Papers citing Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface
Accounts of Chemical Research · 2022
Yanchao Wang, Jian Lv, Pengyue Gao, Yanming Ma
Read original paper ↗ ·On the value of popular crystallographic databases for machine learning prediction of space groups
Acta Materialia · 2022
Vishwesh Venkatraman, P.A. Carvalho
Read original paper ↗ ·Prediction of Plasticizer Property Based on an Improved Genetic Algorithm
Polymers · 2022
Yuyin Zhang, Ningjie Deng, Shiding Zhang, Pingping Liu, Changjing Chen, Ziheng Cui, Biqiang Chen, Tianwei Tan
Read original paper ↗ ·Data-driven materials discovery and synthesis using machine learning methods
Elsevier eBooks · 2022
Sterling G. Baird, Marianne Liu, Hasan Muhammad Sayeed, Taylor D. Sparks
Read original paper ↗ ·Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys
Materialia · 2022
Gaurav S. Arora, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet S. Aidhy
Read original paper ↗ ·What is a minimal working example for a self-driving laboratory?
Matter · 2022
Sterling G. Baird, Taylor D. Sparks
Read original paper ↗ ·Convolutional neural network-based colloidal self-assembly state classification
Soft Matter · 2023
Andrés Lizano-Villalobos, Xun Tang
Read original paper ↗ ·Quantifying the performance of machine learning models in materials discovery
Digital Discovery · 2023
Christopher K. H. Borg, Eric S. Muckley, Clara M. Nyby, James E. Saal, Logan T. Ward, Apurva Mehta, Bryce Meredig
Read original paper ↗ ·Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd3
ChemRxiv · 2023
Sangjoon Lee, Clio Chen, Griheydi Garcia, Anton O. Oliynyk
Read original paper ↗ ·On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE
AIAA SciTech Forum · 2023
Sandipp Krishnan Ravi, Anindya Bhaduri, Amer Ahmad, Sayan Ghosh, Liping Wang, Andrew Hoffman, Rajnikant V. Umretiya, Indranil Roy, Raúl B. Rebak, Voramon S. Dheeradhada, Siyeong Ju, Changjie Sun, Patrick Shower, Anteneh Kebbede, Alexander L. Kitt, Lee Kerwin, Luke Mohr, Lang Yuan, Bojun Feng, Subhrajit Roychowdhury
Read original paper ↗ ·Analysis and evaluation of machine learning applications in materials design and discovery
Materials Today Communications · 2023
Mahsa Golmohammadi, Masoud Aryanpour
Read original paper ↗ ·Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics RX
Chemistry of Materials · 2023
Volodymyr Gvozdetskyi, Balaranjan Selvaratnam, Anton O. Oliynyk, Arthur Mar
Read original paper ↗ ·“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design
Tribology International · 2023
Rui Zhou, Rui Zhou, Rui Ma, Luyao Bao, Meirong Cai, Feng Zhou, Weimin Li, Xiaobo Wang
Read original paper ↗ ·Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering
arXiv (Cornell University) · 2023
Maciej P. Polak, Dane D. Morgan
Read original paper ↗ ·Hybrid interatomic potential for Sn
Physical Review Materials · 2023
Mashroor S. Nitol, Khanh Dang, Saryu Jindal Fensin, Michael I. Baskes, Doyl Dickel, Christopher D. Barrett
Read original paper ↗ ·A2BB’X6/ABX3-type High-performance perovskites screening based on ensemble learning and high throughput screening
Solar Energy · 2023
Fang Xu, Zhengxin Chen, Qian Ma, Jiang Wu, Jia Horng Lin, Jiawei Li, Wenhao Li, Chaofan Liu, Hongtao Shen, Linhong You
Read original paper ↗ ·A framework to predict binary liquidus by combining machine learning and CALPHAD assessments
Materials & Design · 2023
Guillaume Deffrennes, Kei Terayama, Taichi Abe, Etsuko Ogamino, Ryo Tamura
Read original paper ↗ ·Interpretable Machine Learning in Solid-State Chemistry, with Applications to Perovskites, Spinels, and Rare-Earth Intermetallics: Finding Descriptors Using Decision Trees
Inorganic Chemistry · 2023
Balaranjan Selvaratnam, Anton O. Oliynyk, Arthur Mar
Read original paper ↗ ·A coarse-grained study on mechanical behaviors of diamond-like carbon based on machine learning
Nanotechnology · 2023
Zhipeng Xiong, Yifeng Yu, Huan Chen, Lichun Bai
Read original paper ↗ ·Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction
Materials & Design · 2023
Fan Yang, Wenyue Zhao, Yi Ru, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu
Read original paper ↗ ·Steels classification by machine learning and Calphad methods
Calphad · 2023
Pavel Yu. Korotaev, Alexey V. Yanilkin
Read original paper ↗ ·Hardness and fracture toughness models by symbolic regression
The European Physical Journal Plus · 2023
Jinbin Zhao, Peitao Liu, Jian-Tao Wang, Jiangxu Li, Haiyang Niu, Yan Ping Sun, Junlin Li, Xing‐Qiu Chen
Read original paper ↗ ·Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites
Advanced Materials · 2023
Wenjie Shang, Minxiang Zeng, A. N. M. Tanvir, Ke Wang, Mortaza Saeidi‐Javash, Alexander W. Dowling, Tengfei Luo, Yanliang Zhang
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 ↗ ·Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning
Chemistry of Materials · 2023
Zixin Zhuang, Amanda Susan Barnard
Read original paper ↗ ·A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films
Integrating materials and manufacturing innovation · 2023
Kimberly L. Bassett, Tylan S Watkins, Jonathan Coleman, Nathan Bianco, Lauren S. Bailey, Jamin Ryan Pillars, S. Garrett Williams, Tomas F. Babuska, John F. Curry, Frank W. DelRio, Amelia A. Henriksen, Anthony P. Garland, Justin Hall, Brandon A. Krick, Brad L. Boyce
Read original paper ↗ ·Quantifying the evolution of mechanical behaviours of concrete materials resting on realistic microstructural characteristics: A micro-to-macro validated combination analysis method
Construction and Building Materials · 2023
Hanxi Jia, Hong-Liang Yi, Guofu Qiao, Peng Han
Read original paper ↗ ·3DSC - a dataset of superconductors including crystal structures
Scientific Data · 2023
Timo Sommer, Roland Willa, Jörg Schmalian, Pascal Friederich
Read original paper ↗ ·Thermal Stability and Mechanical Properties of Hollow Si Nanowires from Atomic Modeling Combined with a Machine-Learning Prediction for Application as Li-Ion Battery Anodes
ACS Applied Nano Materials · 2023
Dandan Zhao, Yue Guan, Zhennan Wu, Lin Zhang
Read original paper ↗ ·Machine learning for structure-property mapping of Ising models: Scalability and limitations
Physical review. E · 2023
Zhongzheng Tian, Sheng Zhang, Gia-Wei Chern
Read original paper ↗ ·A Prompt Engineering Approach for Structured Data Extraction from Unstructured Text Using Conversational LLMs
International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · 2023
Aishwarya Vijayan
Read original paper ↗ ·A Quantum-Classical Method Applied to Material Design: Photochromic Materials Optimization for Photopharmacology Applications
Intelligent Computing · 2024
Qi Gao, Michihiko Sugawara, Paul D. Nation, Takao Kobayashi, Yu‐ya Ohnishi, Hiroyuki Tezuka, Naoki Yamamoto
Read original paper ↗ ·Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)
Chemical Science · 2024
Shi Xuan Leong, Sergio Pablo‐García, Zijian Zhang, Alán Aspuru‐Guzik
Read original paper ↗ ·Flexible, model-agnostic method for materials data extraction from text using general purpose language models
Digital Discovery · 2024
Maciej P. Polak, Shrey Modi, Anna Latosinska, Jinming Zhang, Ching-Wen Wang, Shaonan Wang, Ayan Deep Hazra, Dane D. Morgan
Read original paper ↗ ·Insights into Nanodiamond from Machine Learning
Topics in applied physics · 2024
Amanda Susan Barnard
Read original paper ↗ ·Navigation through high-dimensional chemical space: discovery of Ba 5 Y 13 [SiO 4 ] 8 O 8.5 and Ba 3 Y 2 [Si 2 O 7 ] 2
Chemical Science · 2024
Nataliya L. Gulay, Marco Zanella, Craig M. Robertson, Daniel Ritchie, Manel Sonni, Matthew A. Wright, Jon A. Newnham, Cara J. Hawkins, Jayne Whitworth, Bhupendra P. Mali, Hongjun Niu, Matthew Stephen Dyer, Christopher M. Collins, Matthew J. Rosseinsky, John B. Claridge, Matthew J. Rosseinsky
Read original paper ↗ ·Neural Networks for Prediction of Swelling in Austenitic Steels Irradiated by Fast Neurons
SSRN Electronic Journal · 2024
Pavel Yu. Korotaev, Alexey V. Yanilkin
Read original paper ↗ ·Neural Networks for Prediction of Swelling in Austenitic Steels Irradiated by Fast Neurons
SSRN Electronic Journal · 2024
Pavel Yu. Korotaev, Alexey V. Yanilkin
Read original paper ↗ ·Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening
Journal of Artificial Intelligence for Materials Science · 2024
Daniel Fischer, Laura Meier, Thomas Braun
Read original paper ↗ ·Predicting the oxidation kinetic rate and near-surface microstructural evolution of alumina-forming Ni-based single crystal superalloy based on machine learning
Acta Materialia · 2024
Fan Yang, Wenyue Zhao, Yi Ru, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu
Read original paper ↗ ·Structured information extraction from scientific text with large language models
Nature Communications · 2024
John M. Dagdelen, Alexander Dunn, Sang‐Hoon Lee, Nicholas Walker, Andrew Rosen, Gerbrand Ceder, Kristin Aslaug Persson, Anubhav Jain
Read original paper ↗ ·Informatics-Driven Design of Superhard B–C–O Compounds
ACS Applied Materials & Interfaces · 2024
Madhubanti Mukherjee, Harikrishna Sahu, Mark D. Losego, Will R. Gutekunst, Rampi Ramprasad
Read original paper ↗ ·Extracting accurate materials data from research papers with conversational language models and prompt engineering
Nature Communications · 2024
Maciej P. Polak, Dane D. Morgan
Read original paper ↗ ·Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction
Applied Catalysis A General · 2024
Yaxin Shi, Zhiqin Liang
Read original paper ↗ ·Machine Learning Aided Design and Optimization of Thermal Metamaterials
Chemical Reviews · 2024
Changliang Zhu, Emmanuel Anuoluwa Bamidele, Xiangying Shen, Guimei Zhu, Baowen Li
Read original paper ↗ ·Machine learning for CO 2 capture and conversion: A review
Energy and AI · 2024
Sung Eun Jerng, Yang Jeong Park, Ju Li
Read original paper ↗ ·Classification of battery compounds using structure-free Mendeleev encodings
Journal of Cheminformatics · 2024
Zixin Zhuang, Amanda Susan Barnard
Read original paper ↗ ·Strain sensing characteristics of 3D-printed carbon nanotubes/polypyrrole/UV-curable composites: experimental validation and machine learning predictions
Progress in Additive Manufacturing · 2024
Nutthapong Poompiew, Wiwittawin Sukmas, Chuanchom Aumnate, Allen Jonathan Román, Thiti Bovornratanaraks, Tim Andreas Osswald, Pranut Potiyaraj
Read original paper ↗ ·Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective
Archives of Computational Methods in Engineering · 2024
Chi Wu, Yanan Xu, Jianguang Fang, Qing Li
Read original paper ↗ ·Accurate space-group prediction from composition
Journal of Applied Crystallography · 2024
Vishwesh Venkatraman, P.A. Carvalho
Read original paper ↗ ·