Wensheng Yan
Read original paper ↗ ·Rational design of high-entropy ceramics based on machine learning – A critical review
Current Opinion in Solid State and Materials Science · 2023
Jun Zhang, Xuepeng Xiang, Biao Xu, Shasha Huang, Yaoxu Xiong, Shihua Ma, Haijun Fu, Yi Ma, Hongyu Chen, Zhenggang Wu, Shijun Zhao
Read original paper ↗ ·Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning
Environmental Science & Technology · 2023
Haiping Gao, Shifa Zhong, Raghav Dangayach, Yongsheng Chen
Read original paper ↗ ·A machine learning model for flank wear prediction in face milling of Inconel 718
The International Journal of Advanced Manufacturing Technology · 2023
Tiyamike Banda, Yuchen Liu, Ali Akhavan Farid, Chin Seong Lim
Read original paper ↗ ·Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning
Materials Today Communications · 2023
Ronald Machaka, Precious Manti Radingoana
Read original paper ↗ ·Water absorption prediction of nanopolymer hydrophobized concrete surface using texture analysis and machine learning algorithms
Construction and Building Materials · 2023
Małgorzata Szafraniec, Zbigniew Omiotek, Danuta Barnat-Hunek
Read original paper ↗ ·Resolution-enhanced X-ray fluorescence microscopy via deep residual networks
npj Computational Materials · 2023
Longlong Wu, Seong‐Min Bak, Youngho Shin, Yong S. Chu, Shinjae Yoo, Ian Robinson, Xiaojing Huang
Read original paper ↗ ·Accelerated prediction of perovskite material properties with classical machine learning and graph neural network
Materials Express · 2023
Zhihao Dong, Youyong Li, Youyong Li
Read original paper ↗ ·A critical examination of robustness and generalizability of machine learning prediction of materials properties
npj Computational Materials · 2023
Kangming Li, Brian DeCost, Kamal Kumar Choudhary, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·Predicting Colorectal Cancer Using Machine and Deep Learning Algorithms: Challenges and Opportunities
Big Data and Cognitive Computing · 2023
Dabiah Alboaneen, Razan Alqarni, Sheikah Alqahtani, Maha Alrashidi, Rawan Alhuda, Eyman A. Alyahyan, Turki Alhumaidi Alshammari
Read original paper ↗ ·Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
Archives of Computational Methods in Engineering · 2023
Dimitrios Angelis, Filippos D. Sofos, Theodoros E. Karakasidis
Read original paper ↗ ·Utilizing ANN for Predicting the Cauchy Stress and Lateral Stretch of Random Elastomeric Foams under Uniaxial Loading
Materials · 2023
Zhentao Liu, Chaoyang Wang, Zhenyu Lai, Zikang Guo, Liang Chen, Kai Zhang, Yong Yi
Read original paper ↗ ·Multiscale optimization of the viscoelastic behavior of short fiber reinforced composites
International Journal of Mechanics and Materials in Design · 2023
Julian Marr, Lukas Zartmann, Doris Reinel-Bitzer, Heiko Andrä, Ralf Müller
Read original paper ↗ ·Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State
Nano Letters · 2023
Ruihao Yuan, Abinash Kumar, Shihao Zhuang, Nicholas Cucciniello, Teng Lü, Deqing Xue, Deqing Xue, Aubrey N. Penn, Alessandro R. Mazza, Quanxi X. Jia, Yun Liu, Dezhen Xue, Dezhen Xue, Jinshan Li, Jia‐Mian Hu, James M. LeBeau, Aiping Chen
Read original paper ↗ ·Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning
Materials Letters · 2023
Haobing Hou, Jianfeng Wang, Ye Li, Shijie Zhu, Liguo Wang, Shaokang Guan
Read original paper ↗ ·Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
npj Computational Materials · 2023
Bruno Focassio, Michelangelo Domina, Urvesh Patil, A. Fazzio, Stefano Sanvito
Read original paper ↗ ·A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides
Materials Today Advances · 2023
Yi Je Cho, Kathy Lu
Read original paper ↗ ·Machine learning-assisted crystal engineering of a zeolite
Nature Communications · 2023
Xinyu Li, He Han, Nikolaos Evangelou, Noah J. Wichrowski, Peng Lü, Wenqian Xu, Son‐Jong Hwang, Wenyang Zhao, Chunshan Song, Xinwen Guo, Aditya Bhan, Ioannis G. Kevrekidis, Michael Tsapatsis B
Read original paper ↗ ·Band gap predictions of double perovskite oxides using machine learning
Communications Materials · 2023
Anjana Talapatra, Blas Pedro Uberuaga, Christopher R. Stanek, Ghanshyam Pilania
Read original paper ↗ ·Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data
Venue not supplied by source · 2023
Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis
Journal of Materials Research and Technology · 2023
Xinliang Zheng, Yi Xie, Xujiao Yang, Muhammad Nasir Amin, Sohaib Nazar, Suleman Ayub Khan, Fadi Althoey, Ahmed Farouk Deifalla
Read original paper ↗ ·Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning
Chemistry of Materials · 2023
Rebecca C. Walker, Andres P. Hyer, Haiquan Guo, James K. Ferri
Read original paper ↗ ·Needs, Trends, and Advances in Scintillators for Radiographic Imaging and Tomography
IEEE Transactions on Nuclear Science · 2023
Zhehui Wang, Christophe Dujardin, Matthew S. Freeman, Amanda E. Gehring, J. F. Hunter, Paul R. Lecoq, Wei Liu, Charles L. Melcher, Christopher L. Morris, Martin Nikl, Ghanshyam Pilania, Reeju Pokharel, Daniel Robertson, Daniel J. Rutstrom, Sky K. Sjue, Anton S. Tremsin, S. A. Watson, Brenden Wiggins, Nicola M. Winch, Mariya Zhuravleva
Read original paper ↗ ·A General Tensor Prediction Framework Based on Graph Neural Networks
The Journal of Physical Chemistry Letters · 2023
Yang Zhong, Hongyu Yu, Xin-Gao Gong, Hongjun Xiang
Read original paper ↗ ·Outliers in Shannon’s effective ionic radii table and the table extension by machine learning
Computational Materials Science · 2023
Mohammed Alsalman, Yousef A. Alghofaili, Ahmer A.B. Baloch, Haydar Alsadah, Abdulmohsen A. Alsaui, Saad M. Alqahtani, Ali Hussein Muqaibel, Fahhad H. Alharbi
Read original paper ↗ ·Data-driven machine learning for alloy research: Recent applications and prospects
Materials Today Communications · 2023
Xueyun Gao, Haiyan Wang, Huijie Tan, Lei Xing, Zhiyu Hu
Read original paper ↗ ·Hardness and fracture toughness models by symbolic regression
The European Physical Journal Plus · 2023
Jinbin Zhao, Peitao Liu, Jian-Tao Wang, Jiangxu Li, Haiyang Niu, Yan Ping Sun, Junlin Li, Xing‐Qiu Chen
Read original paper ↗ ·AlphaMat: a material informatics hub connecting data, features, models and applications
npj Computational Materials · 2023
Zhilong Wang, An Chen, Kehao Tao, Junfei Cai, Yanqiang Han, Jing Li Gao, Simin Ye, Shiwei Wang, Imran Ali, Jinjin Li
Read original paper ↗ ·On establishment of novel constitutive model for directionally solidified nickel-based superalloys utilizing machine learning methods
China Foundry · 2023
Jia-yan Sun, Rong Yin, Ye-yuan Hu, Yunxiang Tan, Qingyan Xu
Read original paper ↗ ·Digital transformation strategies for applied science domains
Research Ideas and Outcomes · 2023
Samuel Bentum, David J. Wild
Read original paper ↗ ·Evolution of artificial intelligence for application in contemporary materials science
MRS Communications · 2023
Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning
Journal of Chemical Information and Modeling · 2023
Han Zhang, Haoyuan Li, Hanshen Xin, Jianhua Zhang
Read original paper ↗ ·Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training
Journal of Chemical Information and Modeling · 2023
Luis Cesar de Azevedo, Ronaldo Cristiano Prati
Read original paper ↗ ·Atomic Positional Embedding-Based Transformer Model for Predicting the Density of States of Crystalline Materials
The Journal of Physical Chemistry Letters · 2023
Yaning Cui, Kang Chen, Lingyao Zhang, Haotian Wang, Lei Bai, David Elliston, Wei Li Ren
Read original paper ↗ ·Development of chemistry-informed interpretable model for predicting compressive strength of recycled aggregate concrete containing supplementary cementitious materials
Journal of Cleaner Production · 2023
Yingli Gao, Zhengkang Li, Yuelin Li, Zhanghuang Zhu, Juncai Zhu
Read original paper ↗ ·A Machine Learning Approach for Segmentation and Characterization of Microtextured Regions in a Near-α Titanium Alloy
Crystals · 2023
Haodong Rao, Dong Liu, Feng Jin, Nan Lv, Jungang Nan, Haiping Wang, Yanhui Yang, Jianguo Wang
Read original paper ↗ ·A versatile strategy for hybridizing small experimental and large simulation data: A case for ceramic tape-casting process
Materials & Design · 2023
Jeong-Hun Kim, Hyunseok Ko, Dong-Hun Yeo, Zeehoon Park, Upendra Kumar, Kwan‐Hee Yoo, Aziz Nasridinov, Sung Beom Cho
Read original paper ↗ ·In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science
Journal of the American Chemical Society · 2023
Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch
Read original paper ↗ ·Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles
Small · 2023
Susan Jyakhwo, Nikita Serov, Andrei Dmitrenko, Vladimir V. Vinogradov
Read original paper ↗ ·Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance
Metals · 2023
Jingteng Xue, Jingtao Huang, Mingwei Li, Jiaying Chen, Zongfan Wei, Yuan Sheng Cheng, Zhonghong Lai, Nan Qu, Yong Liu, Jingchuan Zhu
Read original paper ↗ ·From prediction to design: Recent advances in machine learning for the study of 2D materials
Nano Energy · 2023
Hua He, Yuhua Wang, Yajuan Qi, Zichao Xu, Yue Li, Yumei Wang
Read original paper ↗ ·Development of Prediction Method for Dimensional Stability of 3D-Printed Objects
Applied Sciences · 2023
Kyung-Eun Min, Jaewon Jang, Je-Sik Shin, Cheolhee Kim, Sung Yi
Read original paper ↗ ·Machine Learning: Models, Challenges, and Research Directions
Future Internet · 2023
Tala Talaei Khoei, Naima Kaabouch
Read original paper ↗ ·Organic pollutants removal from aqueous solutions using metal-organic frameworks (MOFs) as adsorbents: A review
Journal of environmental chemical engineering · 2023
Lixin Li, Jiazhen Han, Xiaohui Huang, Shan Qiu, Xihan Liu, Lilai Liu, Mengjie Zhao, Jiwei Qu, Jinlong Zou, Jun Zhang
Read original paper ↗ ·Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing
Optics Express · 2023
Yuhao Zhu, Haoyu Su, Pengsheng Xu, Yuxin Xu, Yujie Wang, Chun‐Hua Dong, Jin Lu, Zichun Le, Xiaoniu Yang, Qi Xuan, Chang‐Ling Zou, Hongliang Ren
Read original paper ↗ ·