Papers citing Machine learning material properties from the periodic table using convolutional neural networks
Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties
Modern Physics · 2019
果 卢
Read original paper ↗ ·Formal structure of periodic system of elements
MPG.PuRe (Max Planck Society) · 2019
Leal, W., Restrepo, G.
Read original paper ↗ ·Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Journal of Chemical Theory and Computation · 2019
Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Formal structure of periodic system of elements
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2019
Wilmer Leal, Guillermo Restrepo
Read original paper ↗ ·Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
Computational Mechanics · 2019
Guorong Chen, Tiange Li, Qijun Chen, Shaofei Ren, Chao Wang, Shaofan Li
Read original paper ↗ ·Recent advances and applications of machine learning in solid-state materials science
npj Computational Materials · 2019
Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques
Read original paper ↗ ·Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation
Computational Materials Science · 2019
Zheng Xiong, Yuxin Cui, Zhonghao Liu, Yong Zhao, Ming Hu, Jianjun Hu
Read original paper ↗ ·Challenges for the Periodic Systems of Elements: Chemical, Historical and Mathematical Perspectives
Chemistry - A European Journal · 2019
Guillermo Restrepo
Read original paper ↗ ·Multi-channel convolutional neural networks for materials properties prediction
Computational Materials Science · 2019
Xiaolong Zheng, Peng Zheng, Liang Zheng, Yang Zhang, Ruizhi Zhang
Read original paper ↗ ·Advances in optical and electrochemical techniques for biomedical imaging
Chemical Science · 2020
Yi‐Tao Long, Thomas J. Meade
Read original paper ↗ ·In silico investigation of Cu(In,Ga)Se 2 -based solar cells
Physical Chemistry Chemical Physics · 2020
Hossein Mirhosseini, Ramya Kormath Madam Raghupathy, Sudhir Kumar Sahoo, Hendrik Wiebeler, Manjusha Chugh, Thomas D. Kühne
Read original paper ↗ ·A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns
Nature Communications · 2020
Jin-Woong Lee, Woon Bae Park, Jin Hee Lee, Satendra Pal Singh, Kee‐Sun Sohn
Read original paper ↗ ·Searching high spin polarization ferromagnet in Heusler alloy via machine learning
Journal of Physics Condensed Matter · 2020
Xiao Hu, Yaqiong Zhang, Shuaiyu Fan, Xin Li, Zhenjie Zhao, Chao He, Yong-Hong Zhao, Yong Liu, Wenhui Xie
Read original paper ↗ ·A Critical Review of Machine Learning of Energy Materials
Advanced Energy Materials · 2020
Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong
Read original paper ↗ ·Machine learning for interatomic potential models
The Journal of Chemical Physics · 2020
Tim Mueller, Alberto Hernández, Chuhong Wang
Read original paper ↗ ·Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network
Journal of Chemical Information and Modeling · 2020
Balaranjan Selvaratnam, Ranjit T. Koodali, Pere Miró
Read original paper ↗ ·Gallium–Boron–Phosphide ($$\hbox {GaBP}_{2}$$): a new III–V semiconductor for photovoltaics
Journal of Materials Science · 2020
Upendra Kumar, Sanjay Nayak, Soubhik Chakrabarty, Satadeep Bhattacharjee, Seung-Cheol Lee
Read original paper ↗ ·Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis
Matter · 2020
Sogol Lotfi, Ziyan Zhang, Gayatri Viswanathan, Kaitlyn Fortenberry, Aria Mansouri Tehrani, Jakoah Brgoch
Read original paper ↗ ·Machine-Learning Assisted Screening of Energetic Materials
The Journal of Physical Chemistry A · 2020
Peng Chao Kang, Zhongli Liu, Hakima Abou‐Rachid, Hong Guo
Read original paper ↗ ·Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Chemical Reviews · 2020
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
Read original paper ↗ ·Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data.
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020
John T. Tencer, Kevin Potter
Read original paper ↗ ·Experimental inspection of a computationally-designed NiCrMnSi Heusler alloy with high Curie temperature
Japanese Journal of Applied Physics · 2020
Y. Onodera, Kelvin Elphick, T. Kanemura, Tufan Roy, Taku Tsuchiya, Masahito Tsujikawa, Kenta Yoshida, Yasuyoshi Nagai, Shigemi Mizukami, Atsufumi Hirohata, Masafumi Shirai
Read original paper ↗ ·Emerging materials intelligence ecosystems propelled by machine learning
Nature Reviews Materials · 2020
Rohit Batra, Le Song, Rampi Ramprasad
Read original paper ↗ ·Exploiting deep learning for predictable carbon dot design
Chemical Communications · 2020
Xiaoyuan Wang, Binbin Chen, Jie Zhang, Ze‐Rui Zhou, Jian Lv, Peter X. Geng, Ruo‐Can Qian
Read original paper ↗ ·A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations
SIAM Journal on Scientific Computing · 2021
John T. Tencer, Kevin Potter
Read original paper ↗ ·Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning
Nanoscale · 2021
Yilei Wu, Shuaihua Lu, Ming‐Gang Ju, Qionghua Zhou, Jinlan Wang
Read original paper ↗ ·Generative models for inverse design of inorganic solid materials
Journal of Materials Informatics · 2021
Litao Chen, Wentao Zhang, Zhiwei Nie, Shunning Li, Feng Ming Pan
Read original paper ↗ ·A general and transferable deep learning framework for predicting phase formation in materials
npj Computational Materials · 2021
Shuo Feng, Huadong Fu, Huiyu Zhou, Yuan Wu, Zhaoping Lü, Hongbiao Dong
Read original paper ↗ ·Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning
Frontiers in Energy Research · 2021
Hongcan Liu, Shun Ma, Junjun Wu, Yingkai Wang, Xinghui Wang
Read original paper ↗ ·Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
arXiv (Cornell University) · 2021
Boyu Zhang, Zhou, Mushen, Wu, Jianzhong, Fuchang Gao
Read original paper ↗ ·Atom classification with Machine Learning and correlations among physical properties of ZnO nanoparticle
Chemical Physics · 2021
Hasan Kurban
Read original paper ↗ ·Rare-class learning over Mg-doped ZnO nanoparticles
Chemical Physics · 2021
Hasan Kurban, Mustafa Kurban
Read original paper ↗ ·A deep neural network inverse solution to recover pre-crash impact data of car collisions
Transportation Research Part C Emerging Technologies · 2021
Qijun Chen, Yuxi Xie, Yu Ao, Tiange Li, Guorong Chen, Shaofei Ren, Chao Wang, Shaofan Li
Read original paper ↗ ·Application of deep transfer learning to predicting crystal structures of inorganic substances
Computational Materials Science · 2021
Shuo Feng, Huiyu Zhou, Hongbiao Dong
Read original paper ↗ ·Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles
Computational Materials Science · 2021
Hasan Kurban, Mustafa Kurban
Read original paper ↗ ·Transfer learning for enhancing the homogenization-theory-based prediction of elasto-plastic response of particle/fiber-reinforced composites
Venue not supplied by source · 2021
Jiyoung Jung, Yong-Taek Kim, Jinkyoo Park, Seunghwa Ryu
Read original paper ↗ ·Machine Learning in Solid‐State Chemistry: Heusler Compounds
Encyclopedia of Inorganic and Bioinorganic Chemistry · 2021
Alexander S. Gzyl, Arthur Mar, Anton O. Oliynyk
Read original paper ↗ ·Encoding the atomic structure for machine learning in materials science
Wiley Interdisciplinary Reviews Computational Molecular Science · 2021
Shunning Li, Yuanji Liu, Dong Chen, Yi Jiang, Zhiwei Nie, Feng Pan
Read original paper ↗ ·Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments
International Journal of Mechanical Sciences · 2021
Adrien Müller, Nikolaos Karathanasopoulos, Christian C. Roth, Dirk Mohr
Read original paper ↗ ·Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth
Communications Materials · 2021
Thomas Martynec, Christos Karapanagiotis, Sabine H. L. Klapp, Stefan Kowarik
Read original paper ↗ ·Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites
Mechanics of Advanced Materials and Structures · 2021
Nang Xuan Ho, Tien-Thinh Le, Minh Vuong Le
Read original paper ↗ ·Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques
Materials Today Proceedings · 2021
Vidyasagar Shetty, Shabari Shedthi B, J. Kumaraswamy
Read original paper ↗ ·Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores
The Journal of Physical Chemistry C · 2021
Calen J. Leverant, Jacob Harvey, Todd Michael Alam, Jeffery A. Greathouse
Read original paper ↗ ·Crystal graph attention networks for the prediction of stable materials
Science Advances · 2021
Jonathan Schmidt, Love Pettersson, Claudio Verdozzi, Silvana Botti, Miguel A. L. Marques
Read original paper ↗ ·Application of Machine Learning in Determining the Mechanical Properties of Materials
Composites science and technology · 2022
Naman Jain, Akarsh Verma, Shigenobu Ogata, M. R. Sanjay, Suchart Siengchin
Read original paper ↗ ·Predicting the Materials Properties Using a 3D Graph Neural Network With Invariant Representation
IEEE Access · 2022
Boyu Zhang, Musen Zhou, Jianzhong Wu, Fuchang Gao
Read original paper ↗ ·Prediction Model for Tensile Modulus of Carbon Nanotube–Polymer Composites
Lecture notes in networks and systems · 2022
Tien-Thinh Le, Minh Vuong Le
Read original paper ↗ ·Transfer learning for enhancing the homogenization-theory-based prediction of elasto-plastic response of particle/short fiber-reinforced composites
Composite Structures · 2022
Jiyoung Jung, Yongtae Kim, Jinkyoo Park, Seunghwa Ryu
Read original paper ↗ ·Representations and strategies for transferable machine learning improve model performance in chemical discovery
The Journal of Chemical Physics · 2022
Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik
Read original paper ↗ ·Deep learning modeling strategy for material science: from natural materials to metamaterials
Journal of Physics Materials · 2022
Wenwen Li, Pu Chen, Bo Xiong, Guandong Liu, Shuliang Dou, Yaohui Zhan, Zhiyuan Zhu, Tao Chu, Yao Li, Wei Ma
Read original paper ↗ ·