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    • Home
    • Research
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  • Home
  • Research
  • Members
  • Publications
  • Courses
  • Resources
  • News
  • Contact

Materials Intelligence

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Rhone Research Group

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resources

Explore ways you can facilitate your education / research in materials informatics by exploring the list of free resources below.

Materials-intelligence tutorials

Materials Informatics

  • Harvard IACS Seminar 2022
  • Introduction to materials research using machine learning


Self-Learning

Coursera

  • Machine Learning, Andrew Ng
  • Data Science, Johns Hopkins University

Textbooks

  • Introduction to Statistical Learning, Trevor Hastie et al.
  • Pattern Recognition and Machine Learning, Christopher Bishop
  • Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville

Newsletters/Tutorials

  • A beginner's guide to using data science for physicists
  • Citrine Newsletter
  • TensorFlow tutorial
  • Introduction to Linear Algebra using Python (by Dr. Steven L. Richardson)


Workshops

  • IPAM, UCLA
  • IACS ComputeFest, Harvard University
  • Citrine Webinars

Data Science Platforms

  • NIST, REsource for Materials Informatics

Data availability

Kaggle datasets

Large repository  of a wide variety of data sources

Google's datasets search

Search for a data source of interest using Google's datasets search tool.

Selected materials databases

  • Materials project
  • Citrine datasets
  • Materials cloud
  • NIMS Materials Database (MatNavi)
  • AFLOW
  • Computational 2D Materials Database (C2DB)

Data science tools

Programming tools

  • Popular programming languages: Python, Julia, R
  • Jupyter notebook
  • JupyterHub
  • Google Colab
  • Useful python packages: Pandas, Numpy, Matplotlib, Plotly, Sci-kit learn*

Sci-kit learn

Sci-kit learn is a python package that allows you to easily implement many machine learning models. This is a popular data science tool for building models that do not involve neural networks.

Deep learning tools

  • TensorFlow
  • Pytorch
  • Keras

Descriptors

  • Coulomb Kernel
  • Bag of bonds
  • MBTR
  • SOAP

Atomistic simulations

  • Properties from Artificial Neural Network Architectures (PANNA)
  • Tensor field networks

Sharing your work

  • github
  • streamlit

Select publications

Materials Informatics

Krishna Rajan,  Materials Today, 38 (2005). 

The high-throughput highway to computational materials design

Stefano Curtarolo, Gus L. W. Hart, Marco Buongiorno Nardelli, Natalio Mingo, Stefano Sanvito and Ohad Levy, Nature Materials  12, 191 (2013). DOI: 10.1038/NMAT3568

Data-driven studies of magnetic two-dimensional materials

Rhone, T.D., Chen, W., Desai, S. et al. Data-driven studies of magnetic two-dimensional materials. Sci Rep 10, 15795 (2020). https://doi.org/10.1038/s41598-020-72811-z

Predicting outcomes of catalytic reactions using machine learning

Trevor David Rhone, Robert Hoyt, Christopher R. O'Connor, Matthew M. Montemore, Challa S.S.R. Kumar, Cynthia M. Friend and Efthimios Kaxiras, arXiv:1908.10953

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli, Jan Vybiral, Sergey V. Levchenko, Claudia Draxl, and Matthias Scheffler,    

PRL 114, 105503 (2015)  

A structural approach to relaxation in glassy liquids

S. S. Schoenholz, E. D. Cubuk, D. M., Sussman, E. Kaxiras and A. J. Liu,      

Nature Physics 12, 469 (2016), DOI: 10.1038/NPHYS3644 

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics C

Atsuto Seko, Atsushi Togo, Hiroyuki Hayashi, Koji Tsuda, Laurent Chaput, and Isao Tanaka,      

PRL 115, 205901 (2015).

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

M. Rupp, A. Tkatchenko, K.-R. Müller, O.A. von Lilienfeld, Physical Review Letters 108(5): 058301, (2012).

Physical Symmetries Embedded in Neural Networks

M. Mattheakis, P. Protopapas, D. Sondak, M. Di Giovanni, E. Kaxiras,  arXiv:1904.08991 

Materials Intelligence

Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute

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