Papers citing Deep materials informatics: Applications of deep learning in materials science
Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra
Journal of Materials Science · 2023
Jaimie Greasley, Patrick A. Hosein
Read original paper ↗ ·Predicting thermoelectric transport properties from composition with attention-based deep learning
Machine Learning Science and Technology · 2023
Luis M. Antunes, Keith T. Butler, Ricardo Grau‐Crespo
Read original paper ↗ ·An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime
Tribology International · 2023
Sutao Han, Samir Khatir, Can Wang, Magd M. Abdel Wahab
Read original paper ↗ ·Evaluating the transferability of machine-learned force fields for material property modeling
Computer Physics Communications · 2023
Shaswat Mohanty, SangHyuk Yoo, Keonwook Kang, Wei Cai
Read original paper ↗ ·MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction
Journal of Chemical Information and Modeling · 2023
Vishu Gupta, Kamal Kumar Choudhary, Yuwei Mao, Kewei Wang, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model
The Journal of Physical Chemistry A · 2023
Siwei Song, Yi Wang, Xiaolan Tian, Wei He, Fang Chen, Junnan Wu, Qinghua Zhang
Read original paper ↗ ·Prediction of compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method
Mechanics of Materials · 2023
Weimin Zhuang, Enming Wang, Hailun Zhang
Read original paper ↗ ·A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation
Engineering With Computers · 2023
Jinlong Fu, Min Wang, Bin Chen, Jinsheng Wang, Dunhui Xiao, Min Luo, Ben J. Evans
Read original paper ↗ ·Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image
Physical review. E · 2023
Juan Li, Qizhi Teng, Ningning Zhang, Honggang Chen, Xiaohai He
Read original paper ↗ ·Improving deep learning model performance under parametric constraints for materials informatics applications
Scientific Reports · 2023
Vishu Gupta, Alec Peltekian, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·An optimization-based supervised learning algorithm for PXRD phase fraction estimation
Materials Today Communications · 2023
Patrick A. Hosein, Jaimie Greasley
Read original paper ↗ ·A unified microstructure segmentation approach via human-in-the-loop machine learning
Acta Materialia · 2023
Juwon Na, Se‐Jong Kim, Heekyu Kim, Seong-Hoon Kang, Seung‐Chul Lee
Read original paper ↗ ·AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations
Venue not supplied by source · 2023
Yuwei Mao, Shahriyar Keshavarz, Vishu Gupta, Andrew C. E. Reid, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
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 ↗ ·An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
npj Computational Materials · 2023
Yuwei Mao, Mahmudul Hasan, Arindam Paul, Vishu Gupta, Kamal Kumar Choudhary, Francesca Tavazza, Wei‐keng Liao, Alok Choudhary, Pınar Acar, Ankit Agrawal
Read original paper ↗ ·Investigating the impact of data scaling on the k-nearest neighbor algorithm
Computer Science and Information Technologies · 2023
Muasir Pagan, Muhammad Zarlis, Ade Candra
Read original paper ↗ ·Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network
Energy and AI · 2023
Joshua Ojih, Alejandro David Rodriguez, Jianjun Hu, Ming Hu
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 Novel Discrete Deep Learning–Based Cancer Classification Methodology
Cognitive Computation · 2023
Marzieh Soltani, Mehdi Khashei, Negar Bakhtiarvand
Read original paper ↗ ·Deep learning modeling in microscopy imaging: A review of materials science applications
Progress in Materials Science · 2023
Marco Ragone, Reza Shahabazian-Yassar, Farzad Mashayek, Vitaliy Yurkiv
Read original paper ↗ ·Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach
Agriculture · 2023
Munmi Gogoi, Vikash Kumar, Shahin Ara Begum, Neelesh Sharma, Surya Kant
Read original paper ↗ ·Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range
APL Materials · 2023
Nuttawat Parse, Supree Pinitsoontorn
Read original paper ↗ ·Physics-based Data-Augmented Deep Learning for Enhanced Autogenous Shrinkage Prediction on Experimental Dataset
Venue not supplied by source · 2023
Vishu Gupta, Yuhui Lyu, Derick Suarez, Yuwei Mao, Wei‐keng Liao, Alok Choudhary, Wing Kam Liu, Gianluca Cusatis, Ankit Agrawal
Read original paper ↗ ·Enhancing corrosion-resistant alloy design through natural language processing and deep learning
Science Advances · 2023
Kasturi Narasimha Sasidhar, Nima Hamidi Siboni, Jaber Rezaei Mianroodi, Michael Rohwerder, Jörg Neugebauer, Dierk Raabe
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 ↗ ·Predicting fatigue crack growth metrics from fractographs: Towards fractography by computer vision
International Journal of Fatigue · 2023
Katelyn Jones, William D. Musinski, Adam L. Pilchak, Reji John, Paul A. Shade, Anthony D. Rollett, Elizabeth A. Holm
Read original paper ↗ ·Application of Machine Learning in Material Synthesis and Property Prediction
Materials · 2023
Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie
Read original paper ↗ ·Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Journal of Composites Science · 2023
Fasikaw Kibrete, Tomasz Trzepieciński, Hailu Shimels Gebremedhen, Dereje Engida Woldemichael
Read original paper ↗ ·Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations
Results in Materials · 2023
Desmond Edem Primus Klenam, Tabiri Kwayie Asumadu, Mobin Vandadi, Nima Rahbar, Fred McBagonluri, W. O. Soboyejo
Read original paper ↗ ·Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors
Computational and Theoretical Chemistry · 2023
Muhamad Akrom, Supriadi Rustad, Adhitya Gandaryus Saputro, Hermawan Kresno Dipojono
Read original paper ↗ ·Nonlinear probabilistic virtual sample generation using Gaussian process latent variable model and fitting for rubber material
Computational Materials Science · 2023
Wenlong Chen, Kai Chen, Kai Chen
Read original paper ↗ ·A data generation approach for surrogate models for magneto‐static simulations
PAMM · 2023
Rainer Niekamp, Johanna Niemann, Maximilian Reichel, Jörg Schröder
Read original paper ↗ ·Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors
Results in Chemistry · 2023
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Read original paper ↗ ·Thermal diffusion in discontinuous media: A hybrid peridynamics-based machine learning model
Computers & Structures · 2023
J. Ramesh Babu, S. Gopalakrishanan
Read original paper ↗ ·Construction of automated optimal control systems with elements of artificial intelligence
IAES International Journal of Robotics and Automation (IJRA) · 2023
Khu Ven–Tsen, Zhanat Rysbayevna Umarova, Pernekul Kozhabekova, Nabat Suieuova
Read original paper ↗ ·Superior Prediction of Graphene Nanoflake Properties withUnbiased Graph Embedding
ChemRxiv · 2023
Nathan Hu, Amanda J. Parker
Read original paper ↗ ·Computational materials discovery and development for Li and non-Li advanced battery chemistries
Journal of Electrochemical Science and Engineering · 2023
Henu Sharma, Aqsa Nazir, Arvind Kasbe, Prathamesh Ganesh Kekarjawlekar, Kajari Chatterjee, Saeme Motevalian, Ana G. Claus, Viswesh Prakash, Sagnik Acharya, Kisor Kumar Sahu
Read original paper ↗ ·Analysis of the Possibility of Using Selected Tools and Algorithms in the Classification and Recognition of Type of Microstructure
Materials · 2023
Michał Szatkowski, Dorota Wilk-Kołodziejczyk, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka, Mirosław Głowacki
Read original paper ↗ ·Examining the microstructure, morphological features, and wetting characteristics of Ti/TiN/TiAlN thin films produced through RF/DC magnetron co-sputtering
Materials Today Communications · 2023
Hichem Benzouid, Said Boudebane, Hana Ferkous, Fouaz Lekoui, Nafarizal Nayan, Amel Delimi, Maha Awjan Alreshidi, Muhammad Arshad, Krishna Kumar Yadav, Barbara Ernst, Noureddine Ali Elboughdiri, Yacine Benguerba
Read original paper ↗ ·Advances in materials informatics for tailoring thermal radiation: A perspective review
Next Energy · 2023
Jiang Guo, Junichiro Shiomi
Read original paper ↗ ·Aprendizaje automático aplicado a la caracterización de nanoestructuras base carbono
Venue not supplied by source · 2023
Luis Enrique Vivanco-Benavides, División de Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolina, Unidad Azcapotzlco., Cecilia Mercado Zúñiga
Read original paper ↗ ·Size-Dependent Elastic Properties of Nanofilms: Exponentially Decreased Surface Elasticity Model
Mechanics of Solids · 2023
Jiangang Li, Xiao Lei, Huihui Xu, Zhixiang Gao, Hua Wang
Read original paper ↗ ·A Deep Learning Framework for Time-Series Processing-Microstructure-Property Prediction
International Conference on Machine Learning and Applications · 2023
Yuwei Mao, Mahmudul Hasan, Claire Lee, Muhammed Nur Talha Kilic, Vishu Gupta, Wei‐keng Liao, Alok Choudhary, Pınar Acar, Ankit Agrawal
Read original paper ↗ ·Models Supporting Cast Iron Production Based on Artificial Intelligence and Prediction Methods
Intelligent systems reference library · 2024
Dorota Wilk-Kołodziejczyk, Krzysztof Jaśkowiec, Marcin Małysza, Adam Bitka
Read original paper ↗ ·Predicting Grain Orientations of 316 Stainless Steel Using Convolutional Neural Networks
Intelligent Automation & Soft Computing · 2024
Dhia Kadhm Suker, Ahmed R. Abdo, Khalid Abdulkhaliq M. Alharbi
Read original paper ↗ ·Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
npj Computational Materials · 2024
Vishu Gupta, Kamal Kumar Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal
Read original paper ↗ ·Advances in materials informatics: a review
Journal of Materials Science · 2024
Dawn Sivan, K. Satheesh Kumar, Aziman Abdullah, Veena Raj, Izan Izwan Misnon, Seeram Ramakrishna, Rajan Jose
Read original paper ↗ ·Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances
Science and Technology of Advanced Materials Methods · 2024
Khurram Shahzad, Andrei Ionut Mardare, Achim Walter Hassel
Read original paper ↗ ·Machine Learning Design of Perovskite Catalytic Properties
Advanced Energy Materials · 2024
Ryan M. Jacobs, Jian Liu, Harry Abernathy, Dane D. Morgan
Read original paper ↗ ·Augmentation of scarce data—A new approach for deep-learning modeling of composites
Composites Science and Technology · 2024
Hon Lam Cheung, Petter Uvdal, Mohsen Mirkhalaf
Read original paper ↗ ·