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May 09 2024
Simplifying Computational Simulations: Using Large Language Models for Automated Research in Materials Science
Simplifying Computational Simulations: Using Large Language Models for Automated Research in Materials ScienceDate and timeThursday, May 9, 2024; 12:00 - 1:00 PM EDTPresenterEthan...
https://nanohub.org/events/details/2420
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May 02 2024
Unlocking the Power of Large Language Models for Research Innovation
Unlocking the Power of Large Language Models for Research InnovationDate and timeThursday, May 2, 2024; 12:00 - 1:00 PM EDTPresenterJuan C. Verduzco, Ph.D., Network for Computational...
https://nanohub.org/events/details/2419
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The Ultimate SuperComputer-on-a-Chip for Massive Big Data and Highly Iterative Algorithms
10 Apr 2024 | | Contributor(s):: Veljko M. Milutinovic
ECE 606: Solid State Devices I - Guest LectureThis presentation analyses the essence of DataFlow SuperComputing, defines its advantages and sheds lighton the related programming model.DataFlow computers, compared to ControlFlow computers, offer speedups of 20 to 200 (even 2000 for some...
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Supplementary Data for "An unsupervised machine learning based approach to identify efficient spin-orbit torque materials"
18 Feb 2024 | | Contributor(s):: Shehrin Sayed, Hannah Kleidermacher, Giulianna Hashemi-Asasi, Cheng-Hsiang Hsu, Sayeef Salahuddin
Introduction:There has been a growing interest in materials with large spin-orbit torques (SOT) for many novel applications, and in our article [1], which is currently under review, we have shown that a machine-learning-based approach using a word embedding model can predict...
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nanoHUB: AI, Data, and Simulations for Students, Researchers and Instructors
11 Jan 2024 | | Contributor(s):: Alejandro Strachan, The Micro Nano Technology - Education Center
This talk will introduce nanoHUB resources for physics-based simulations, machine learning, and collaboration that can be used by students and instructors in research and education.
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Resources and Cyberinfrastructure for Laser Powder Bed Fusion – Tools to enable 3D Additive Metals Manufacturing
11 Jan 2024 | | Contributor(s):: Elif Ertekin, The Micro Nano Technology - Education Center
We will describe laser powder bed fusion, how machine learning and modeling/simulation tools can help optimize the process, and opportunities to engage students in the work.
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Data-Driven Materials Innovation: where Machine Learning Meets Physics
29 Nov 2023 | | Contributor(s):: Anand Chandrasekaran
Learn how Schrödinger’s tools can address common issues by using a combination of physics-based simulation data, enterprise informatics, and chemistry-aware ML.
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Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR
29 Nov 2023 | | Contributor(s):: Michael Rauch
In this workshop, we will demonstrate the hands-on use of Schrödinger's MS Maestro graphical user interface within nanoHUB to perform machine learning model creation and implementation.
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Nov 07 2023
Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR
Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSARSpeaker: Michael Rauch, Principal Scientist, SchrödingerDate: Nov. 7,...
https://nanohub.org/events/details/2397
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Scientific Text-Based Machine Learning
02 Nov 2023 | | Contributor(s):: Shehrin Sayed
Please note that this page is under development and information provided may change. Introduction: Machine learning is undoubtedly a useful tool and is gradually changing how we function in everyday life. The application of this powerful tool in materials and devices research may have...
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Oct 31 2023
Machine Learning for Materials Science with Schrödinger
Machine Learning for Materials Science with SchrödingerSpeaker: Anand Chandrasekaran, Principal Scientist, SchrödingerDate: Oct 31, 2023 1:00 PM EST Click here to...
https://nanohub.org/events/details/2396
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Schrödinger Materials Science AutoQSAR for Machine Learning
11 Sep 2023 |
Build quantitative structure-activity relationships (QSAR) automatically for molecular systems with Schrödinger's AutoQSAR tool
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Olusegun Odunayo Felix
https://nanohub.org/members/410227
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ANN-based friction factor and Nusselt number models for developing flow across square pin fins
30 May 2023 | | Contributor(s):: Saeel Shrivallabh Pai, Justin A. Weibel
ANN-based correlations which provide friction factor and Nusselt number values for developing flows across square pin fins of different pitch.
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Eugenio Culurciello
https://culurciello.github.iohttps://euge-blog.github.io
https://nanohub.org/members/403223
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How to predict band gap of other compounds ?
Q&A|Open | Responses: 1
How will I predict the band gap of any compound other than Si, SiO2, NaCl, Sn and Diamond ? What is the code for doing so? Also if i want to predict the bandgap of the polymer can I do so? and...
https://nanohub.org/answers/question/2663
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Abdu-Jabbar Bozdar
I graduated majoring in electronics and currently working as a Computer Programmer. My keen interest in electronics engineering, semiconductor materials and devices brought me here.
https://nanohub.org/members/387719
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Gaussian Process Regression for Surface Interpolation
22 Nov 2022 | | Contributor(s):: Zhiqiao Dong, Manan Mehta
This tutorial will introduce the fundamentals of GPR and its application to surface interpolation. We will also introduce a new technique called filtered kriging (FK), which uses a pre-filter to improve interpolation performance.
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Nongnuch Artrith
Dr. rer. nat. Nongnuch Artrith (http://nartrith.atomistic.net) is a Tenure-Track Assistant Professor in the Materials Chemistry and Catalysis group at the Debye Institute for Nanomaterials Science,...
https://nanohub.org/members/384244
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No-code ML models
18 Oct 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
No-code ML models