Papers citing Artificial intelligence and machine learning in design of mechanical materials
Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing
Virtual and Physical Prototyping · 2022
Sandeep Suresh Babu, Abdel‐Hamid I. Mourad, Khalifa Hamad Harib, Sanjairaj Vijayavenkataraman
Read original paper ↗ ·End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
ACS Nano · 2022
Yiwen Hu, Markus J. Buehler
Read original paper ↗ ·Application of LSTM Approach for Predicting the Fission Swelling Behavior within a CERCER Composite Fuel
Energies · 2022
Jian Hong Zhao, Zhenyue Chen, Jingqi Tu, Yunmei Zhao, Yiqun Dong
Read original paper ↗ ·Inverse design of truss lattice materials with superior buckling resistance
npj Computational Materials · 2022
Marco Maurizi, Chao Gao, Filippo Berto
Read original paper ↗ ·Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks
Polymers · 2022
Josué García-Ávila, Diego de Jesus Torres Serrato, Ciro Angel Rodriguez, Adriana Vargas‐Martínez, Erick Ramírez-Cedillo, J. Israel Martínez-López
Read original paper ↗ ·A deep learning model for predicting mechanical properties of polycrystalline graphene
Computational Materials Science · 2022
Md. Imrul Reza Shishir, Mohan Surya Raja Elapolu, Alireza Tabarraei
Read original paper ↗ ·Optimization of vascular structure of self-healing concrete using deep neural network (DNN)
Construction and Building Materials · 2022
Zhi Wan, Ze Chang, Yading Xu, Branko Šavija
Read original paper ↗ ·Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network
Eurasian Chemico-Technological Journal · 2022
Jalal Rasulzade, Yelaman Maksum, Makpal Nogaibayeva, Samir Rustamov, B. Akhmetov
Read original paper ↗ ·A deep learning approach to the texture optimization problem for friction control in lubricated contacts
arXiv (Cornell University) · 2022
Alexandre Iury Barbosa da Silva, Veniero Lenzi, Sergey Pyrlin, S. Carvalho, A. Cavaleiro, Luis S. A. Marques
Read original paper ↗ ·Perspectives on the Impact of Artificial Intelligence & Machine Learning on Processes & Structures Engineering
International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · 2022
Rajat Kishor Varshney, Sanjay Pratap Singh Chauhan, Vishnu Sharma
Read original paper ↗ ·Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Scientific Reports · 2022
M Maurizi, Chao Gao, Filippo Berto
Read original paper ↗ ·Machine learning for mechanics prediction of 2D MXene-based aerogels
Composites Communications · 2022
Chao Rong, Lei Zhou, Bowei Zhang, Fu‐Zhen Xuan
Read original paper ↗ ·Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets
Resources Policy · 2022
Umer Shahzad, Mahdi Ghaemi Asl, Mirela Clementina Panait, Tapan Sarker, Simona Andreea Apostu
Read original paper ↗ ·Digital Multiphase Material Microstructures for Image-Based AI Methods
Lecture notes in mechanical engineering · 2023
Eray Aksit, Karl R. Haapala, Ali Tabei
Read original paper ↗ ·Machine learning in materials modeling and design
Elsevier eBooks · 2023
Kamrun Nahar Keya, Amara Arshad, Sara A. Tolba, Wenjian Nie, Amirhadi Alesadi, Luis Ruiz Pestana, Wenjie Xia
Read original paper ↗ ·Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review
Materials Horizons · 2023
Junhyeong Lee, Donggeun Park, Mingyu Lee, Hugon Lee, Kundo Park, Ikjin Lee, Seunghwa Ryu
Read original paper ↗ ·Using genetic algorithms to discover novel ground-state triplet conjugated polymers
Physical Chemistry Chemical Physics · 2023
Omri D. Abarbanel, Geoffrey Hutchison
Read original paper ↗ ·Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures
Materials · 2023
Ali Cheloee Cheloee Darabi, Shima Rastgordani, Mohammadreza Khoshbin, Vinzenz Guski, Siegfried Schmauder
Read original paper ↗ ·Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics
Chemical Reviews · 2023
Sabrina Chin-yun Shen, Eesha Khare, Nic Lee, Michael K. Saad, David Lee Kaplan, Markus J. Buehler
Read original paper ↗ ·Machine learning-based beta transus temperature prediction for titanium alloys
Journal of Materials Research and Technology · 2023
Yong Niu, Zhi-qiang Hong, Yaoqi Wang, Yanchun Zhu
Read original paper ↗ ·Perspectives on development of biomedical polymer materials in artificial intelligence age
Journal of Biomaterials Applications · 2023
Shijin Xie
Read original paper ↗ ·The Evolution of Artificial Intelligence in the Digital Economy: An Application of the Potential Dirichlet Allocation Model
Sustainability · 2023
Chunyi Shan, Jun Wang, Yongming Zhu
Read original paper ↗ ·A Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO
Journal of Marine Science and Engineering · 2023
Zhongyan Liu, Jiangtao Mei, Deguo Wang, Yanbao Guo, Lei Wu
Read original paper ↗ ·Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times
Journal of Energy Chemistry · 2023
Xuhao Liu, Zilin Yan, Junwei Wu, Jake Huang, Yifeng Zheng, Neal P. Sullivan, Ryan O’Hayre, Zheng Zhong, Zehua Pan
Read original paper ↗ ·Operator Learning for Predicting Mechanical Response of Hierarchical Composites with Applications of Inverse Design
International Journal of Applied Mechanics · 2023
Chuang Liu, QiangSheng He, Aiguo Zhao, Tao Wu, Zhaoshang Song, Bing Liu, Chuang Feng
Read original paper ↗ ·Perspective: Machine learning in experimental solid mechanics
Journal of the Mechanics and Physics of Solids · 2023
Neal R. Brodnik, Caelin Muir, Nikhil Tulshibagwale, Jeff Rossin, McLean P. Echlin, Craig M. Hamel, Sharlotte Kramer, Tresa M. Pollock, James Doug Kiser, Christian W. Smith, Samantha Hayes Daly
Read original paper ↗ ·Advanced ocean wave energy harvesting: current progress and future trends
Journal of Zhejiang University. Science A · 2023
Fang He, Yibei Liu, Jiapeng Pan, Xinghong Ye, Pengcheng Jiao
Read original paper ↗ ·Data-Efficient Deep Generative Model with Discrete Latent Representation for High-Fidelity Digital Materials
ACS Materials Letters · 2023
Namjung Kim, Dongseok Lee, Youngjoon Hong
Read original paper ↗ ·Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Hybrid Advances · 2023
Christian Emeka Okafor, Sunday Iweriolor, Okwuchukwu Innocent Ani, Shahnawaz Ahmad, Shabana Mehfuz, Godspower Onyekachukwu Ekwueme, Okechukwu Emmanuel Chukwumuanya, Sylvester Emeka Abonyi, Ignatius Echezona Ekengwu, Peter Okechukwu Chikelu
Read original paper ↗ ·Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces
ACS Applied Materials & Interfaces · 2023
Zhen Zhang, Zenan Yang, Zhixi Zhao, Yiyang Liu, Chenchong Wang, Wei Xu
Read original paper ↗ ·An efficiency-driven, correlation-based feature elimination strategy for small datasets
APL Machine Learning · 2023
Carolin A. Rickert, Manuel Henkel, Oliver Lieleg
Read original paper ↗ ·Deep language models for interpretative and predictive materials science
APL Machine Learning · 2023
Yiwen Hu, Markus J. Buehler
Read original paper ↗ ·Perspectives for multiphase mechanical metamaterials
Materials Science and Engineering R Reports · 2023
Yuan Chen, Yiu‐Wing Mai, Lin Ye
Read original paper ↗ ·3D Printability of Silk/Hydroxyapatite Composites for Microprosthetic Applications
ACS Biomaterials Science & Engineering · 2023
Mario Milazzo, Vincent Fitzpatrick, Crystal E. Owens, Igor Matteo Carraretto, Gareth H. McKinley, David Lee Kaplan, Markus J. Buehler
Read original paper ↗ ·Arquitetura para integração e análise de dados baseada no Padrão Indústria 4.0.
Venue not supplied by source · 2023
Vitor Furlan de Oliveira
Read original paper ↗ ·Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation
Composite Structures · 2023
Do-Won Kim, Myeong‐Seok Go, Jae Hyuk Lim, Seung‐Chul Lee
Read original paper ↗ ·An Overview of the Recent Advances in Composite Materials and Artificial Intelligence for Hydrogen Storage Vessels Design
Journal of Composites Science · 2023
Mourad Nachtane, Mostapha Tarfaoui, Mohamed amine Abichou, Alexandre A. Vetcher, Marwane Rouway, Abdeouhaed Aâmir, Habib Mouadili, Houda Laaouidi, Hassan Naanani
Read original paper ↗ ·Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information
Advanced Materials · 2023
Zhenze Yang, Markus J. Buehler
Read original paper ↗ ·Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space
ChemRxiv · 2023
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible, Subramanian Sankaranarayananan
Read original paper ↗ ·Accelerating the design of compositionally complex materials via physics-informed artificial intelligence
Nature Computational Science · 2023
Dierk Raabe, Jaber Rezaei Mianroodi, Jörg Neugebauer
Read original paper ↗ ·Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model
Materials Today · 2023
Andrew James Lew, Markus J. Buehler
Read original paper ↗ ·Machine learning-based optimization design of bistable curved shell structures with variable thickness
Structures · 2023
Junbang Liu, Jinke Chang, Jizhou Yu, Wen‐Hua Zhang, Shiqing Huang
Read original paper ↗ ·Materials Informatics Tools in the Context of Bio-Inspired Material Mechanics
Journal of Applied Mechanics · 2023
Rachel K. Luu, Markus J. Buehler
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 ↗ ·CyRSoXS : a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering
Journal of Applied Crystallography · 2023
Kumar Saurabh, Peter J. Dudenas, Eliot H. Gann, Veronica G. Reynolds, S. Mukherjee, Daniel F. Sunday, Tyler B. Martin, Peter A. Beaucage, Michael L. Chabinyc, Dean M. DeLongchamp, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
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 ↗ ·Synthetic data generation method for data-free knowledge distillation in regression neural networks
Expert Systems with Applications · 2023
Tianxun Zhou, Keng‐Hwee Chiam
Read original paper ↗ ·Deep Learning Approach to the Texture Optimization Problem for Friction Control in Lubricated Contacts
Physical Review Applied · 2023
Alexandre Iury Barbosa da Silva, Veniero Lenzi, Sergey Pyrlin, S. Carvalho, A. Cavaleiro, Luis S. A. Marques
Read original paper ↗ ·A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites
Engineering Fracture Mechanics · 2023
Xinyu Zhang, Tingting Zhao, Yifan Liu, Qingqing Chen, Zhiyong Wang, Zhiyong Wang, Zhihua Wang, Zhihua Wang
Read original paper ↗ ·Composition-based aluminum alloy selection using an artificial neural network
Modelling and Simulation in Materials Science and Engineering · 2023
Jaka Fajar Fatriansyah, Raihan Kenji Rizqillah, Iping Suhariadi, Andreas Federico, Ade Kurniawan
Read original paper ↗ ·