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1 month ago | 
link.aps.org | Trupti Mohanty |Hasan M. Sayeed |Chitrasen Mohanty |Taylor Sparks
It is not necessary to obtain permission to reuse thisarticle or its components as it is available under the terms ofthe Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, andreproduction in any medium, provided attribution to the author(s) andthe published article's title, journal citation, and DOI aremaintained. Please note that some figures may have been included withpermission from other third parties.
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Oct 31, 2024 | 
nanohub.org | Taylor Sparks
0 users 0 questions (Ask a question) 0 review(s) (Review this) Abstract In this seminar, Dr. Sparks presents an innovative online platform for teaching materials informatics, designed to seamlessly integrate advanced computational tools with an accessible user experience.
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Sep 25, 2024 | 
nanohub.org | Taylor Sparks
Materials Informatics: Data, Algorithms, and Deployment from Property Prediction to Large Language Models Launch Tool Version 1.0 - published on 26 Sep 2024 doi:10.21981/CV4Y-SZ16 cite this Open source: license | download View All Supporting Documents 29 users, detailed usage 0 Citation(s) 0 questions (Ask a question) 0 review(s) (Review this) 0 wish(es) (New Wish) Published on 26 Sep 2024 Abstract This tool includes a series of notebooks that accompany the course "Materials Informatics:...
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics series! In today's episode, we delve into Bayesian Optimization, a critical tool for incrementally improving processes and designs in materials research. Bayesian Optimization leverages Bayes' theorem to make informed decisions with minimal data, making it particularly valuable in material science, where property trade-offs are common.
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics playlist! In today's episode, we explore the transformative impact of Large Language Models (LLMs) on materials science. David Sparks from the University of Utah's Materials Science and Engineering department takes us through the exciting possibilities that LLMs bring to the field.
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics series! In this video, we continue exploring Bayesian and probabilistic machine learning by diving deeper into Naive Bayes classification. In this video, we cover:A brief recap of Naive Bayes, highlighting its simplicity and the assumption of feature independence. A practical demonstration of Naive Bayes using materials project data to classify whether a material is a metal or an insulator.
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics playlist! In our previous video, we explored generative models like GANs. Today, we’re taking a brief interlude to discuss the fascinating world of image segmentation because tools from image segmentation are crucial to our next generative model!In this video, we cover:The basics of image segmentation and its importance in materials science. Different types of segmentation: semantic segmentation, instance segmentation, and more.
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics series! In this video, Taylor Sparks from the University of Utah delves into the fundamentals of Bayes Theorem and Naive Bayes classifiers, essential concepts in probabilistic machine learning. In this video, we cover: An introduction to Bayesian and frequentist approaches in machine learning, highlighting key differences. The basics of conditional probability, illustrated with examples from materials science.
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Generative machine learning models have the potential to allow us to move beyond screening to true materials discovery. Generative adversarial networks (GANs) are one powerful tool and variational autoencoders (VAEs) are another. This video descrbies autoencoders, latent space, reparameterization trick, Researchers should cite this work as follows:
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Aug 13, 2024 | 
nanohub.org | Taylor Sparks
Welcome back to our Materials Informatics series! In this video, we're diving into the fascinating world of diffusion models, the cutting-edge technology in generative models that is outperforming GANs and VAEs.In this video, we cover:Introduction to diffusion models and their superiority in generative tasks. Real-world applications of diffusion models, including popular tools like Midjourney, DALL-E 3, and Adobe Firefly.
