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tamkintools-multi-fidelity-bayesian

https://github.com/maxfleck/tamkintools-multi-fidelity-bayesian

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Repository

Basic Info
  • Host: GitHub
  • Owner: maxfleck
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 32.7 MB
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

tamkintoolsmultifidelity_bayesian

Supporting Information / Code for the publication xxx

This repository consists of several examples combining different levels of quantum theory with Multi-Fidelity modeling and Bayesian methods including a problem-oriented acquisition function for thermodynamic properties.

All plots shown in the paper can be reproduced with this code.

PI_example

a starting example to get used to the approach and play around with kernels and hyperparameters

PItamkinmulti_fidelity

  • first example, focus on testing different kernels
  • quantum mechanic calculation results from different levels of theory are combined

PIreducedhf_data

  • impact of missing (high energy) samples can be tested here

tetrahydrofuran

in this example quantum mechanic results from different levels of theory contradict each other

multifidelitytetrahydrofuran

  • shows behavior of linear Multi-Fidelity models for contradicting samples

Umer_results

in this example the impact of missing high energy high fidelity samples is shown

umer_mf

  • shows the impact of missing high energy high fidelity samples
  • deviates a sampling strategy for initial high fidelity samples when a low fidelity scan is available

DMM_R3H+CH3

most extensive example including Bayesian techniques for better sampling

ThermoAcquisition_thermoProps

  • only low fidelity samples are used
  • a problem oriented acquisition function is introduced and tested
  • thermodynamic properties are calculated based on the samples
  • ... and can be compared to our extensively sampled reference

ThermoAcquisitionDyn

  • a dynamic version of our problem oriented acquisition function is introduced
  • adjusts its parameters on the run
  • useful when energy range of a scan is a priori unknown

ThermoAcquisitionMFe1scan1

  • utilizes everything:
    • Multi-Fidelity model
    • sampling strategy for initial high fidelity samples
    • Bayesian methods
    • our acquisition function
  • allows deeper insights

ThermoAcquisitionMFTSscan2

  • utilizes everything:
    • Multi-Fidelity model
    • sampling strategy for initial high fidelity samples
    • Bayesian methods
    • our acquisition function
  • allows deeper insights

code

tamkinmultifidelity

contains our Multi-Fidelity model based on Gpy and emukit

  • tamkinmultifidelity
    • class to initialize, train and plot a Multi-Fidelity model
    • written for 1D rotational scans
    • ...but easily extendable to higher dimensional cases

thermoAQ

contains our acquisition function based on GpyOpt

  • AcquisitionThermo
    • implementation of our problem oriented acquisition function
  • AcquisitionThermoDyn
    • implementation the dynamic version of our problem oriented acquisition function

Installation of tamkin and TamkinTools packages

  • Tamkin installation
    • Tamkin installation can be carried out with the help of the following links: https://molmod.github.io/tamkin/tutorial/install.html
    • Alternatively, conda/conda-forge can also be used to install Tamkin. For directions, use the link: https://anaconda.org/conda-forge/tamkin
  • TamkinTools installation
    • TamkinTools is a seperate package with improved and additional features from Tamkin. It can be downloaded from: https://git.rwth-aachen.de/Wassja.Kopp/tamkintools.git

Owner

  • Login: maxfleck
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: ' tamkintools-multi-fidelity-bayesian'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Maximilian
    family-names: Fleck
    email: maxi_fleck@posteo.com
    affiliation: >-
      Institute of Thermodynamics and Thermal Process
      Engineering, University of Stuttgart, Pfaffenwaldring
      9, 70569 Stuttgart, Germany
    orcid: 'https://orcid.org/0000-0003-3394-7105'
  - given-names: Wassja A
    family-names: Kopp
    email: wassja.kopp@ltt.rwth-aachen.de
    affiliation: ' Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstr. 8, 52062 Aachen, Germany'
    orcid: 'https://orcid.org/0000-0001-8147-3464'
  - given-names: Narasimhan
    family-names: Viswanathan
    email: narasimhan.viswanathan@ltt.rwth-aachen.de
    affiliation: ' Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstr. 8, 52062 Aachen, Germany'
    orcid: 'https://orcid.org/0000-0003-0369-3853'
  - given-names: 'Niels '
    family-names: Hansen
    email: hansen@itt.uni-stuttgart.de
    affiliation: >-
      Institute of Thermodynamics and Thermal Process
      Engineering, University of Stuttgart, Pfaffenwaldring
      9, 70569 Stuttgart, Germany
  - given-names: Joachim
    family-names: Gross
    email: gross@itt.uni-stuttgart.de
    orcid: 'https://orcid.org/0000-0001-8632-357X'
    affiliation: >-
      Institute of Thermodynamics and Thermal Process
      Engineering, University of Stuttgart, Pfaffenwaldring
      9, 70569 Stuttgart, Germany
  - given-names: 'Kai '
    family-names: Leonhard
    email: kai.leonhard@ltt.rwth-aachen.de
    affiliation: ' Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstr. 8, 52062 Aachen, Germany'
    orcid: 'https://orcid.org/0000-0001-6231-6957'
identifiers:
  - type: url
    value: >-
      https://chemrxiv.org/engage/chemrxiv/article-details/65f7e074e9ebbb4db9ec7d6d
repository-code: >-
  https://github.com/maxfleck/tamkintools-multi-fidelity-bayesian.git
keywords:
  - >-
    Multi Fidelity, bayesian Optimization, Gaussian Process,
    Coupled Cluster, DFT

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Dependencies

setup.py pypi
  • emukit *