Papers citing A critical examination of robustness and generalizability of machine learning prediction of materials properties
Failure, Uncertainty, and Risk in Materials AI — How Negative Outcomes Are Handled Across the Literature: A Review Study
Journal of Artificial Intelligence for Materials Science · 2022
Ravi Menon, Arjun Nair, Meera Pillai, Suresh Varma
Read original paper ↗ ·Scientific Overconfidence in High-Performing Materials AI Systems
Journal of Artificial Intelligence for Materials Science · 2022
Ahmed Mansour, Omar Saeed
Read original paper ↗ ·A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning
Journal of Artificial Intelligence for Materials Science · 2023
中村博志, Yuta Kato
Read original paper ↗ ·Algorithmic Novelty vs Chemical Novelty: Rethinking Innovation Metrics
Journal of Computational and Data-Driven Materials Engineering · 2023
Lucas Andrade, Mariana Lopes
Read original paper ↗ ·Benchmarking Without Reality: Dataset Construction Bias in Materials Evaluation
Journal of Computational and Data-Driven Materials Engineering · 2023
Wei Liu, Zhang Min
Read original paper ↗ ·Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential
Journal of Computational and Data-Driven Materials Engineering · 2023
Maria Gonzalez, Javier Ruiz, Lucia Torres
Read original paper ↗ ·Data-Driven Materials Engineering: Inverse Design Strategies, Machine Learning Architectures, and Application Domains
Journal of Computational and Data-Driven Materials Engineering · 2023
Ahmed El-Kholy, Nour Abdelrahman, Karim Hassan, Mona Saad
Read original paper ↗ ·Discovery Pipelines as Epistemic Filters: What Computational Workflows Exclude
Journal of Computational and Data-Driven Materials Engineering · 2023
Hiroshi Tanaka, Yuki Sato, Kenji Mori
Read original paper ↗ ·Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics
Journal of Artificial Intelligence for Materials Science · 2023
Fatima Zahra Amrani, Youssef Benali, Samira El-Haddad
Read original paper ↗ ·Uranium and lithium extraction from seawater: challenges and opportunities for a sustainable energy future
Journal of Materials Chemistry A · 2023
Yu Jie Lim, Kunli Goh, Atsushi Goto, Yanli Zhao, Rong Wang
Read original paper ↗ ·ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Applied Physics Reviews · 2023
James M. Rondinelli, Wei Chen
Read original paper ↗ ·An interpretable LSTM deep learning model predicts the time-dependent swelling behavior in CERCER composite fuels
Materials Today Communications · 2023
Yunmei Zhao, Zhenyue Chen, Yiqun Dong, Jingqi Tu
Read original paper ↗ ·Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts
Journal of Chemical Information and Modeling · 2023
Juhwan Noh, Hyunju Chang
Read original paper ↗ ·In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science
Journal of the American Chemical Society · 2023
Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch
Read original paper ↗ ·Building materials genome from ground‐state configuration to engineering advance
Materials Genome Engineering Advances · 2023
Zi‐Kui Liu
Read original paper ↗ ·Exploiting redundancy in large materials datasets for efficient machine learning with less data
Nature Communications · 2023
Kangming Li, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·Multi-principal element alloy discovery using directed energy deposition and machine learning
Materials Science and Engineering A · 2023
Phalgun Nelaturu, Jason R. Hattrick-Simpers, Michael J. Moorehead, Vrishank Jambur, Izabela Szlufarska, Adrien Couet, Dan J. Thoma
Read original paper ↗ ·A call for caution in the era of AI-accelerated materials science
Matter · 2023
Kangming Li, Edward Kim, Yao Fehlis, Daniel Persaud, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·Systems Analysis of Bias and Risk in AI-Enabled Medical Diagnosis
IEEE Symposium Series on Computational Intelligence (SSCI) · 2023
Negin Moghadasi, Misagh Piran, Stephen Baek, Rupa S. Valdez, Michael D. Porter, DeAndre A. Johnson, James H. Lambert
Read original paper ↗ ·A Universal Validation Method for Mitigating Machine Learning Extrapolation Risk
Research Square · 2023
Fangyou Yan, Mengxian Yu, Yin‐Ning Zhou, Qiang Wang
Read original paper ↗ ·Realistic material property prediction using domain adaptation based machine learning
Digital Discovery · 2023
Jeffrey Hu, David Liu, Nihang Fu, Rongzhi Dong
Read original paper ↗ ·A Conceptual Theory of Measurement Validity for AI-Generated Materials Properties
Journal of Artificial Intelligence for Materials Science · 2024
Andreas Müller, Stefan Weber, Julia Hoffmann, Lukas Schneider, Tobias Klein
Read original paper ↗ ·Algorithmic Consensus vs Scientific Consensus in Materials Prediction
Journal of Computational and Data-Driven Materials Engineering · 2024
Sanjay Kulkarni, Meenal Joshi, Rohan Patil
Read original paper ↗ ·Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Journal of Materials Chemistry A · 2024
Kangming Li, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers
Read original paper ↗ ·Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk
Digital Discovery · 2024
Mengxian Yu, Yin‐Ning Zhou, Qiang Wang, Fangyou Yan
Read original paper ↗ ·Representation Drift in Iterative Materials Learning Systems
Journal of Computational and Data-Driven Materials Engineering · 2024
Nguyen Thanh Huy, Pham Quang Minh, Le Thi Bich
Read original paper ↗ ·Unsupervised learning and pattern recognition in alloy design
Digital Discovery · 2024
Ninad Bhat, N. Birbilis, Amanda Susan Barnard
Read original paper ↗ ·Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
npj Computational Materials · 2024
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
Read original paper ↗ ·Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets
Microscopy and Microanalysis · 2024
Katherine Sytwu, Luis Rangel DaCosta, Mary Cooper Scott
Read original paper ↗ ·Efficient Modeling of Composition-Dependent Lattice Constants of Multicomponent Metallic Solid Solutions Based on High-Throughput Experiment
Metallurgical and Materials Transactions A · 2024
Yuanxun Zhou, Jiantao Fan, Biao Wu, Chen Cai, Jianhao Wang, Hongwei Du, Bing Zhao, Lanting T. Zhang, Hong Wang
Read original paper ↗ ·Feature purify: An examination of spurious correlations in high-entropy alloys
Materials & Design · 2024
Yue Pan, Hua Hou, Xiaolong Pei, Yuhong Zhao
Read original paper ↗ ·Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties
Journal of Chemical Information and Modeling · 2024
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
Read original paper ↗ ·Modulating thermal and electrical conductivities in polymers: An approach toward extracting molecular design rules through atomistic simulations
Applied Physics Letters · 2024
Hiroto Yokoyama, Hajime Shimakawa, Akiko Kumada, Masahiro Sato
Read original paper ↗ ·Quantitative predictive theories through integrating quantum, statistical, equilibrium, and nonequilibrium thermodynamics
Journal of Physics Condensed Matter · 2024
Zi‐Kui Liu
Read original paper ↗ ·JARVIS-Leaderboard: a large scale benchmark of materials design methods
npj Computational Materials · 2024
Kamal Kumar Choudhary, Daniel Wines, Kangming Li, Kevin F. Garrity, Vishu Gupta, Aldo Humberto Romero, Jaron T. Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul R. C. Kent, Keqiang Yan, Yuchao Lin, Shuiwang Ji, Ben J. Blaiszik, Patrick Reiser, Pascal Friederich, Ankit Agrawal, Pratyush Tiwary, Eric R. Beyerle, Peter Minch, Trevor David Rhone, Ichiro Takeuchi, Robert B. Wexler, Arun Mannodi‐Kanakkithodi, Elif Ertekin, Avanish Mishra, Nithin Mathew, Mitchell A. Wood, Andrew Rohskopf, Jason R. Hattrick-Simpers, Shih‐Han Wang, Luke E. K. Achenie, Hongliang Xin, Maureen E. Williams, Adam J. Biacchi, Francesca Tavazza
Read original paper ↗ ·Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions
Acta Materialia · 2024
Jeong‐Ah Lee, Roberto B. Figueiredo, Hyojin Park, Jaehoon Kim, Hyoung Seop Kim
Read original paper ↗ ·A Study Towards Machine Learning Prediction of Thermal Conductivity of Polymers Based on Molecular Dynamics
IEEE International Conference on Dielectrics (ICD) · 2024
Hiroto Yokoyama, Hajime Shimakawa, Akiko Kumada, Masahiro Sato
Read original paper ↗ ·Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning
Computational Materials Science · 2024
Mingwei Hu, Qiyang Tan, Ruth Knibbe, Miao Xu, Guofang Liang, Jianxin Zhou, Jun Xu, Bin Jiang, Xue Li, Mahendra Ramajayam, Thomas Dorin, Mingxing Zhang
Read original paper ↗ ·Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
npj Computational Materials · 2024
Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, Ming Hu, Jianjun Hu
Read original paper ↗ ·Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials’ Surfaces
ACS Applied Materials & Interfaces · 2024
Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel Ravanhani Schleder
Read original paper ↗ ·Evaluating external generalizability of machine learning models for recycled aggregate concrete property prediction
Journal of Cleaner Production · 2024
Shreyas Pandurang Jadhav, M. Selvam, Sheik Mohammad Zunain, Nikhil Bugalia
Read original paper ↗ ·The Impact of Domain Knowledge on Universal Machine Learning Models
ChemRxiv · 2024
Lin Wang, Tanjin He, Bin Ouyang
Read original paper ↗ ·Optimization and prediction of dye adsorption utilising cross-linked chitosan-activated charcoal: Response Surface Methodology and machine learning
Journal of Molecular Liquids · 2024
Arun Kumar Shukla, Javed Alam, Santanu Mallik, Janne Ruokolainen, Kavindra Kumar Kesari, Mansour Saleh Alhoshan
Read original paper ↗ ·Machine learning in materials research: Developments over the last decade and challenges for the future
Current Opinion in Solid State and Materials Science · 2024
Anubhav Jain
Read original paper ↗ ·MD-HIT: Machine learning for material property prediction with dataset redundancy control
npj Computational Materials · 2024
Qin Li, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu
Read original paper ↗ ·Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence
Science China Materials · 2024
Ivan A. Kruglov, Liudmila A. Bereznikova, Congwei Xie, Dongdong Chu, Ke Li, Evgenii Tikhonov, Abudukadi Tudi, Arslan Mazitov, Min Zhang, Shilie Pan, Zhihua Yang
Read original paper ↗ ·Machine Learning in 3D and 4D Printing of Polymer Composites: A Review
Polymers · 2024
Ivan Pavlovich Malashin, Igor S. Masich, В С Тынченко, Andrei Gantimurov, Vladimir Nelyub, А. С. Бородулин, Dmitry Martysyuk, A.L. Galinovsky
Read original paper ↗ ·Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning
Sensors · 2024
Vinu Sooriyaarachchi, David John Lary, Lakitha O. H. Wijeratne, John Waczak
Read original paper ↗ ·Impact of data bias on machine learning for crystal compound synthesizability predictions
Machine Learning Science and Technology · 2024
Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei
Read original paper ↗ ·Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy
npj Materials Degradation · 2024
Runze Zhang, Debashish Sur, Kangming Li, Julia Witt, Robert Black, Alexander W. H. Whittingham, John R. Scully, Jason R. Hattrick-Simpers
Read original paper ↗ ·