Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Reyes, Kristofer G.
and
Maruyama, Benji
2019.
The machine learning revolution in materials?.
MRS Bulletin,
Vol. 44,
Issue. 7,
p.
530.
Chen, Chi
Zuo, Yunxing
Ye, Weike
Li, Xiangguo
Deng, Zhi
and
Ong, Shyue Ping
2020.
A Critical Review of Machine Learning of Energy Materials.
Advanced Energy Materials,
Vol. 10,
Issue. 8,
Batra, Rohit
Dai, Hanjun
Huan, Tran Doan
Chen, Lihua
Kim, Chiho
Gutekunst, Will R.
Song, Le
and
Ramprasad, Rampi
2020.
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders.
Chemistry of Materials,
Vol. 32,
Issue. 24,
p.
10489.
Xiong, Jie
Shi, San-Qiang
and
Zhang, Tong-Yi
2020.
A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys.
Materials & Design,
Vol. 187,
Issue. ,
p.
108378.
Batra, Rohit
Song, Le
and
Ramprasad, Rampi
2020.
Emerging materials intelligence ecosystems propelled by machine learning.
Nature Reviews Materials,
Vol. 6,
Issue. 8,
p.
655.
Chang, Zhipeng
Chen, Wenhe
Gu, Yuping
and
Xu, Haoyue
2020.
Mahalanobis-Taguchi System for Symbolic Interval Data Based on Kernel Mahalanobis Distance.
IEEE Access,
Vol. 8,
Issue. ,
p.
20428.
Samavatian, Vahid
Fotuhi-Firuzabad, Mahmud
Samavatian, Majid
Dehghanian, Payman
and
Blaabjerg, Frede
2020.
Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics.
Scientific Reports,
Vol. 10,
Issue. 1,
Batra, Rohit
and
Sankaranarayanan, Subramanian
2020.
Machine learning for multi-fidelity scale bridging and dynamical simulations of materials.
Journal of Physics: Materials,
Vol. 3,
Issue. 3,
p.
031002.
Abdellaoui, Ismail Alaoui
and
Mehrkanoon, Siamak
2021.
Symbolic regression for scientific discovery: an application to wind speed forecasting.
p.
01.
Asadzadeh, Mohammad Zhian
Gänser, Hans-Peter
and
Mücke, Manfred
2021.
Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process.
Applications in Engineering Science,
Vol. 6,
Issue. ,
p.
100049.
He, Ning
Ouyang, Runhai
and
Qian, Quan
2021.
Learning interpretable descriptors for the fatigue strength of steels.
AIP Advances,
Vol. 11,
Issue. 3,
Hart, Gus L. W.
Mueller, Tim
Toher, Cormac
and
Curtarolo, Stefano
2021.
Machine learning for alloys.
Nature Reviews Materials,
Vol. 6,
Issue. 8,
p.
730.
Samavatian, Majid
Gholamipour, Reza
and
Samavatian, Vahid
2021.
Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach.
Computational Materials Science,
Vol. 186,
Issue. ,
p.
110025.
Lakshminarayanan, Madhavkrishnan
Dutta, Rajdeep
Repaka, D. V. Maheswar
Jayavelu, Senthilnath
Leong, Wei Lin
and
Hippalgaonkar, Kedar
2021.
Comparing data driven and physics inspired models for hopping transport in organic field effect transistors.
Scientific Reports,
Vol. 11,
Issue. 1,
Moscato, Pablo
Sun, Haoyuan
and
Haque, Mohammad Nazmul
2021.
Analytic Continued Fractions for Regression: A Memetic Algorithm Approach.
Expert Systems with Applications,
Vol. 179,
Issue. ,
p.
115018.
Xiong, Jie
Lei, Tong-Xing
Fu, Da-Meng
Wu, Jun-Wei
and
Zhang, Tong-Yi
2022.
Data driven discovery of an analytic formula for the life prediction of Lithium-ion batteries.
Progress in Natural Science: Materials International,
Vol. 32,
Issue. 6,
p.
793.
Beniwal, Dishant
and
Ray, Pratik K.
2022.
FCC vs. BCC phase selection in high-entropy alloys via simplified and interpretable reduction of machine learning models.
Materialia,
Vol. 26,
Issue. ,
p.
101632.
Ren, Jiaolong
Zhang, Lin
Zhao, Hongbo
Zhao, Zedong
and
Wang, Siyuan
2022.
Determination of the fatigue equation for the cement-stabilized cold recycled mixtures with road construction waste materials based on data-driven.
International Journal of Fatigue,
Vol. 158,
Issue. ,
p.
106765.
Wang, Changxin
Zhang, Yan
Wen, Cheng
Yang, Mingli
Lookman, Turab
Su, Yanjing
and
Zhang, Tong-Yi
2022.
Symbolic regression in materials science via dimension-synchronous-computation.
Journal of Materials Science & Technology,
Vol. 122,
Issue. ,
p.
77.
Beniwal, Dishant
and
Ray, Pratik K.
2022.
FCC vs. BCC Phase Selection in High-Entropy Alloys Via Simplified and Interpretable Reduction of Machine Learning Models.
SSRN Electronic Journal ,