Last Updated: May 11, 2023
1. Introduction to CREASE
Members of Prof. Arthi Jayaraman’s research lab have developed the ‘Computational Reverse-Engineering Analysis for Scattering Experiments’ (CREASE) method to address these needs for alternate scattering analysis methods that are applicable to both conventional soft materials structures with existing analytical models and unconventional structures/chemistries that may not have good analytical models.
Figure 1 shows the general workflow in CREASE (as of summer 2023) where experimentally measured 1D scattering profiles are taken as input and CREASE, through an internal optimization, generates as output the key structural features as well as representative 3D real space structures whose computed scattering profiles match the experimental scattering input. If you are interested in this method, you may wish to watch Prof. Jayaraman’s recently recorded lecture on CREASE and its uses. The lecture can be found in this link.
2. Guiding Philosophy
CREASE’s workflow is based on the philosophy that the real-space three-dimensional (amorphous) arrangement of constituents in soft materials can be reduced to a lower dimensional mathematical representation of key ‘structural features’ and that the distributions of those structural features give rise to a computed scattering profile. For example, for a system with core-corona spherical micelles at low concentrations, these structural features could be sizes of core and corona and probability distributions of those sizes. At higher concentration, there would be additional structural features that describe the relative neighborhood of each micelle, for example through mathematical order parameters describing positional and orientational order. The user can decide the types of structural features they are interested in (e.g., any fundamentally interesting structural information and/or structural features that the researcher knows will impact the soft materials’ eventual application). Once the users have decided on the key structural features they are interested in, they can use CREASE to run an optimization loop where it iterates over various sets of structural features. In the optimization loop, for each set of structural features CREASE i) calculates the computed scattering profile, Icomp (q) (more about this calculation below), and ii) compares the Icomp (q) profile to the experimental (input) profile, Iexp (q), eventually converging towards the sets (note the intentional use of plural!) that have Icomp (q) profiles most closely matching the input Iexp (q).
3. CREASE-GA Implementation
As of summer 2023, CREASE has been implemented in a python code using a simple optimization method - genetic algorithm. CREASE’s genetic algorithm (CREASE-GA) takes as input 1D SAXS and/or SANS scattering profile from amorphous soft materials structures. It also requires the user’s choice of the types of structural features (i.e., ‘genes’ of the ‘individuals’ in GA) based on their knowledge of the general shape of the assembled structure from other imaging techniques and/or subject matter expertise. Then, CREASE-GA starts with an initial ‘generation’ of multiple sets of structural features (i.e., multiple ‘individuals’ with specific values of ‘genes’) and iterates in the GA loop towards the optimal individuals whose genes gives rise to a computed scattering profile, Icomp (q), that closely matches the input experimentally measured scattering profile, Iexp (q). One important calculation in this loop is the Icomp (q) for a given set (‘individual’) of structural features (‘genes’); this has been done so far in one of two ways (Figure 2). One way (let us call it Debye method) is by creating for each set of genes their representative three-dimensional real space structures filled with point scatterers whose scattering length densities represent the constituents of the system, and using the Debye equation on the scatterer positions to compute Icomp (q). This way can be computationally intensive either due to the structure generation step or the Debye calculation despite computational tricks [1-3]. Another way is by using a machine learning (ML) model that links the structural features directly to Icomp (q); Jayaraman and coworkers have used neural networks trained on thousands of computed scattering profiles calculated from the Debye method for various sets of genes. Using this ML model for Icomp (q) calculation can give orders of magnitude speed up over the Debye method, after the initial time investment of training the ML model.
4. How has CREASE been used so far?
As of summer 2023, CREASE method has been used to interpret small angle scattering results to
Identify relevant dimensions of assembled structures in polymer solutions at dilute concentrations [5-9]: CREASE has been applied to characterize structure of the ‘primary particle’ using scattering profiles I(q) ~ P(q) (i.e., conditions where S(q) is ~1) for a variety of ‘primary particles’ (micelles [6, 7, 9], vesicles , and fibrils ) bypassing the need for an analytical model.
Understand the amorphous structure of spherical particles at high concentrations regardless of extent of mixing/segregation: CREASE has also been extended to analyze S(q) part of the scattering profiles from concentrated binary mixture of polydisperse spherical nanoparticles (i.e., P(q) is a sphere form factor) to determine the extent of segregation/mixing of the two types of nanoparticles and the precise mixture composition [4, 10].
Elucidate the amorphous structure of particles / micelles in solutions, with unknown primary particle form and unknown assembled/dispersed structure : Most recently, for systems where one does not know the P(q) or S(q) a priori, CREASE has been extended to simultaneously interpret structural information held in P(q) and S(q) and appropriately called ‘P(q) and S(q) CREASE’ .
CREASE has taken as input 1D SAXS profiles and/or SANS profiles: In the studies above, the input to CREASE has been (i) a single SAXS profile of the system, or (ii) one SAXS profile and a one SANS profile of the same system, or (iii) multiple SANS profiles with contrast matching one or the other component(s) in the system with the solvent. Next development steps of CREASE development are focused on 2D profiles for soft materials that show anisotropy in the assembled structure.
CREASE with Debye method vs. ML-model for computed scattering profile calculation: In earlier implementations of CREASE, the Debye method for computed scattering profile calculation was used; as noted above this calculation was initially found to be quite time consuming. In following work, the structure generation (done in every step of Debye method) was found to more computationally intensive while the computed scattering calculations using Debye method have been made faster than in previous implementations. The machine learning (ML) enhanced CREASE-GA, with a well-trained ML model avoids both Debye equation based computed scattering calculation and the three-dimensional real space structure generation in the optimization loop, making is significantly faster than using Debye method (e.g., one can complete CREASE-GA optimization is less than an hour on a laptop with a pre-trained ML model!)
5. Unique advantages of CREASE
Here are some unique advantages of CREASE-GA regardless of availability of appropriate analytical models for the system being characterized:
The computed scattering profile calculation is done using scatterer placement within structures defined by the ‘genes’ (i.e., structural features). This treats all soft materials systems in the same way as being composed of scatterers with no detail about the molecules. So, even in the case of polymer chains, there are no chains in this GA step – only scatterers. This overcomes issues one may have not knowing anything about chain conformations (e.g., is Gaussian distribution of chain conformations valid or not?). If one needs information about the chain configurations they can follow up this GA step with an molecular simulation step using models (coarse-grained or atomistic) representing polymers.
Any structural feature of interest can be a ‘gene’; for the same system, two different users may be interested in different structural features. For example, in the case of vesicles (Figure 2), one user may be interested in all four dimensions (core radius and thickness of every individual layer in the shell leading to four ‘genes’) and another user may be interested simply in the core radius and shell thickness. Further, some structural features the user may be interested in may not be in any existing analytical model. Note: If and how well CREASE can identify a structural feature reliably from an input experimental scattering profile will depend on how much that structural feature affects the computed scattering profile and how the computed scattering profile changes with the values of the structural features. See for example recent work on analysis of scattering results from methylcellulose fibrils from Wu and Jayaraman . In that system, the length, Kuhn length (KL), and diameter of fibrils are the structural features of interest, however Wu and Jayaraman showed that KL values could only be identified well if they were within a certain range of values for the methylcellulose systems. Such sensitivity analysis is very useful in deciding on the genes used in the optimization.
Genetic algorithm (GA) is the chosen optimization method here because it is easy for others to adopt regardless of prior computational knowledge and experience. Furthermore, GA’s output contains multiple individuals whose computed scattering matches experimental scattering. This is useful as it informs us about the degeneracy of solutions for a given experimental profile; in other words, there can be many different structures whose computed scattering profile can match with experiments, so knowing this distribution from the converged ‘best match’ individuals in the last couple of generations of GA is valuable.
CREASE also gives as output representative real-space structures – either as is because the system is made of particles whose positions can be generated from scatterer positions or via additional molecular modeling and simulation step to show chain conformations in the structures output from CREASE-GA (e.g., Wessels et al. ). These structures can then be used as an input for other non-equilibrium simulations or calculations of properties that depend on structure (e.g., resistor network model calculation for electrical conductivity12 and finite-difference time-domain method for optical properties [13, 14]).
One major advantage of Machine learning (ML) enhanced CREASE-GA is the computational speed up. As a result, ML-enhanced CREASE-GA can facilitate high-throughput analysis of related systems as long as the trained ML model, in particular the structural features that are inputs to the ML model, are valid for those related systems.
CREASE can be used to test the researcher’s hypotheses about how the soft materials structures of interest form/evolve with changing conditions. The user is directed to examples of hypothesis testing in the studies presented in Refs. [5, 11, 15]
Brisard, S.; Levitz, P., Small-angle scattering of dense, polydisperse granular porous media: Computation free of size effects. Phys. Rev. E 2013, 87 (1), 013305. (link)
Olds, D. P.; Duxbury, P. M., Efficient algorithms for calculating small-angle scattering from large model structures. Journal of Applied Crystallography 2014, 47 (3), 1077-1086. (link)
Schmidt-Rohr, K., Simulation of small-angle scattering curves by numerical Fourier transformation. Journal of Applied Crystallography 2007, 40 (1), 16-25. (link)
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 Cent. Sci. 2022, 8 (7), 996-1007. (link)
Wu, Z.; Jayaraman, A., Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions. Macromolecules 2022, 55 (24), 11076-11091. (link)
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 (37), 14916-14930. (link)
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 (2), 783-796. (link)
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)
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)
Heil, C. M.; Jayaraman, A., Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles. ACS Materials Au 2021, 1 (2), 140. (link)
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)
White, S. I.; DiDonna, B. A.; Mu, M.; Lubensky, T. C.; Winey, K. I., Simulations and electrical conductivity of percolated networks of finite rods with various degrees of axial alignment. Physical Review B 2009, 79 (2), 024301. (link)
Patil, A.; Heil, C. M.; Vanthournout, B.; Bleuel, M.; Singla, S.; Hu, Z.; Gianneschi, N. C.; Shawkey, M. D.; Sinha, S. K.; Jayaraman, A., Structural Color Production in Melanin‐Based Disordered Colloidal Nanoparticle Assemblies in Spherical Confinement. Advanced Optical Materials 2021, 2102162. (link)
Patil, A.; Heil, C. M.; Vanthournout, B.; Singla, S.; Hu, Z.; Ilavsky, J.; Gianneschi, N. C.; Shawkey, M. D.; Sinha, S. K.; Jayaraman, A.; Dhinojwala, A., Modeling Structural Colors from Disordered One-Component Colloidal Nanoparticle-based Supraballs using Combined Experimental and Simulation Techniques. ACS Materials Letters 2022, 4 (9), 1848-1854. (link)
Lee, J. Y.; Song, Y.; Wessels, M. G.; Jayaraman, A.; Wooley, K. L.; Pochan, D. J., Hierarchical Self-Assembly of Poly(D-glucose carbonate) Amphiphilic Block Copolymers in Mixed Solvents. Macromolecules 2020, 53 (19), 8581-8591. (link)
If you have any questions or feedback, please let us know by emailing creasejayaramanlab AT gmail.com.