Papers citing Deep materials informatics: Applications of deep learning in materials science
Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology
Wireless Communications and Mobile Computing · 2022
Haifeng Zhang, Lianzhu Zhou
Read original paper ↗ ·Featurization strategies for polymer sequence or composition design by machine learning
Molecular Systems Design & Engineering · 2022
Roshan A. Patel, Carlos H. Borca, Michael A. Webb
Read original paper ↗ ·Materials property prediction using feature selection based machine learning technique
Materials Today Proceedings · 2022
K. Valli Priyadharshini, A. Vijay, K. Swaminathan, T. Avudaiappan, V Banupriya
Read original paper ↗ ·Prediction of material property using optimized augmented graph-attention layer in GNN
Materials Today Proceedings · 2022
V. Sathana, M. Mathumathi, K. Makanyadevi
Read original paper ↗ ·Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study
Journal of Artificial Intelligence for Materials Science · 2022
Daniel Fischer, Laura Meier, Thomas Braun, Stefan Koch, Felix Roth
Read original paper ↗ ·Fatigue life prediction of aluminum alloy via knowledge-based machine learning
International Journal of Fatigue · 2022
Zhengheng Lian, Minjie Li, Wencong Lu
Read original paper ↗ ·Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis
ACS Omega · 2022
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily L. Robertson, Donald Loveland, Xiaoting Zhong, T. Yong-Jin Han
Read original paper ↗ ·Machine learning in nuclear materials research
Current Opinion in Solid State and Materials Science · 2022
Dane D. Morgan, Ghanshyam Pilania, Adrien Couet, Blas Pedro Uberuaga, Cheng Sun, Ju Li
Read original paper ↗ ·Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids
physica status solidi (b) · 2022
Taruto Atsumi, Kosei Sato, Yudai Yamaguchi, Masato Hamaie, Risa Yasuda, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ichiro Takeuchi
Read original paper ↗ ·Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating
Journal of Mechanical Design · 2022
Zihan Wang, Weikang Xian, Mohamed Ridha Baccouche, Horst Lanzerath, Ying Li, Hongyi Xu
Read original paper ↗ ·On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems
International Communications in Heat and Mass Transfer · 2022
Hassan Bararnia, Mehdi Esmaeilpour
Read original paper ↗ ·Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations
Scientific Data · 2022
Fancy Qian Wang, Kamal Kumar Choudhary, Yu Liu, Jianjun Hu, Ming Hu
Read original paper ↗ ·A deep learning-aided prediction approach for creep rupture time of Fe–Cr–Ni heat-resistant alloys by integrating textual and visual features
Journal of Materials Research and Technology · 2022
Shulin Xiang, Xuedong Chen, Zhichao Fan, Tao Chen, Xiaoming Lian
Read original paper ↗ ·Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides
Journal of Physics Condensed Matter · 2022
Armin Sahinovic, Benjamin Geisler
Read original paper ↗ ·Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions
Computational Materials Science · 2022
Karen J. DeMille, Ashley D. Spear
Read original paper ↗ ·Casting Defects Detection in Aluminum Alloys Using Deep Learning: a Classification Approach
International Journal of Metalcasting · 2022
Filip Nikolić, Ivan Štajduhar, Marko Čanađija
Read original paper ↗ ·Deep learning approach for chemistry and processing history prediction from materials microstructure
Scientific Reports · 2022
Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Read original paper ↗ ·Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes
npj Computational Materials · 2022
Dan Guevarra, Lan Zhou, Matthias Hermann Richter, Aniketa Shinde, Di Chen, Carla Pedro Gomes, John M. Gregoire
Read original paper ↗ ·Recent advances and applications of deep learning methods in materials science
npj Computational Materials · 2022
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 ↗ ·Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures
Integrating materials and manufacturing innovation · 2022
Mahmudul Hasan, Yuwei Mao, Kamal Kumar Choudhary, Francesca Tavazza, Alok Choudhary, Ankit Agrawal, Pınar Acar
Read original paper ↗ ·Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy
Engineering Technology & Applied Science Research · 2022
Dhia Kadhm Suker
Read original paper ↗ ·Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs
Cell Reports Physical Science · 2022
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 ↗ ·Stimulus: Accelerate Data Management for Scientific AI applications in HPC
IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · 2022
Hariharan Devarajan, Anthony Kougkas, Huihuo Zheng, Venkatram Vishwanath, Xian‐He Sun
Read original paper ↗ ·Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications
IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · 2022
Kewei Wang, Sunwoo Lee, J. Balewski, Alex Sim, P. Nugent, Ankit Agrawal, Alok Choudhary, Kesheng John Wu, Wei‐keng Liao
Read original paper ↗ ·A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials
Computational Materials Science · 2022
Lijun Shen, Quan Qian
Read original paper ↗ ·Deep learning object detection in materials science: Current state and future directions
Computational Materials Science · 2022
Ryan M. Jacobs
Read original paper ↗ ·Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data
Materials & Design · 2022
Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Read original paper ↗ ·Materials informatics for dielectric loss tangent in the millimeter wave region
Japanese Journal of Applied Physics · 2022
Yusuke Oba, Yuji Umeda, Daiki Ishii
Read original paper ↗ ·Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Nature Communications · 2022
Charlotte Loh, Thomas Christensen, Rumen Dangovski, Samuel Kim, Marin Soljačić
Read original paper ↗ ·Artificial Intelligence‐Based Material Discovery for Clean Energy Future
Advanced Intelligent Systems · 2022
Reza Maleki, Mohsen Asadnia, Amir Razmjou
Read original paper ↗ ·End-to-end ancient ceramic classification toolkit based on deep learning: A case study of black glazed wares of Jian kilns (Song Dynasty, Fujian province)
Ceramics International · 2022
Yong Feng Qi, Meng-Zhe Qiu, Haizhao Jing, Zhi-Qi Wang, Cheng-Long Yu, Jianfeng Zhu, Fen Wang, Tian Wang
Read original paper ↗ ·Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime
Journal of Chemical Information and Modeling · 2022
Chuan Li, Chenghui Wang, Ming Sun, Yan Qi Zeng, Yuan Yuan, Qiaolin Gou, Guangchuan Wang, Yanzhi Guo, Xuemei Pu
Read original paper ↗ ·Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys
npj Materials Degradation · 2022
Kasturi Narasimha Sasidhar, Nima Hamidi Siboni, Jaber Rezaei Mianroodi, Michael Rohwerder, Jörg Neugebauer, Dierk Raabe
Read original paper ↗ ·A machine learning approach to predict the structural and magnetic properties of Heusler alloy families
Computational Materials Science · 2022
Srimanta Mitra, Aquil Ahmad, Sajib Biswas, A. K. Das
Read original paper ↗ ·A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures
Journal of Intelligent Manufacturing · 2022
Yuwei Mao, Hui Zhi Lin, Christina Xuan Yu, Roger Frye, Darren Beckett, Kevin Anderson, Lars Jacquemetton, Fred Carter, Zhangyuan Gao, Wei‐keng Liao, Alok Choudhary, Kornel F. Ehmann, Ankit Agrawal
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
Igor I. Baskin, Yair Ein‐Eli
Read original paper ↗ ·Estimation of surface roughness in selective laser sintering using computational models
The International Journal of Advanced Manufacturing Technology · 2022
Ebubekir Koç, Sultan Zeybek, Burçin Özbay Kısasöz, Cemal İrfan Çalışkan, Mustafa Enes Bulduk
Read original paper ↗ ·Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design
Integrating materials and manufacturing innovation · 2022
Yuwei Mao, Zijiang Yang, Dipendra Jha, Arindam Paul, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·Deep Learning of Microstructures
Venue not supplied by source · 2022
Amir Abbas Kazemzadeh Farizhandi
Read original paper ↗ ·To Shuffle or Not To Shuffle: Mini-Batch Shuffling Strategies for Multi-class Imbalanced Classification
International Conference on Computational Science and Computational Intelligence (CSCI) · 2022
Yuwei Mao, Vishu Gupta, Kewei Wang, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
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
Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion
Computational Mechanics · 2022
Rainer Niekamp, Johanna Niemann, Jörg Schröder
Read original paper ↗ ·Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data
Energy Advances · 2023
Haruka Tobita, Yuki Namiuchi, Takumi Komura, Hiroaki Imai, Koki Obinata, Masato Okada, Yasuhiko Igarashi, Yuya Oaki
Read original paper ↗ ·Interpretable models for extrapolation in scientific machine learning
Digital Discovery · 2023
Eric S. Muckley, James E. Saal, Bryce Meredig, Christopher S. Roper, John H. Martin
Read original paper ↗ ·Supervised machine learning techniques to protect IoT healthcare environment against cyberattacks
Elsevier eBooks · 2023
Sanaa Kaddoura, Amal El Arid, Auday Al-Dulaimy
Read original paper ↗ ·Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021
ChemEngineering · 2023
Aníbal Alviz-Meza, Juan Orozco-Agamez, Diana Carolina Parada Quinayá, Antistio Alviz Amador
Read original paper ↗ ·Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning
ACS Applied Bio Materials · 2023
Roshan A. Patel, Michael A. Webb
Read original paper ↗ ·Discrete learning-based intelligent methodology for heart disease diagnosis
Biomedical Signal Processing and Control · 2023
Mehdi Khashei, Negar Bakhtiarvand
Read original paper ↗ ·A surrogate model for the prediction of permeabilities and flow through porous media
PAMM · 2023
Rainer Niekamp, Johanna Niemann, Jörg Schröder
Read original paper ↗ ·AtomVision: A Machine Vision Library for Atomistic Images
Journal of Chemical Information and Modeling · 2023
Kamal Kumar Choudhary, Ramya L. Gurunathan, Brian DeCost, Adam J. Biacchi
Read original paper ↗ ·