Machine learning of mechanical properties of steels

[1]  Jie Xiong,et al.  A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys , 2020, Materials & Design.

[2]  B. Grabowski,et al.  A machine learning approach to model solute grain boundary segregation , 2018, npj Computational Materials.

[3]  Ankit Agrawal,et al.  An online tool for predicting fatigue strength of steel alloys based on ensemble data mining , 2018 .

[4]  Qiang Zhu,et al.  Predicting phase behavior of grain boundaries with evolutionary search and machine learning , 2017, Nature Communications.

[5]  Chiho Kim,et al.  Machine Learning and Materials Informatics: Recent Applications and Prospects , 2017, 1707.07294.

[6]  Ichiro Takeuchi,et al.  Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies , 2017 .

[7]  Jun Sun,et al.  An informatics approach to transformation temperatures of NiTi-based shape memory alloys , 2017 .

[8]  Logan T. Ward,et al.  A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials , 2016, 1606.09551.

[9]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[10]  B. Meredig,et al.  Materials science with large-scale data and informatics: Unlocking new opportunities , 2016 .

[11]  A. Choudhary,et al.  Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science , 2016 .

[12]  Keisuke Takahashi,et al.  Material synthesis and design from first principle calculations and machine learning , 2016 .

[13]  Miroslav Kubat An Introduction to Machine Learning , 2015, Springer International Publishing.

[14]  Oren E. Nahum,et al.  Data Mining and Machine Learning Tools for Combinatorial Material Science of All‐Oxide Photovoltaic Cells , 2015, Molecular informatics.

[15]  A. Choudhary,et al.  Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters , 2014, Integrating Materials and Manufacturing Innovation.

[16]  Gilles Louppe,et al.  Understanding variable importances in forests of randomized trees , 2013, NIPS.

[17]  Kalyan Veeramachaneni,et al.  Knowledge mining with genetic programming methods for variable selection in flavor design , 2010, GECCO '10.

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[19]  M. Kubát An Introduction to Machine Learning , 2017, Springer International Publishing.

[20]  Michael Affenzeller,et al.  HeuristicLab: A Generic and Extensible Optimization Environment , 2005 .

[21]  L. Billard,et al.  Symbolic Regression Analysis , 2002 .