Machine Learning for Materials Informatics offered by MIT University
- Private University
168 acre campus- Estd. 1861
Machine Learning for Materials Informatics at MIT University Overview
Duration | 4 days |
Total fee | ₹2.80 Lakh |
Mode of learning | Online |
Schedule type | Self paced |
Course Level | UG Certificate |
Machine Learning for Materials Informatics at MIT University Course details
- For Lead scientists or engineers
- For Software engineers or data scientists
- For Technology outreach directors, technology scouts, IP/patent professionals, or consultants
- For Sustainability directors
- For Technical leaders or business intelligence managers/directors
- For Entrepreneurs, founders, investors, venture capitalists, futurists, and visionaries
- For Creatives and science communicators/marketers
- For Policymakers/influencers
- Explore the cutting-edge of modern material informatics tools, including machine learning, data analysis and visualization, and molecular/multiscale modeling
- Learn how to fine-tune general-purpose models for materials applications
- Deepen your knowledge of the frontiers of data-driven material analysis and ready-to-deploy code solutions
- Master computational methods for building better materials, such as language models, protein models, and graph neural networks
- Learn how to identify the most effective tool for solving your specific challenge, and gain an overview across the most promising neural network architectures and their most suitable application areas, challenges and potentials
- Enhance the speed, efficiency, and cost effectiveness of your materials design and production processes through next-generation molecular modeling
- Monetize your existing data and develop an actionable vision for incorporating material informatics into your organization?s current strategies
- Artificial intelligence is changing the paradigm for many industries, and materials-focused commerce is no exception, where tremendous opportunities lie ahead
- In this course you will fully learn how to incorporate these new technologies and methods into your own material design processes in order to capitalize on recent AI breakthroughs, such as language models (e.g. GPT-3, BERT), DNA and protein models (AlphaFold), graph neural networks for molecular to macroscale structures, and computer vision, specifically for the analysis, design and modeling of materials
- You will learn how modern computational tools enable us achieve almost any desirable accuracy in multiscale material discovery, connecting quantum to the macro-world
- The course curriculum is grounded in relevant examples and case studies from a variety of fields and at distinct scales (molecular to macro), including structural materials, additive manufacturing, nanotechnology, healthcare, and biomedical engineering
Machine Learning for Materials Informatics at MIT University Curriculum
Day One
Foundations in material informatics
Clinic #1: Convolutional neural network
Digging deeper: Deep neural nets, loss functions, Stochastic optimization methods
Clinic #2: Material failure analysis
Interactive virtual networking reception
Day Two
Hands-on introduction to PyTorch
Hands-on introduction to TensorFlow
Practical guide to tensor algebra and other important math concepts needed
Ethics, bias and sustainability in material informatics
Data, data, everywhere?De novo dataset construction (imaging lab) and application to build a deep neural network (covers computer vision tools, live imaging using depth camera
Introduction to graph neural networks (applications to molecular systems, truss systems, alloys, proteins, and healthcare; graph transformers)
Day Three
Transforming AI and healthcare with attention (AlphaFold and applications to protein design, synthesis)
Deepening the understanding of language models applied to materials (pre-training and fine-tuning); BERT and GPT-3-like (applications of large language models to materials problems; category theory; time-dependent material phenomena)
Clinic #3: Transformer models for inverse materials design (develop multiscale transformer model from scratch)
Adversarial neural networks and applications to materials design (manufacturing, inverse problem, characterization)
Case study: Image segmentation in microscopy, medical imaging, and analysis
Day Four
Autoencoders (vision, graphs, NLP, proteins)
Clinic #4: To fail or not to fail: Buckling modeling (time-dependent phenomena)
Concluding discussion
Machine Learning for Materials Informatics at MIT University Faculty details
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Machine Learning for Materials Informatics at MIT University Contact Information
77 Massachusetts Ave, Cambridge, MA 02139, USA
Cambridge ( Massachusetts)