You are currently viewing the github page for the previous version of CREASE (2019 - 2023) which is relevant to publications [1-8] listed below.

Please visit our readthedocs.io page for detailed introduction on CREASE and the philosophy behind it.

If you are interested in using the current version (2024-present) of CREASE relevant to publication [9], for analyzing 1D or 2D scattering profiles [e.g., I(qx, qy)], please proceed to the github page for CREASE-2D.

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If you use this code, please cite one or more of the relevant references from the list below:

1. Original Article on CREASE for spherical micelles:

   Beltran-Villegas, D. J.; Wessels, M. G.; Lee, J. Y.; Song, Y.; Wooley, K. L.; Pochan, D. J.; Jayaraman, A. Computational Reverse-Engineering Analysis for Scattering Experiments on Amphiphilic Block Polymer Solutions. J. Am. Chem. Soc. 2019, 141, 14916−14930. link to article

2. Extension of CREASE for cylindrical and elliptical micelles:

   Wessels, M. G.; Jayaraman, A. Computational Reverse-Engineering Analysis of Scattering Experiments (CREASE) on Amphiphilic Block Polymer Solutions: Cylindrical and Fibrillar Assembly. Macromolecules 2021, 54, 783-796. link to article

3. Machine Learning Enhanced CREASE:

   Wessels, M. G.; Jayaraman, A. Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions. ACS Polym. Au 2021, 1, 3, 153-164. link to article

4. Extension of CREASE's Genetic Algorithm Step to Handle Structure Factors:

   Heil, C. M.; Jayaraman, A. Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles. ACS Mater. Au 2021, 1, 2, 140-156. link to article

5. Extension of CREASE for vesicles as well as the the ability to estimate polydispersity in dimensions of the domains in the assembled structure and distribution of molecules between the different domains of the assembled structure:

   Ye, Z.; Wu, Z.; Jayaraman, A. Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular Solutions. JACS Au 2021, 1, 11, 1925-1936. link to article

6. Machine Learning Enhanced CREASE for determining structure (e.g., extent of mixing/demixing, particle aggretation/dispersion) of nanoparticle mixtures and solutions:

   Heil, C. M.; Patil, A.; Dhinojwala, A.; & Jayaraman, A. Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions. ACS Central Science 2022, 8, 7, 996-1007. link to article

7. Machine Learning Enhanced CREASE for Semi-flexible Fibrils:

   Wu, Z. & Jayaraman, A. Machine learning enhanced computational reverse-engineering analysis for scattering experiments (CREASE) for analyzing fibrillar structures in polymer solutions. Macromolecule 2022, 55, 24, 11076-11091. link to article

8. Machine Learning Enhanced CREASE for Simultaneous Form Factor and Structure Factor Elucidation for Concentrated Macromolecular Solutions (e.g., micelles, polymer coated nanoparticles):

   Heil, C. M.; Ma, Y.; Bharti, B.; & Jayaraman, A. Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination ('P(q) and S(q) CREASE'). JACS Au 2023, 3, 3, 889-904. link to article

9. Extension of the CREASE method to analyze 2D scattering profiles (CREASE-2D). CREASE-2D outputs the relevant structural features that can be used to interpret 3D structure. The identified structural features provide information of shapes, sizes and orientational order of particles, which is useful to understand structural anisotropy. More information also available at CREASE-2D github page.

   Akepati, S. V. R.; Gupta, N.; Jayaraman, A., Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D). JACS Au 2024, 4, 1570-1582. link to article

Installation

To install this package on a Windows, linux, or macOS machine, follow these steps:

1. We will be using Anaconda to handle the python environment. So first, we need to install anaconda on our machine. You can also consider installing miniconda for faster installation and lower storage consumption only if you are an advanced user. We recommend installing the full version of anaconda if you have limited experience with python and/or computer programming.

2. At this point, you will need a bash terminal to work with. All the installation steps after this step will need to be accomplished from a terminal, using command line interface.

   • If you are on a linux or MacOS machine

     You can directly launch a terminal.

   • If you are on a Windows machine

     If you have installed the full version of Anaconda/Miniconda in Step 1, the most straightforward way to launch a terminal will be using Anaconda prompt that comes with the conda installation. You should be able to find Anaconda prompt from your Start menu. You can also consider installing Windows Subsytem for Linux (WSL).

3. Download the package.

   git clone https://github.com/arthijayaraman-lab/crease_ga
   

   • You can also directly download the package as a ZIP file from our github webpage by following the guidance here. If you are following this route, you will need to unzip the package to your desired location after the download.

4. Create a new conda environment and install the package and all its dependencies.

   • Navigate into the root directory of the cloned package. If you are using the anaconda prompt, you can look up common windows command line prompts. If you are using a unix-based terminal (linux, macOS, or WSL subsystem), you can look up common commands for Unix. Either case, all you will need would probably be displaying list of files and directories in the current folder(dir for windows, ls for unix), and moving to different directories(cd [new_directory_path] for both windows and unix). You should end up at a directory called crease_ga and be able to see files named setup.py, environment.yml and README.md (among others) in the directory.
   • create a fresh conda environment with the package and its dependencies installed using

     conda env create -f environment.yml
     

   • Activate the environment with

     conda activate crease_ga
     

To test if the crease-ga package is installed properly, run

python3

to launch python, and then in the resulting python command line, run

import crease_ga
crease_ga.__version__

If all installation steps are done properly, you should see the version number of the package printed, and you are all set to use the crease_ga package! Remember to activate the proper environment every time by with conda activate crease_ga.

• NOTE1: We are in the process of preparing a new tutorial based on recent advances in machine learning enhanced CREASE. It will be available in September 2023. We plan to offer a workshop and tutorial in September 2023. If you are interested in attending that, please email us at arthij AT udel.edu.

• NOTE2: if you intend to run this on a supercomputing cluster, you will need to follow the steps to create a python environment of the corresponding cluster.

Contact us

If you have any questions or feedback, please let us know by emailing arthij AT udel.edu.