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Katherine Hollingsworth
5 days ago4 min read

Easily Create and Publish Materials Simulation Custom Protocols with MedeA Flowcharts

Updated: 5 days ago


At-a-Glance:


MedeA Flowcharts make it easy to design and conduct systematic computational materials science studies with a visual programming interface. As an illustration, we present here a custom flowchart that generates structures to match powder diffraction data.


By leveraging the integrated MedeA Environment, Flowcharts allow you to combine methods intuitively to study and compare algorithms, and to generate properties directly from structures.


Visual Programming:


Visual programming has been used to create efficient computational protocols for many years [1]. MedeA Flowcharts are often used to carry out high throughput calculations but can be used to develop algorithms and protocols too. The figure below is a Flowchart that generates atomistic trial structures, simulates the corresponding powder diffraction patterns, and then uses simulated annealing and optimization methods to create atomistic models that most closely match a target powder diffraction pattern[2].



Figure 1. A Flowchart to create models that reproduce supplied powder diffraction data. Synthetic powder diffraction data, based on the neutron diffraction study of Bacon and coworkers [3], form the target for the annealing and minimization steps.
Figure 1. A Flowchart to create models that reproduce supplied powder diffraction data. Synthetic powder diffraction data, based on the neutron diffraction study of Bacon and coworkers [3], form the target for the annealing and minimization steps.

With this MedeA Flowchart, you can investigate the choice of different variables in generating models for molecular crystals. Here are some illustrations of its use:


1. An initial simulation using three individual atom positions as variables (so 9 variables in total):



2. A simulation employing a lower symmetry and six atom positions as variables (18 variables):


3. A simulation where rigid body translations and rotations of a single benzene (6 variables) ring within the symmetric unit cell were employed:


4. A simulation where rigid body translations and rotations of four benzene rings (24 variables) in the unit cell with only translational symmetry were used:



5. Finally, a simulation where 36 atom positions were used as adjustable parameters (108 variables):


Not surprisingly, the best performance was returned by the flowchart using the smallest number of variables (the third video). This Flowchart-based study allows you to quantify the effect of such parameters on the performance of a method. The results are stored on your MedeA JobServer for future reference, for reuse of the Flowchart, and for sharing of all calculation details with colleagues - so results can be reproduced and extended with ease.


And once you have a molecular crystal structure, you can compute a wide range of properties based on that structure. If you have an indexed powder pattern, and you initiate a VASP based DFT structural optimization for any of the structures shown above within that unit cell, you will generate a structure which is within about 0.04Å RMS of the experimental coordinates reported by Bacon and coworkers. Such calculations take just a few minutes. And with a few minutes more, you can compute elastic properties for a crystal. A MedeA-VASP MT calculation for this system, using the Bacon et al unit cell parameters (to appropriately capture the temperature dependence of the material's volume) yields a computed c11 of 6.71 versus the experimental value for c11 of 6.14 GPa [2], using PBE with a D3 Grimme dispersion correction term[4].


Summary:


You can rapidly proceed from an analytical measurement, in this case an experimental powder pattern, to detailed structural knowledge, and the resulting physical properties using the MedeA Environment. And if you would like to add additional points of comparison with your analytical probes to the flowchart, you simply add the appropriate stages. To learn more about MedeA Flowcharts, request a product tour or email at info@materialsdesign.com.


References and Notes


1. Examples of early and successful visual programming environments: AVS from Stellar & later Stardent Computing (http://vis.cs.brown.edu/docs/pdf/Upson-1989-AVS.pdf,https://bitsavers.org/pdf/stardent/002424-001_Rev_A_Application_Visualization_System_Users_Guide_1989.pdf) and SanScript from Northwood Software (http://www.sanscript.net/)


2. Studies that have used simulated annealing to tackle structure solution in powder diffraction over the years:


Deem, M. W., & Newsam, J. M. (1992). Framework crystal structure solution by simulated annealing: test application to known zeolite structures. Journal of the American Chemical Society, 114(18), 7189-7198.


Newsam, J. M., Deem, M. W., & Freeman, C. M. (1992). Accuracy in powder diffraction II. NIST special publication, 846, 80-91.


Ramprasad, D., Pez, G. P., Toby, B. H., Markley, T. J., & Pearlstein, R. M. (1995). Solid state lithium cyanocobaltates with a high capacity for reversible dioxygen binding: Synthesis, reactivity, and structures. Journal of the American Chemical Society, 117(43), 10694-10701.


David, W. I. F., Shankland, K. & Shankland, N. (1998). Routine determination of molecular crystal structures from powder diffraction data. J. Chem. Soc. Chem. Commun., 1998, 931- 932.


Engel, G. E., Wilke, S., König, O., Harris, K. D. M., & Leusen, F. J. J. (1999). PowderSolve–a complete package for crystal structure solution from powder diffraction patterns. Journal of applied crystallography, 32(6), 1169-1179.


Sherwood, A. M., Kargbo, R. B., Kaylo, K. W., Cozzi, N. V., Meisenheimer, P., & Kaduk, J. A. (2022). Psilocybin: crystal structure solutions enable phase analysis of prior art and recently patented examples. Acta Crystallographica Section C: Structural Chemistry, 78(1), 36-55.


Kaduk, J. A., Rost, M. M., Dosen, A., & Blanton, T. N. (2024). A proposed crystal structure of lifitegrast sesquihydrate Form A,(C29H24Cl2N2O7S) 2 (H2O) 3. Powder Diffraction, 39(4), 275-282.


3. Experimental sources used here:


Bacon, G. E., Curry, N. T., & Wilson, S. A. (1964). A crystallographic study of solid benzene by neutron diffraction. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 279(1376), 98-110.


Heseltine, J. C., Elliott, D. W., & Wilson Jr, O. B. (1964). Elastic constants of single‐crystal benzene. The Journal of Chemical Physics, 40(9), 2584-2587.


4. Key MedeA VASP DFT methodologies employed:


Perdew, J. P., Burke, K., & Ernzerhof, M. (1996). Generalized gradient approximation made simple. Physical review letters, 77(18), 3865.


Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010). A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. The Journal of chemical physics, 132(15).

 



 

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