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The DACHS (Database for Automation, Characterization and Holistic Synthesis) project aims to create completely traceable experimental data, covering syntheses, measurements, analyses, and interpretations. DACHS_MOFs focuses on the synthesis and characterisation of metal-organic frameworks, across multiple, automation-assisted experimental series (AutoMOFs), with the overall goal of producing reproducible MOF samples through tracking of the synthesis parameters.
DACHS_MOFs is simultaneously used to test the DACHS principles.
This upload contain synthesis data from AutoMOFs_3 in HDF5 format (.h5). Each .h5 file contains detailed information on the chemical, experimenal, and synthesis parameters used during the synthesis of a single AutoMOF sample.
The DACHS (Database for Automation, Characterization and Holistic Synthesis) project aims to create completely traceable experimental data, covering syntheses, measurements, analyses, and interpretations. DACHS_MOFs focuses on the synthesis and characterisation of metal-organic frameworks, across multiple, automation-assisted experimental series (AutoMOFs), with the overall goal of producing reproducible MOF samples through tracking of the synthesis parameters.
DACHS_MOFs is simultaneously used to test the DACHS principles.
This upload contain synthesis data from AutoMOFs_2 in HDF5 format (.h5). Each .h5 file contains detailed information on the chemical, experimenal, and synthesis parameters used during the synthesis of a single AutoMOF sample.
The DACHS (Database for Automation, Characterization and Holistic Synthesis) project aims to create completely traceable experimental data, covering syntheses, measurements, analyses, and interpretations. DACHS_MOFs focuses on the synthesis and characterisation of metal-organic frameworks, across multiple, automation-assisted experimental series (AutoMOFs), with the overall goal of producing reproducible MOF samples through tracking of the synthesis parameters.
DACHS_MOFs is simultaneously used to test the DACHS principles.
This upload contain synthesis data from AutoMOFs_1 in HDF5 format (.h5). Each .h5 file contains detailed information on the chemical, experimenal, and synthesis parameters used during the synthesis of a single AutoMOF sample.
This tutorial is aimed at developers who would like to develop workflows with Jobflow. This could include contributions to atomate2 and quacc. Jobflow workflows can also be executed with Fireworks on Supercomputers.
This tutorial includes information on how to write a job for jobflows, how to connect jobs to a workflow including dynamic features and how to store job results in databases. The structure of the workflow is inspired by workflows that have been developed for atomate2 and quacc.
This tutorial is also connected to google collab so that you can execute the code via their services.
Please access the tutorial here: https://jageo.github.io/Advanced_Jobflow_Tutorial/intro.html
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
McSAS3
(2023)
McSAS3 is a refactored version of the original McSAS (see DOI 10.1107/S1600576715007347). This software fits scattering patterns to obtain size distributions without assumptions on the size distribution form. The refactored version has some neat features:
- Multiprocessing is included, spread out over as many cores as number of repetitions!
- Full state of the optimization is stored in an organized HDF5 state file.
- Histogramming is separate from optimization and a result can be re-histogrammed as many times as desired.
- SasModels allow a wide range of models to be used
- If SasModels does not work (e.g. because of gcc compiler issues on Windows or Mac), an internal sphere model is supplied
- Simulated data of the scattering of a special shape can also be used as a McSAS fitting model. Your models are infinite!
- 2D fitting also works.
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.