CREASE: Computational Reverse Engineering Analysis for Scattering Experiments

Image above shows CREASE-2D workflow.

If you are interested in learning about CREASE, we ask that you first visit the OVERVIEW page in this document READMEDOCS

If you are interested in joining a future CREASE tutorial, please email Prof. Jayaraman – arthij@udel.edu

Latest codes are for CREASE-2D available at CREASE-2D github page. 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. 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

Here is an image of what a GPT-4o agent replies to a query “explain the CREASE-2D approach for interpreting scattering profiles”!

We recommend that any user considering CREASE should read a couple of the latest CREASE papers to understand the workflow and how it is adapted to each scientific problem. Here are all CREASE publications in chronological order with a brief explanation of what they describe. The earlier papers do not have the machine-learning enhancement which was incorporated in the later years.

  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

Acknowledgements:

We are grateful for funding from these programs that supported early developments of CREASE:

NSF CMMT (2021 – 2025)

NSF DMREF (2016-2021)

NSF CBET (2020 – 2024)

MURI – AFOSR – Muri Melanin (2018- 2023)

NSF-MRI funded DARWIN supercomputer

Print Friendly, PDF & Email