Tags: machine learning

All Categories (1-20 of 238)

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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...

  8. 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

  9. 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

  10. Olusegun Odunayo Felix

    https://nanohub.org/members/410227

  11. 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.

  12. Eugenio Culurciello

    https://culurciello.github.iohttps://euge-blog.github.io

    https://nanohub.org/members/403223

  13. 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

  14. 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

  15. 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.

  16. 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

  17. No-code ML models

    18 Oct 2022 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    No-code ML models

  18. The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction

    06 Oct 2022 | | Contributor(s):: Ryan Jacobs

    Hands-on activities, we will use MAST-ML to (1) import materials datasets from online databases and clean and examine our input data, (2) conduct feature engineering analysis, including generation, preprocessing, and selection of features, (3) construct, evaluate and compare the performance of...

  19. Introduction to a Basic Machine Learning Workflow for Predicting Materials Properties

    04 Oct 2022 | | Contributor(s):: Benjamin Afflerbach

    This tutorial will introduce core concepts of machine learning through the lens of a basic workflow to predict material bandgaps from material compositions.

  20. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression

    26 Aug 2022 | | Contributor(s):: Davis McGregor

    This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.