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X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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DTSTART;TZID=America/Detroit:20241204T160000
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SUMMARY:MICDE-Aerospace Engineering Seminar: Jan Janssen\, Scientist\, Max Planck Institute
DESCRIPTION:Bio:\nDr. Jan Janssen is the group leader for materials informatics at the Max Planck Institute for Sustainable Materials in Düsseldorf\, Germany. Previously\, he was a Director’s Postdoctoral Fellow at Los Alamos National Laboratory\, where he designed materials for fusion reactors as part of the Exascale Computing Project. In addition\, he leads the development of the open-source pyiron software package\, is a maintainer of over 900 open-source software packages for the conda-forge community and an active contributor to open-source projects on Github. \nTitle:\nHow to use machine learning in the discovery and design of materials for the future? \nAbstract:\nDesigning materials for a sustainable future requires rethinking traditional materials design\, which is centered on optimizing and fine-tuning already known alloying compositions. In a mathematical sense this can be identified as a local or global optimization in the multi-dimensional alloy phase space. To sample the whole periodic table\, already a three-component alloy with 20 temperature steps and 10 concentration steps requires a million experiments\, making it prohibitive for purely experimental approaches.\nTo address this challenge\, simulation approaches and\, more recently\, machine learning models are applied to screen the periodic table. The pyiron workflow framework developed at the Max-Planck-Institute for sustainable materials predicts new materials using ab-intio thermodynamics. Starting from the interaction of electrons\, it predicts macroscopic material properties like heat capacity\, thermal expansion\, and phase stability. Recently\, the pyiron workflow framework was extended with a large language model (LLM) interface named LangSim.\nThis raises the question: Can a LLM replace a scientist? Or how does the thought process of a scientist differ from the statistical approach of the LLM? Can the LLM make us better scientists? We benchmark the capabilities of current LLMs to design new materials using atomistic simulation. The presentation introduces ab-initio thermodynamics\, covers the importance of simulation workflows to efficiently predict sustainable materials and highlights how LLMs accelerate their discovery.
URL:https://micde.umich.edu/event/workshop-seminarmicde-aerospace-engineering-seminar-jan-janssen-how-to-use-machine-learning-in-the-discovery-and-design-of-materials-for-the-future/
LOCATION:Cooley Building – 906
CATEGORIES:Aerospace Engineering,Micde,Micde Seminar,Michigan Engineering
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/11/Jan-Janssen.png
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DTSTART;TZID=America/Detroit:20241206T120000
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CREATED:20241011T181202Z
LAST-MODIFIED:20241204T144214Z
UID:10000779-1733486400-1733490000@micde.umich.edu
SUMMARY:FSML Lecture Series - Anoushka Bhutani: Foundation Model for Molecular Design
DESCRIPTION:Zoom link \nBio: Anoushka is a third-year PhD student in Prof. Venkat Viswanathan’s group at the University of Michigan. Her research interests include machine learning for materials design and electrochemical battery modeling. \nFoundation Model for Molecular Design\nAbstract: The paradigm of molecular machine learning for material screening has accelerated material development cycles\, improved efficiency\, and reduced costs. However\, current state-of-the-art molecular property prediction models still require labeled training data generated using wet-lab experiments or Density Functional Theory (DFT) calculations. Their utility is limited by the scarcity and heterogeneity of labeled materials datasets. Foundation models (FMs) offer a solution to this: these models use self-supervised pre-training strategies to leverage unlabeled datasets and learn representations of data that can be applied to downstream tasks. Large unlabeled datasets of billions of synthesizable molecules are readily available. Prior attempts to train FMs for molecular property prediction demonstrate promise; however\, equivariant geometric models trained using supervised learning are still more accurate. This can be attributed to the fact that foundation models are extremely expensive to train and can be difficult to interpret; they require huge computing budgets\, complex distributed computing techniques\, and extensive hyperparameter searches. Our work addresses these challenges on three fronts: (1) we have prototyped a scalable workflow for distributed training of molecular foundation models (2) we have trained large foundation models using this workflow which demonstrates state-of-the-art molecular property prediction capabilities across several benchmarks\, and (3) we have applied model interpretability strategies such as the attention visualization to shed insight on molecular structure relationships learn by the transformer. \n 
URL:https://micde.umich.edu/event/workshop-seminaranoushka-bhutani-foundation-model-for-molecular-design/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Computational Science,Engineering,FSML,Graduate School,Graduate Students,Michigan Engineering,Rackham,Research,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Copy-of-MICDE-2022-2023-Fellowship-Portraits.png
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