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(127b) The Search for Novel Mesoscale Materials

Cersonsky, R. - Presenter, EPFL STI IMX COSMO
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.
  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.
  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.
  7. G. Fraux, R. K. Cersonsky, M. Ceriotti, "Chemiscope: interactive structure-property explorer for materials and molecules." (2020). Journal of Open Source Software.
  8. R. K. Cersonsky , G. Fraux , S. Kliavinek , A. Goscinski , B. A. Helfrecht , M. Ceriotti, “scikit-COSMO”, (2020).
  9. M. Pahknova, R. K. Cersonsky, M. Ceriotti, "Identifying High-Stability Components of Molecular Crystals." In Preparation.