Papers citing Materials discovery and design using machine learning
Search for high-capacity oxygen storage materials by materials informatics
RSC Advances · 2019
Nobuko Ohba, Takuro Yokoya, Seiji Kajita, Kensuke Takechi
Read original paper ↗ ·Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines
ACS Energy Letters · 2019
Yongze Yu, Xuanheng Tan, Shougui Ning, Yiying Wu
Read original paper ↗ ·A Transductive Learning Approach for Identification of Microstructure-Process Linkages
AIAA SciTech Forum · 2019
Pınar Acar
Read original paper ↗ ·Non-uniform spontaneous surface curvature gives limited shape control in simulated single-polymer micelles
Materials Research Express · 2019
Brian Moths
Read original paper ↗ ·Detection method of TFe content of iron ore based on visible-infrared spectroscopy and IPSO-TELM neural network
Infrared Physics & Technology · 2019
Dong Xiao, Chongmin Liu, Ba Tuan Le
Read original paper ↗ ·Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: Deriving a data-driven model using machine learning methods
Tribology International · 2019
Alireza Borjali, Kenneth L. Monson, Bart Raeymaekers
Read original paper ↗ ·Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides
Journal of Energy Chemistry · 2019
Masanori Kaneko, Mikiya Fujii, Takashi Hisatomi, Koichi Yamashita, Kazunari Domen
Read original paper ↗ ·Materials informatics: From the atomic-level to the continuum
Acta Materialia · 2019
Jeffrey M. Rickman, Turab Lookman, Sergei V. Kalinin
Read original paper ↗ ·A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels
Computational Materials Science · 2019
Elizabeth J. Kautz, Alexander R. Hagen, Jesse Johns, Douglas E. Burkes
Read original paper ↗ ·Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1
Metals · 2019
Johan Andreas Stendal, Markus� Bambach, Mark Eisentraut, Irina Sizova, Sabine Weiß
Read original paper ↗ ·From DFT to machine learning: recent approaches to materials science–a review
Journal of Physics Materials · 2019
Gabriel Ravanhani Schleder, A. C. M. Padilha, Carlos Mera Acosta, Marcio Costa, A. Fazzio
Read original paper ↗ ·Nanomaterials Discovery and Design through Machine Learning
Small Methods · 2019
Ming Wang, Ting Wang, Pingqiang Cai, Xiaodong Chen
Read original paper ↗ ·High-throughput experiments facilitate materials innovation: A review
Science China Technological Sciences · 2019
Yihao Liu, Ziheng Hu, Zhiguang Suo, Lianzhe Hu, Lingyan Feng, Xiuqing Gong, Yi Liu, Jincang Zhang
Read original paper ↗ ·Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent
International Journal of Hydrogen Energy · 2019
Alireza Rahnama, Guilherme Zepon, S. Sridhar
Read original paper ↗ ·Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning
HMD Praxis der Wirtschaftsinformatik · 2019
Kai Heinrich, Patrick Zschech, Björn Möller, Lukas Breithaupt, Johannes Maresch
Read original paper ↗ ·A descriptor of “material genes”: Effective atomic size in structural unit of ionic crystals
Science China Technological Sciences · 2019
Dong Chen, Shunning Li, Jianshu Jie, SiBai Li, Shisheng Zheng, Mouyi Weng, ChangCheng Yu, ShuCheng Li, DaJun Chen, Feng Ming Pan
Read original paper ↗ ·Machine learning for composite materials
MRS Communications · 2019
Chun‐Teh Chen, Grace X. Gu
Read original paper ↗ ·Rapid identification of two-dimensional materials via machine learning assisted optic microscopy
Journal of Materiomics · 2019
Yuhao Li, Yangyang Kong, Jinlin Peng, Chuanbin Yu, Zhi Li, Penghui Li, Yunya Liu, Cun‐Fa Gao, Rong Wu
Read original paper ↗ ·Discovering unusual structures from exception using big data and machine learning techniques
中国科学通报：英文版 · 2019
Jianshu Jie, Zongxiang Hu, Guoyu Qian, Mouyi Weng, Shunning Li, Shucheng Li, Mingyu Hu, Dong Chen, Weiji Xiao, Jiaxin Zheng, Lin‐Wang Wang, Feng Pan
Read original paper ↗ ·A two-stage predicting model for γ′ solvus temperature of L12-strengthened Co-base superalloys based on machine learning
Intermetallics · 2019
Jinxin Yu, Shun Guo, Yuechao Chen, Jiajia Han, Yong Lu, Qingshan Jiang, Cuiping Wang, Xingjun Liu
Read original paper ↗ ·Incomplete Conditional Density Estimation for Fast Materials Discovery
Society for Industrial and Applied Mathematics eBooks · 2019
Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Matthew Robert Barnett, Svetha Venkatesh
Read original paper ↗ ·Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists
Chemical Reviews · 2019
Jessica G. Freeze, H. Ray Kelly, Víctor S. Batista
Read original paper ↗ ·A Bayesian framework for materials knowledge systems
MRS Communications · 2019
Surya R. Kalidindi
Read original paper ↗ ·Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data
Metals · 2019
Cristiano Fragassa, Matej Babič, Carlos Pérez Bergmann, Giangiacomo Minak
Read original paper ↗ ·Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science
Computational Materials Science · 2019
Heesu Hwang, Jiwon Oh, Keon-Hee Lee, Jung-Hwan Cha, Eunsoo Choi, Young Yoon, Jin‐Ha Hwang
Read original paper ↗ ·Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks
Journal of Nanomaterials · 2019
George Alexandru Nemneş, Tudor Luca Mitran, Andrei R. Manolescu
Read original paper ↗ ·Research and application of machine learning method based on swarm intelligence optimization
Journal of Computational Methods in Sciences and Engineering · 2019
Jue Wang, Di Yao, Rui Xiao
Read original paper ↗ ·Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations
The Journal of Physical Chemistry A · 2019
Samrendra Kumar Singh, Karteek K. Bejagam, Yaxin An, Sanket A. Deshmukh
Read original paper ↗ ·Ultrahigh elastically compressible and strain-engineerable intermetallic compounds under uniaxial mechanical loading
APL Materials · 2019
Gyuho Song, Vladislav Borisov, William R. Meier, Mingyu Xu, Keith J. Dusoe, John T. Sypek, Roser Valentí, Paul C. Canfield, Seok‐Woo Lee
Read original paper ↗ ·Machine Learning Approach for Identification of Microstructure–Process Linkages
AIAA Journal · 2019
Pınar Acar
Read original paper ↗ ·Research on ancient ceramic identification by artificial intelligence
Ceramics International · 2019
Tianhong Mu, Fen Wang, Xiufeng Wang, Hongjie Luo
Read original paper ↗ ·A new MaterialGo database and its comparison with other high-throughput electronic structure databases for their predicted energy band gaps
Science China Technological Sciences · 2019
Jianshu Jie, Mouyi Weng, Shunning Li, Dong Chen, ShuCheng Li, Weiji Xiao, Jiaxin Zheng, Feng Ming Pan, LinWang Wang
Read original paper ↗ ·Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools
Science and Technology of Advanced Materials · 2019
Dmitry S. Bulgarevich, Susumu Tsukamoto, Tadashi Kasuya, Masahiko Demura, Makoto Watanabe
Read original paper ↗ ·Methods for data-driven multiscale model discovery for materials
Journal of Physics Materials · 2019
Steven L. Brunton, J. Nathan Kutz
Read original paper ↗ ·Computer-aided design of polymeric materials: Computational study for characterization of databases for prediction of mechanical properties under polydispersity
Chemometrics and Intelligent Laboratory Systems · 2019
Fiorella Cravero, Santiago A. Schustik, María Jimena Martínez, Carlos D. Barranco, Mónica F. Díaz, Ignacio Ponzoni
Read original paper ↗ ·Database of novel magnetic materials for high-performance permanent magnet development
Computational Materials Science · 2019
P. Nieves, S. Arapan, Jesús Maudes, Raúl Marticorena Sánchez, N.L. Del Brío, Alexander Kovacs, Cristina Echevarría–Bonet, Daniel Salazar, Jürgen Weischenberg, Hongbin Zhang, Olga Yu. Vekilova, Roberto Serrano-López, Jose Manuel Barandiaran, Konstantin P. Skokov, Oliver Gutfleisch, Olle Eriksson, Heike C. Herper, T. Schrefl, Santiago Cuesta‐López
Read original paper ↗ ·QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry
Journal of Chemical Information and Modeling · 2019
Felipe Zapata, Lars Ridder, Johan Hidding, Christoph R. Jacob, Ivan Infante, Lucas Visscher
Read original paper ↗ ·Towards machine learning approaches for predicting the self-healing efficiency of materials
Computational Materials Science · 2019
Wenjun Wang, Nicolette G. Moreau, Yingfang Yuan, Paul R. Race, Wei Pang
Read original paper ↗ ·Predicting the onset temperature (Tg) of Ge Se1− glass transition: a feature selection based two-stage support vector regression method
中国科学通报：英文版 · 2019
Yue Liu, Junming Wu, Guang Yang, Tianlu Zhao, Siqi Shi
Read original paper ↗ ·Generative Counterfactual Introspection for Explainable Deep Learning
arXiv (Cornell University) · 2019
Shusen Liu, Bhavya Kailkhura, Loveland, Donald, Yong Han
Read original paper ↗ ·Embedding domain knowledge for machine learning of complex material systems
MRS Communications · 2019
Christopher M. Childs, Newell R. Washburn
Read original paper ↗ ·Rectification and negative differential effects of Si2C2 clusters: An ab initio study
International Journal of Modern Physics B · 2019
Qinghua Zhou, Ziqing Wei, Wei Hu, Yan Liang, Wenhua Liu, Huili Li, Kerong He, Haiqing Wan
Read original paper ↗ ·ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learning
Computational Materials Science · 2019
Xiangmeng Wang, Yonglin Xu, Jiong Yang, Jianyue Ni, Wu Zhang, Wenhao Zhu
Read original paper ↗ ·Understanding Self-Assembly and Charge Transport in Organic Solar Cells Through Efficient Computation
Venue not supplied by source · 2019
Evan D. Miller
Read original paper ↗ ·New insights into the predicament of DFT assisted optimized energy, stability and distortions of optimized topologies of some novel complexes of Zirconium (IV) and enhancement of antimicrobial potential
Applied Organometallic Chemistry · 2019
Shobhana Sharma, Ramesh Kumar, Pradeep Kumar, Asha Jain, Sanjiv Saxena
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 ↗ ·Accelerating small-angle scattering experiments with simulation-based machine learning
Journal of Physics Materials · 2019
Takuya Kanazawa, Akinori Asahara, Hidekazu Morita
Read original paper ↗ ·Machine learning predictions of electronic couplings for charge transport calculations of P3HT
AIChE Journal · 2019
Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski
Read original paper ↗ ·Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models
Journal of Materials in Civil Engineering · 2019
Rachel Cook, Jonathan Lapeyre, Hongyan Ma, Aditya Kumar
Read original paper ↗ ·Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel
Acta Materialia · 2019
Chunguang Shen, Chenchong Wang, Xiaolu Wei, Yong Li, Sybrand van der Zwaag, Wei Xu
Read original paper ↗ ·