We regularly conduct online, hands-on machine learning workshops to help students, researchers, and industrial practitioners equip themselves with skills in data science and machine learning to complement their research.
This series of modules introduces key concepts in data science in the context of application in materials science and engineering. Each module consists of a recorded lecture, hands-on tutorial, and homework assignment with online simulations.
This talk provides a overview of machine learning and its possible applications in material science. Suggested hands-on activities are using the nanoHUB tool Machine Learning for Materials Science: Part 1 or the tool Citrine Tools for Materials Informatics
This document introduces basic concepts of data science and machine learning in the context of materials science applications. The focus is on hands-on activities where readers use open, online tools in nanoHUB to explore the concepts being introduced. Topics covered include querying data resources and data organization and preparation. Regression exercises, including neural networks, to predict materials properties from a set of descriptors and a classification exercise designed to predict the crystal structure of elemental metals. The examples are provided as fully contained Jupyter notebooks and state of the art, open software. They are designed to introduce to the main steps in data science workflows and readers can modify or extend them to solve additional problems.
This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation. We will also discuss principles of design of experiments so that the data generated by experiments/simulation are statistically relevant and useful. We will conclude with a discussion of analytical tools for machine learning and principle component analysis. At the end of the course, a student will be able to use a broad range of tools embedded in MATLAB and Excel to analyze and interpret their data.
The Jupyter Notebooks in this tool implement methods developed by Citrine Informatics for materials design. Users can modify the notebooks to explore different models, try new ideas and adapt them for their own problems. Examples seek to solve materials design problems (posed as a maximization or minimization problem) with the fewest number of experiments possible.
The notebooks use sequential learning to identify the material with the highest bulk modulus and highest ionic conductivity. They all obtain their data from Citrination databases (https://citrination.com/), build models using random forest or neural networks, and compare different information acquisition strategies against random searches.
Data science and machine learning are playing increasingly important role in science and engineering and materials science and engineering is not an exception. This online tool provides examples of the use of these tools in the field of materials science using Jupyter notebooks. The notebooks contain step by step explanations of the activities and live code, that can be modified by the users for hands-on learning. The initial set of tutorials focus on: i) data query, organization and visualization, ii) developing a simple model using linear regression to explore correlations between materials properties, and iii) neural network models trained to predict materials properties from basic element properties. Suggested activities are included in the Jupyter notebooks.
This tool provides a set of tutorials to get started with machine learning using TensorFlow and Keras. The set of tutorials were taken from TensorFlow (https://www.tensorflow.org/tutorials/) with copyright by François Chollet and deployed with minimal modifications. Using nanoHUB resources users can run the tutorials, modify them and explore machine learning from any laptop or tablet, without downloading or installing any software.
- Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie
- Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan
- Computational Chemistry/Materials, Peilin Liao
- Optimizing Thermal Transport using a Genetic Algorithm, Xiulin Ruan
- Computational Methods & Machine Learning Algorithms, Guang Lin
Imaging Sciences at the Oak Ridge National Laboratory: Identity Sciences, Advanced Manufacturing, Computational Imaging, Machine Learning, and Super Computing
Dr. Santos takes us on the journey of working at the Oak Ridge National Laboratory as an imaging scientist. He showcases work in the areas of Identity Sciences (i.e., biometrics), Machine Learning, and Computational Imaging. Some application to discuss are coded source neutron imaging, non-ideal iris recognition, face reconstruction from a DNA sample, plenoptic imaging, brain neuron tracking tools, evolutionary deep neural architectures, model-based iterative image reconstruction, and Bragg-Edge tomography.