CDS&E: Toward a Pattern Recognition Framework to Identify Reaction Coordinates for Order-Disorder Transitions: Application to Block Copolymers

Cornell University, Ithaca NY

Investigators

Abstract

NONTECHNICAL ABSTRACT This award supports computational research and education to develop a new method to identify the degree of organization contained in an arrangement of atoms or molecules as it changes from a disorganized or disordered arrangement to a more organized or ordered arrangement, particularly in computer simulations of materials. In the processing of many materials, from soft materials made of long chain-like molecules, or polymers, to inorganic solids like metal alloys, transformations often occur that involve changes in the nature of the way atoms or molecules self-organize from a more disordered arrangement to a more ordered arrangement. Such changes in internal order typically translate into substantial changes in the properties of the material. Among the many promising building blocks available to generate desirable ordered structures, block copolymers, a class of polymers, stand out because a variety of materials can be made from them with highly regular patterns and pores of nanoscale dimensions, for potential applications in the making of semiconductor films, filters, and conducting membranes. However, the realization of such highly regular structures is difficult and often involves more art than science. Paramount to understanding and controlling microscopic ordering is to have good "order parameters," quantities that researchers can use to monitor the progress towards the desired well organized or ordered state. Such order parameters distill a huge amount of microscopic and atomic-scale data into just a few crucial quantities; currently, they are developed in a case-by-case fashion, often involving significant computational effort and trial and error. In this project, the PI aims to advance a framework to help identify useful order parameters based on ideas from the area of pattern recognition. Just like effective computer programs have been developed and deployed for face recognition, for example, the PI aims to develop techniques for describing the key geometrical signatures that accompany the onset and propagation of microscopic order. The close collaboration of the PI with experimental groups will ensure a synergy between computational and experimental efforts. The project provides an environment to train and support a doctoral student and to provide research experience for two undergraduate students, one of whom will develop outreach educational modules. Results from this investigation will also be used in advanced chemical engineering modeling classes taught by the PI. NONTECHNICAL ABSTRACT This award supports computational research and education with the goal to apply and develop pattern recognition (PR) algorithms that would allow users to screen out and identify good descriptors of the transition kinetics from input trajectory data containing a large number of snapshots of configuration files of the system as it goes from one state of order to another. In particular, this proposal seeks pattern-recognition-based reaction coordinates to track the kinetics of order-disorder phase transitions involved in block copolymer systems, emphasizing the not-well understood processes that form bicontinuous phases like gyroid, double diamond, and plumbers nightmare, all made of periodic interweaving 3D networks. The PI envisions that by identifying appropriate reaction coordinates discovered in this way, a more complete and intuitive understanding will be attained of the ordering process and how the system could be steered into alternative pathways that lead to different stable and metastable states. The PI plans to execute the following activities: (a) Determine the conditions at which sought after bicontinuous phases form, a step entailing free energy simulations to discriminate stable from metastable phases and generation of the reference ordered structures of interest. (b) Simulate transition-path ensemble data for varying supercooling conditions. (c) Develop candidate pattern-recognition-based descriptors from the phases simulated in step (a), and assess their quality based on data collected in (b). (d) Apply the best reaction coordinate thus found to gain physical insight into the kinetics of the process and phase bifurcation. This investigation will provide pattern-recognition algorithms and pattern-recognition-based descriptors proven to be helpful in describing the formation kinetics of geometrically complex ordered phases. The method can find numerous new applications, including kinetic studies of thermal, laser and solvent annealing, and directed assembly of block copolymer thin films on patterned substrates.