Papers citing Artificial intelligence and machine learning in design of mechanical materials
DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures
MRS Communications · 2022-03-12
Markus J. Buehler
Read original paper ↗ ·Design of artistic graphic symbols based on intelligent guidance marking system
Neural Computing and Applications · 2022-03-13
Yongqiang Guo
Read original paper ↗ ·Machine Learning–Assisted Design of Material Properties
Annual Review of Chemical and Biomolecular Engineering · 2022-03-17
Sanket Kadulkar, Zachary M. Sherman, Venkat Ganesan, Thomas M. Truskett
Read original paper ↗ ·Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods
Journal of the Mechanics and Physics of Solids · 2022-03-19
Xiaolei Chen, Rémi Dingreville, Thiebaud Richeton, Stéphane Berbenni
Read original paper ↗ ·Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input
APL Materials · 2022-04-01
Yu-Chuan Hsu, Zhenze Yang, Markus J. Buehler
Read original paper ↗ ·Mechanical properties and healing efficiency of 3D-printed ABS vascular based self-healing cementitious composite: Experiments and modelling
Engineering Fracture Mechanics · 2022-04-18
Zhi Wan, Yading Xu, Yu Zhang, Shan He, Branko Šavija
Read original paper ↗ ·Computational Acceleration of Topology Optimization Using Parallel Computing and Machine Learning Methods – Analysis of Research Trends
Journal of Industrial Information Integration · 2022-04-25
Yelaman Maksum, Anar Amirli, A. Amangeldi, Medet Inkarbekov, Yixing Ding, Alessandro Romagnoli, Samir Rustamov, Bakytzhan Akhmetov
Read original paper ↗ ·Deep learning accelerated prediction of the permeability of fibrous microstructures
Composites Part A Applied Science and Manufacturing · 2022-04-26
Barış Çağlar, Guillaume Broggi, Muhammad A. Ali, Laurent Orgéas, Veronique J. Michaud
Read original paper ↗ ·Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break
The Journal of Chemical Physics · 2022-05-09
Fiorella Cravero, Mónica F. Díaz, Ignacio Ponzoni
Read original paper ↗ ·Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling
Materials · 2022-05-17
Nikolaos Karathanasopoulos, Dimitrios C. Rodopoulos
Read original paper ↗ ·Jigsaw‐Inspired Modular Architected Materials with Tailorable Stiffness and Programmable Reconfiguration for Adaptive Flow Regulations
Advanced Engineering Materials · 2022-05-23
Pan Liu, Weining Mao, Yiwei Yin, Zhantu Gan, Dongdong Zhao, Nan Hu
Read original paper ↗ ·Analysis of process dependent mechanical properties of sintered copper nanoparticle pillars for the plating-free bumping by finite element method
I-THERM :/I-THERM - Intersociety Conference on Thermal Phenomena in Electronic Systems · 2022-05-31
Sayuri Kohara, Toyohiro Aoki, Chinami Marushima, Christine Taylor, Kuniaki Sueoka, Takashi Hisada
Read original paper ↗ ·A phenomenological framework for modeling of nonlinear mechanical responses in soft network materials with arbitrarily curved microstructures
Extreme Mechanics Letters · 2022-06-01
Shunze Cao, Jun Jiao Wu, Yuchen Lai, Zhangming Shen, Jianxing Liu, Renheng Bo, Jiahui Chang, Zhanyu Wang, Yihui Zhang
Read original paper ↗ ·End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks
Biomedical Engineering Advances · 2022-06-01
Eric L. Buehler, Markus J. Buehler
Read original paper ↗ ·The philosophy of extreme biomimetics
Sustainable materials and technologies · 2022-06-07
Hermann Ehrlich, Marcin Wysokowski, Teofil Jesionowski
Read original paper ↗ ·FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs
Materials Today · 2022-06-09
Markus J. Buehler
Read original paper ↗ ·Automated Design of Li + ‐Conducting Polymer by Quantum‐Inspired Annealing
Macromolecular Rapid Communications · 2022-06-27
Kan Hatakeyama‐Sato, Hiroki Adachi, Momoka Umeki, Takahiro Kashikawa, Kôichi Kimura, Kenichi Oyaizu
Read original paper ↗ ·Machine learning assisted wrinkling design of hierarchical thin sheets
Computational Materials Science · 2022-07-08
Xinghan Qiu, Yue Yin, Jiawei Zhang, Haotian Wang, Huifeng Tan, Yuanpeng Liu, Changguo Wang
Read original paper ↗ ·Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials
Communications Materials · 2022-07-09
H. Pahlavani, Muhamad Amani, Mauricio Cruz Saldívar, Jie Zhou, Mohammad Javad Mirzaali, AMIR ABBAS ZADPOOR
Read original paper ↗ ·A review on Machine learning aspect in physics and mechanics of glasses
Materials Science and Engineering B · 2022-07-13
Jashanpreet Singh, Simranjit Singh
Read original paper ↗ ·Nacre-like composites with superior specific damping performance
Proceedings of the National Academy of Sciences · 2022-07-25
Wilhelm Woigk, Erik Poloni, Madeleine Grossman, Florian Bouville, Kunal Masania, André R. Studart
Read original paper ↗ ·High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning
Small Methods · 2022-07-29
Zhenze Yang, Markus J. Buehler
Read original paper ↗ ·Nonlinear coarse-graining models for 3D printed multi-material biomimetic composites
Additive manufacturing · 2022-07-30
Mauricio Cruz Saldívar, Eugeni L. Doubrovski, Mohammad Javad Mirzaali, AMIR ABBAS ZADPOOR
Read original paper ↗ ·Overview of Methods and Software for the Design of Functionally Graded Lattice Structures
Advanced Engineering Materials · 2022-08-06
Fernando Veloso, João Gomes Fonseca, Pedro Morais, Jorge Correia‐Pinto, A. Pinho, João L. Vilaça
Read original paper ↗ ·Knowledge database creation for design of polymer matrix composite
Computational Materials Science · 2022-08-10
Hannah Huang, Satyajit Mojumder, Derick Suarez, Abdullah Al Amin, Mark Fleming, Wing Kam Liu
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 ↗ ·Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning
Advanced Theory and Simulations · 2022-08-17
Chi‐Hua Yu, Bor‐Yann Tseng, Zhenze Yang, Cheng‐Che Tung, Elena Zhao, Zhi‐Fan Ren, Shengsheng Yu, Po‐Yu Chen, Chuin‐Shan Chen, Markus J. Buehler
Read original paper ↗ ·Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks
Journal of Applied Physics · 2022-08-17
Wei Lu, Zhenze Yang, Markus J. Buehler
Read original paper ↗ ·A Review of Performance Prediction Based on Machine Learning in Materials Science
Nanomaterials · 2022-08-26
Ziyang Fu, Weiyi Liu, Chen Shi. Huang, Tao Mei
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 ↗ ·Transformation field analysis and clustering discretization method in pyridynamic micromechanics of composites
Research Square · 2022-09-16
Valeriy A. Buryachenko
Read original paper ↗ ·Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
npj Computational Materials · 2022-09-17
Zhenze Yang, Markus J. Buehler
Read original paper ↗ ·Al-Sn-Al Bonding Strength Investigation Based on Deep Learning Model
Processes · 2022-09-20
Jiang Min, Min Yu, Bao Li, Hongze Zhang, Zhiyuan Zhu
Read original paper ↗ ·Interpreting the Strength Activity Index of Fly Ash with Machine Learning
Advances in Civil Engineering Materials · 2022-09-20
Yu Jin Song, Steven L. Zhang, Kaixin Wang, Jin Chen, Gaurav Sant, Mathieu Bauchy
Read original paper ↗ ·Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites
Defence Technology · 2022-09-26
Sristi Gupta, Tanmoy Mukhopadhyay, Vinod Kushvaha
Read original paper ↗ ·Multifunctional 1D Nanostructures toward Future Batteries: A Comprehensive Review
Advanced Functional Materials · 2022-09-30
Jun Young Cheong, Su‐Ho Cho, Jiyoung Lee, Ji‐Won Jung, Chanhoon Kim, Il‐Doo Kim
Read original paper ↗ ·On the use of artificial neural networks in topology optimisation
Structural and Multidisciplinary Optimization · 2022-10-01
Rebekka V. Woldseth, Niels Aage, Jakob Andreas Bærentzen, Ole Sigmund
Read original paper ↗ ·Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science
Materials & Design · 2022-10-05
Costas A. Charitidis, Marco Sebastiani, Gerhard Goldbeck
Read original paper ↗ ·High-efficient low-cost characterization of composite material properties using domain-knowledge-guided self-supervised learning
Computational Materials Science · 2022-10-06
Binglin Xie, Xianhua Yao, Weining Mao, Mohammad Hossein Rafiei, Nan Hu
Read original paper ↗ ·Deep learning for predicting the thermomechanical behavior of shape memory polymers
Polymer · 2022-10-10
Diego Segura Ibarra, Jacob Mathews, Fan Li, Hongfang Lü, Guoqiang Li, Jinyuan Chen
Read original paper ↗ ·Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model
Journal of the Mechanics and Physics of Solids · 2022-10-12
Zhenze Yang, Yu-Chuan Hsu, Markus J. Buehler
Read original paper ↗ ·High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning
Journal of Materials Research and Technology · 2022-10-14
Zijun Qin, Weifu Li, Zi Wang, Junlong Pan, Zexin Wang, Zihang Li, Guowei Wang, Jun Pan, Feng Liu, Lan Huang, Liming Tan, Lina Zhang, Hua Han, Hong Chen, Liang Jiang
Read original paper ↗ ·Extraction of the plastic properties of metallic materials from scratch tests using deep learning
Mechanics of Materials · 2022-10-22
Jianwei Zhang, Jinhong Qin, Yuan‐Xin Li, Chunsheng Lu, Haitao Liu, MingHao Zhao
Read original paper ↗ ·Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics
ACS Applied Nano Materials · 2022-10-26
Prottay Malakar, Md Shajedul Hoque Thakur, Shahriar Muhammad Nahid, Md Mahbubul Islam
Read original paper ↗ ·Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels
Applied Sciences · 2022-10-26
Yasin Shokrollahi, Pengfei Dong, Peshala Thibbotuwawa Gamage, Nashaita Y. Patrawalla, Vipuil Kishore, Hozhabr Mozafari, Linxia Gu
Read original paper ↗ ·An automated biomateriomics platform for sustainable programmable materials discovery
Matter · 2022-11-01
Nic Lee, Sabrina Chin-yun Shen, Markus J. Buehler
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 ↗ ·FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames
Computer-Aided Design · 2022-11-11
Lyle Regenwetter, Colin Weaver, Faez Ahmed
Read original paper ↗ ·Multiscale Modeling at the Interface of Molecular Mechanics and Natural Language through Attention Neural Networks
Accounts of Chemical Research · 2022-11-15
Markus J. Buehler
Read original paper ↗ ·AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · 2022-11-17
C. Fanelli, Z. Papandreou, K. Suresh, J. Kevin Adkins, Y. Akiba, A. Albataineh, M. J. Amaryan, I. C. Arsene, C. Ayerbe Gayoso, J. Bae, X. Bai, M. D. Baker, M. Bashkanov, R. Bellwied, F. Benmokhtar, В. В. Бердников, J. C. Bernauer, F. Bock, Werner U. Boeglin, M. Borysova, E. J. Brash, Paul D. Brindza, W. J. Briscoe, M. Brooks, S. Bueltmann, M. H. S. Bukhari, A. Bylinkin, R. Capobianco, W. C. Chang, Yoo-Lim Cheon, K. Chen, K.-F. Chen, K. y. Cheng, M. Chiu, T. Chujo, Z. H. Citron, Ethan W. Cline, E.Ø. Cohen, T. M. Cormier, Yasser Corrales Morales, Cameron Cotton, J. Crafts, C. Crawford, S. Creekmore, C. Cuevas, J. Cunningham, G. David, Cameron Thomas Dean, M. Demarteau, S. Diehl, N. Doshita, Raphaël Dupré, J.M Durham, R. Dzhygadlo, R. J. Ehlers, Lamiaa El Fassi, A. Emmert, R. Ent, R. Fatemi, S. Fegan, М. Фингер, M. Finger, J. E. Frantz, M. Friedman, Ivica Friščić, D. Gangadharan, Susan V Gardner, K. Gates, F. J. M. Geurts, R. Gilman, D. I. Glazier, E. Glimos, Yuji Goto, N. Grau, S.V. Greene, A. Q. Guo, Lingzhong Guo, S.K. Ha, John S. Haggerty, T. B. Hayward, X. He, O. Hen, Douglas W. Higinbotham, M. Hoballah, Tanja Horn, A. Hoghmrtsyan, P.-h.J. Hsu, J. Huang, Garth M. Huber, A. Hutson, K.Y. Hwang, C. E. Hyde-Wright, M. Inaba, T. Iwata, H. S. Jo, K. Joo, Narbe Kalantarians, G. Kalicy, K. Kawade, S. Kay
Read original paper ↗ ·