Papers citing Opportunities and Challenges for Machine Learning in Materials Science
Recent advances and applications of deep learning methods in materials science
npj Computational Materials · 2022-04-05
Kamal Kumar Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth A. Holm, Shyue Ping Ong, Chris M. Wolverton
Read original paper ↗ ·MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art
npj Computational Materials · 2022-04-11
Jianjun Hu, Stanislav Stefanov, Yuqi Song, Sadman Sadeed Omee, Steph-Yves Louis, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Lai Wei
Read original paper ↗ ·The case for data science in experimental chemistry: examples and recommendations
Nature Reviews Chemistry · 2022-04-21
Junko Yano, Kelly J. Gaffney, John M. Gregoire, Linda Hung, A. Ourmazd, Joshua Schrier, James A. Sethian, Francesca Maria Toma
Read original paper ↗ ·Data‐Driven Materials Innovation and Applications
Advanced Materials · 2022-04-22
Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue‐Feng Yu
Read original paper ↗ ·Transport in Lithium Garnet Oxides as Revealed by Atomistic Simulations
Annual Review of Materials Research · 2022-04-26
Wei Lai
Read original paper ↗ ·Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels
npj Computational Materials · 2022-04-27
Yuchen Liu, Henry Wu, Tam Mayeshiba, Benjamin T. Afflerbach, Ryan M. Jacobs, Josh Perry, Jerit George, Josh Cordell, Jinyu Xia, Hao Yuan, Aren Lorenson, Haotian Wu, Matthew Parker, Fenil Doshi, Alexander L. Politowicz, Linda Xiao, Dane D. Morgan, Peter B. Wells, Nathan Almirall, Takuya Yamamoto, G. Robert Odette
Read original paper ↗ ·Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis
Current Opinion in Chemical Engineering · 2022-04-29
Gloria A Sulley, M. M. Montemore
Read original paper ↗ ·Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs
Cell Reports Physical Science · 2022-05-01
Ryan M. Jacobs, Mingren Shen, Yuhan Liu, Hao Wei, Xiaoshan Li, Ruoyu He, Jacob R.C. Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G. Field, Dane D. Morgan
Read original paper ↗ ·Sn-based solder design using machine learning approach
International Conference on Electronics Packaging (ICEP) · 2022-05-11
Yuchen Liu, Chih-han Yang, Shih‐kang Lin
Read original paper ↗ ·The machine learning in lithium-ion batteries: A review
Engineering Analysis with Boundary Elements · 2022-05-16
Liyuan Zhang, Zijun Shen, S. Mohammad Sajadi, Anton Satria Prabuwono, Mustafa Z. Mahmoud, Goshtasp Cheraghian, ElSayed M. Tag El Din
Read original paper ↗ ·Calibration after bootstrap for accurate uncertainty quantification in regression models
npj Computational Materials · 2022-05-20
Glenn Palmer, Siqi Du, Alexander L. Politowicz, Joshua Paul Emory, Xiyu Yang, Anupraas Gautam, Grishma Gupta, Zhelong Li, Ryan M. Jacobs, Dane D. Morgan
Read original paper ↗ ·Deep learning object detection in materials science: Current state and future directions
Computational Materials Science · 2022-05-24
Ryan M. Jacobs
Read original paper ↗ ·Machine learning in concrete science: applications, challenges, and best practices
npj Computational Materials · 2022-06-06
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, Farshad Rajabipour, Wil V. Srubar, Ismaïla Dabo, Aleksandra Radlińska
Read original paper ↗ ·Innovations Toward the Valorization of Plastics Waste
Annual Review of Materials Research · 2022-07-01
Zachary R. Hinton, Michael R. Talley, Pavel A. Kots, Anne V. Le, Tan Zhang, Michael E. Mackay, Aditya M. Kunjapur, Peng Bai, Dionisios G. Vlachos, Mary P. Watson, Michael C. Berg, Thomas H. III Epps, LaShanda T. J. Korley
Read original paper ↗ ·Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases
Computational Materials Science · 2022-07-28
Massimiliano Lupo Pasini, Gang Seob Jung, Stephan Irle
Read original paper ↗ ·Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology
Surface Topography Metrology and Properties · 2022-08-17
Linh Thi Phuong Nguyen, Bernard Haochih Liu
Read original paper ↗ ·Identification of dislocation reaction kinetics in complex dislocation networks for continuum modelling using data-driven methods
Journal of the Mechanics and Physics of Solids · 2022-08-18
Balduin Katzer, Kolja Zoller, D. Weygand, Katrin Schulz
Read original paper ↗ ·A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls
Chemistry of Materials · 2022-09-01
Piyush Karande, Brian J. Gallagher, T. Yong-Jin Han
Read original paper ↗ ·When not to use machine learning: A perspective on potential and limitations
MRS Bulletin · 2022-09-01
Matthew R. Carbone
Read original paper ↗ ·Towards out of distribution generalization for problems in mechanics
Computer Methods in Applied Mechanics and Engineering · 2022-09-14
Lingxiao Yuan, Harold S. Park, Emma Lejeune
Read original paper ↗ ·Machine learning for high-entropy alloys: Progress, challenges and opportunities
Progress in Materials Science · 2022-09-15
Xianglin Liu, Jiaxin Zhang, Zongrui Pei
Read original paper ↗ ·A machine learning approach to nonlinear ultrasonics for classifying annealing conditions in austenitic stainless steel
Journal of Applied Physics · 2022-09-16
Saju T. Abraham, Manju Mohan, Pandian Chelliah, Krishnan Balasubramaniam, Balasubramanian Venkatraman
Read original paper ↗ ·Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques
Science and Technology of Advanced Materials Methods · 2022-09-22
Gengo Hayashi, K. Suzuki, Tomoyuki Terai, H. Fujii, M. Ogura, Kazunori Satō
Read original paper ↗ ·Design and Optimization of CNN Architecture to Identify the Types of Damage Imagery
Mathematics · 2022-09-23
Ching‐Lung Fan, Yu-Jen Chung
Read original paper ↗ ·Machine learning versus human learning in predicting glass-forming ability of metallic glasses
Acta Materialia · 2022-11-02
Guannan Liu, Sungwoo Sohn, Sebastian Alexander Kube, Arindam Raj, Andrew Mertz, Aya Nawano, Anna Gilbert, Mark D. Shattuck, Corey S. O’Hern, Jan Schroers
Read original paper ↗ ·State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning
Metals · 2022-11-04
Upadesh Subedi, Sachin Poudel, Khem Gyanwali, Yuri Amorim Coutinho, G. Matula, Anil Kunwar
Read original paper ↗ ·Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology
Advanced Energy Materials · 2022-11-07
Igor I. Baskin, Yair Ein‐Eli
Read original paper ↗ ·Machine learning accelerates the materials discovery
Materials Today Communications · 2022-11-09
Jiheng Fang, Ming Xie, Xingqun He, Jiming Zhang, Jieqiong Hu, Yongtai Chen, Youcai Yang, Qinglin Jin
Read original paper ↗ ·Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks
ACS Omega · 2022-11-17
Igor V. Volgin, Pavel A. Batyr, Andrey Matseevich, Alexey Yu. Dobrovskiy, Maria V. Andreeva, Victor M. Nazarychev, Sergey V. Larin, Mikhail Ya. Goikhman, Yury V. Vizilter, А.А. Аскадский, Sergey V. Lyulin
Read original paper ↗ ·Prediction of mechanical properties for deep drawing steel by deep learning
International Journal of Minerals Metallurgy and Materials · 2022-11-25
Gang Xu, Jinshan He, Zhimin Lü, Min Li, Jinwu Xu
Read original paper ↗ ·Exploiting Machine Learning in Multiscale Modelling of Materials
Journal of The Institution of Engineers (India) Series D · 2022-11-28
G. Anand, Swarnava Ghosh, Liwei Zhang, Angesh Anupam, Colin L. Freeman, Christoph Ortner, Markus Eisenbach, James R. Kermode
Read original paper ↗ ·Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling
International Conference on Computational Science and Computational Intelligence (CSCI) · 2022-12-01
Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·Process safety consequence modeling using artificial neural networks for approximating heat exchanger overpressure severity
Computers & Chemical Engineering · 2022-12-05
Ahmed Harhara, Akhil Arora, M. M. Faruque Hasan
Read original paper ↗ ·Automatic Recognition of Microstructures of Air-Plasma-Sprayed Thermal Barrier Coatings Using a Deep Convolutional Neural Network
Coatings · 2022-12-23
Xiao Shan, Tianmeng Huang, Lirong Luo, Jie Lu, Huangyue Cai, Junwei Zhao, Gang Sheng, Xiaofeng Zhao
Read original paper ↗ ·14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Digital Discovery · 2023-01-01
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al‐Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan A. Bringuier, L. Catherine Brinson, Kamal Kumar Choudhary, Defne Çırcı, Sam Cox, Wibe Albert de Jong, Matthew L. Evans, Nicolas Gastellu, Jérôme Genzling, M.V. Gil, Ankur Kumar Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Galen Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan T. Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, K. J. Schmidt, Ian T Foster, Andrew Dickson White, Ben J. Blaiszik
Read original paper ↗ ·Algorithmic Path Dependence in AI-Driven Materials Design
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Maria Gonzalez, Javier Ruiz, Lucia Torres
Read original paper ↗ ·Application of high-throughput methodologies and artificial intelligence for adhesion testing
Elsevier eBooks · 2023-01-01
Kshitish A. Patankar, Tom H. Kalantar, Simon Cook, Edoardo Nicoli, Brad Tuft, Matthew Crimmins
Read original paper ↗ ·Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials
Energy Advances · 2023-01-01
Haruna Adamu, Sani Isah Abba, Paul Betiang Anyin, Yusuf Sani, Mohammad Qamar
Read original paper ↗ ·Artificial neural networks and their applications in computational materials science: A review and a case study
Advances in applied mechanics · 2023-01-01
Shaoping Xiao, John Li, Stéphane Bordas, Tae‐Yeon Kim
Read original paper ↗ ·Complementing a Continuum Thermodynamic Approach to Constitutive Modeling with Symbolic Regression
Journal of the Mechanics and Physics of Solids · 2023-01-01
Karl Garbrecht, Donovan Birky, Brian Lester, John M Emery, Jacob D. Hochhalter
Read original paper ↗ ·Dynamic Material Characterization Through In-Situ Electrical Resistivity Measurements of High Temperature Transient Liquid Phase Sinter Alloys
The minerals, metals & materials series · 2023-01-01
Gilad Nave, P. McCluskey
Read original paper ↗ ·Ensemble Methods in Materials AI — Why Models Disagree and What It Means Scientifically
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Claire Martin, Julien Robert, Sophie Bernard
Read original paper ↗ ·Guided Clustering for Selecting Representatives Samples in Chemical Databases
Lecture notes in computer science · 2023-01-01
Felipe V. Calderan, João Paulo Almeida de Mendonça, Juarez L. F. Da Silva, Marcos G. Quiles
Read original paper ↗ ·Materials AI as Policy Actor: A Conceptual Framework for Downstream Decision Impact
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Maria Hernandez, Carlos Vega, Lucia Torres
Read original paper ↗ ·Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
Springer eBooks · 2023-01-01
Purvi Bhatt, Neha Singh, Sumit Chaudhary
Read original paper ↗ ·Scientific Blind Spots in Materials AI—A Systematic Conceptual Mapping: A Review Study
Journal of Artificial Intelligence for Materials Science · 2023-01-01
James Walker, Olivia Harris
Read original paper ↗ ·Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Fatima Zahra Amrani, Youssef Benali, Samira El-Haddad
Read original paper ↗ ·Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Maria Hernandez, Carlos Vega
Read original paper ↗ ·The Curse of Optimality: When Perfect Optimization Undermines Scientific Understanding
Journal of Artificial Intelligence for Materials Science · 2023-01-01
Nour Abdelrahman, Karim Hassan, Ahmed El-Kholy
Read original paper ↗ ·Data Augmentation of Micrographs and Prediction of Impact Toughness for Cast Austenitic Steel by Machine Learning
Metals · 2023-01-04
Gang Xu, Xiaotong Zhang, Jinwu Xu
Read original paper ↗ ·