(127b) The Search for Novel Mesoscale Materials

The design of new materials has often relied on crystal structure as a primary source for design complexity and innovation, requiring new crystal structures and new manners of constructing and synthesizing these structures. A promising mechanism for synthesis is self-assembly, the spontaneous emergence of order due to particle interactions. Nanoparticle assembly has also been identified as a favorable synthesis route in the photonics community, among others, as nanoscale photonic crystals would reflect wavelengths within the visible range. The effort to self-assemble photonic crystals has been fraught with obstacles — from difficulty in synthesizing the diamond structure, the holy-grail within the photonics community, to the degradation of photonic band gaps with thermal noise within the assembly.

In this talk, I will first discuss the unexpectedly diverse range of crystallographic structures that will support a photonic band gap, determined by more than 150,000 photonic band structure calculations that identify nearly 300 previously unstudied targets for photonic crystals(1). I will demonstrate avenues to predict the polyhedral nanoparticles that can cleanly self-assemble these targets and the implications, both good and bad, that small changes to nanoparticle shape have on the resulting crystal’s behavior (2-4).

Furthermore, the so-called machine learning revolution has bolstered the study of mesoscale self-assembly; these advanced statistical methods can accelerate simulations or aid in their analysis. In the final section of my talk, I will discuss my most recent work focused on developing machine learning methods for atomic systems(5,6). I will cover my efforts developing open-source software(7,8) and the employment of these methods for learning the behavior of hierarchical materials(9). The utility of these methods is in no way limited to the atomic scale; I will end with my plan for extending machine learning representations and techniques developed for atomic-scale simulations to mesoscale studies and the open questions that these techniques will address.

1. R. K. Cersonsky, J. Antonaglia, B. D. Dice, S. C. Glotzer, "Unexpected Diversity of Three-Dimensional Photonic Crystals.” Accepted for publication at Nature Communications.
2. R. K. Cersonsky, G. van Anders, P. M. Dodd, and S. C. Glotzer, "Relevance of Packing in Colloidal Self-Assembly," (2018). Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1720139115.
3. R. K. Cersonsky, J. Dshemuchadse, J. Antonaglia, G. van Anders, S. C. Glotzer, "Pressure–Tunable Band Gap in an Entropic Crystal", (2018). Phys. Rev. Mat. https://doi.org/10.1103/PhysRevMaterials.2.125201.
4. Y. Zhou, R. K. Cersonsky, S. C. Glotzer, "A New Route to the Diamond Colloidal Crystal.” In preparation.
5. R. K. Cersonsky, B. Helfrecht, E. A. Engel, S. Kliavinek, M. Ceriotti, "Improved Data Sub-selection with Principal Covariates Regression." Submitted.
6. B. Helfrecht, R. K. Cersonsky, G. Fraux, M. Ceriotti, "Structure-property mapping with Kernel Principal Covariate Regression," (2020) Machine Learning: Science and Technology. https://doi.org/10.1088/2632-2153/aba9ef.
7. G. Fraux, R. K. Cersonsky, M. Ceriotti, "Chemiscope: interactive structure-property explorer for materials and molecules." (2020). Journal of Open Source Software. https://doi.org/10.21105/joss.02117.
8. R. K. Cersonsky , G. Fraux , S. Kliavinek , A. Goscinski , B. A. Helfrecht , M. Ceriotti, “scikit-COSMO”, (2020). https://scikit-cosmo.readthedocs.io/en/latest/
9. M. Pahknova, R. K. Cersonsky, M. Ceriotti, "Identifying High-Stability Components of Molecular Crystals." In Preparation.