Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session B01: Machine Learning and Data in Polymer PhysicsInvited Live
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Sponsoring Units: DPOLY Chair: Debra Audus, National Institute of Standards and Technology |
Monday, March 15, 2021 11:30AM - 12:06PM Live |
B01.00001: Recent Advances on AI for Polymer Design Invited Speaker: Juan De Pablo Advanced optimization and machine learning algorithms offer considerable promise for design of polymeric materials. The sequence space that is available for polymer design is extraordinarily large, and a central question that arises is whether it is possible to develop strategies that rely on a limited data set to train networks that can then be used to not only predict the properties of molecules having a given sequence, but to generate sequences that lead to desirable properties. In this work, that question is addressed by relying on simulations of model polymers that include different backbone and side groups to generate a numerical data base of molecules with different sequences. We also consider the extent to which such approaches can be extended to realistic polymeric materials, and the challenges that one faces in representing polymeric materials in a manner that is tractable and effective for polymer design. |
Monday, March 15, 2021 12:06PM - 12:42PM Live |
B01.00002: DATA CENTRIC NANOCOMPOSITES DESIGN VIA MIXED-VARIABLE BAYESIAN OPTIMIZATION Invited Speaker: Wei Chen With an unprecedented combination of mechanical and electrical properties, polymer nanocomposites have the potential to be widely used across multiple industries. Tailoring nanocomposites to meet application specific requirements remains a challenging task, owing to the vast, mixed-variable design space that includes composition (i.e. choice of polymer, nanoparticle, and surface modification) and microstructures (i.e. dispersion and geometric arrangement of particles) of the nanocomposite material. Modeling properties of interphase, the region surrounding a nanoparticle, introduces additional complexity to the design process and requires computationally expensive simulations. As a result, previous attempts at designing polymer nanocomposites have focused on finding the optimal microstructure for only a fixed combination of constituents. In this talk, we present a data centric design framework to concurrently identify optimal composition and microstructure using mixed-variable Bayesian Optimization. This framework integrates experimental data with state-of-the-art techniques in interphase modeling, microstructure characterization & reconstructions and machine learning. Latent Variable Gaussian Processes (LVGPs) quantifies the lack-of-data uncertainty over the mixed-variable design space that consists of qualitative and quantitative material design variables. The design of electrically insulating nanocomposites is cast as a multicriteria optimization problem with the goal of maximizing dielectric breakdown strength while minimizing dielectric permittivity and dielectric loss. Within tens of simulations, our method identifies a diverse set of designs on the Pareto frontier indicating the tradeoff between dielectric properties. These findings project data centric design, effectively integrating experimental data with simulations for Bayesian Optimization, as an effective approach for design of engineered material systems. |
Monday, March 15, 2021 12:42PM - 1:18PM Live |
B01.00003: Optimization of organic molecules and macromolecules using machine learning Invited Speaker: Yaroslava Yingling The number of applications of data-driven materials discovery is rapidly growing. A large amount of available materials characterization and computational data, combined with high-level statistical algorithms, is proving to be extremely useful in developing complex predictive models. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields mainly due to the complexity of underlying processes and plethora of processing components that dictates the properties. In this talk, I will discuss the application of machine learning (ML) technique for optimization of ligand functionalized nanoparticles (NPs) and biopolymers. In our approach, we use a combination of high-throughput molecular dynamics simulations and data available from the literature to train the ML model. We address the uncertainty associated with MD simulations in the development of the model. Using this approach, we were able to design novel nanoparticle ligands capable of specific desired properties driven by the optimization function. Our methods can significantly speed up the search for a new organic materials. |
Monday, March 15, 2021 1:18PM - 1:54PM Live |
B01.00004: Molecular Simulations Integrated Machine Learning Study of Bottlebrush Polymers Invited Speaker: Sanket Deshmukh Thermosensitive bottlebrush polymers (BBPs) are a type of graft polymers in which thermosensitive polymer side-chains are grafted to a polymer backbone. Most of these thermosensitive polymers with lower critical solution temperature (LCST) can undergo a coil-to-globule conformational transition with increasing temperature. This further results in a change in the overall shape of the BBPs, which is one of the most important properties needed in many biomedical applications including drug delivery, molecular actuators, etc. In this talk, I will discuss our recent coarse-grained (CG) molecular dynamics (MD) simulations study of poly(N-isopropylacrylamide) (PNIPAM; LCST= 305 K) BBPs of four different shapes: 1. Worm-like, 2. Cone-like, 3. Cake-like and 4. Dumbbell-like. The CG MD simulations were performed at 290 K (below LCST) and 320 K (above LCST) in the presence of explicit CG water for 500 ns. The similarities and differences in the shapes of these BBPs were quantified by analyzing the simulation trajectories using machine-learning. Unexpectedly, both Cone-like and Cake-like BBPs were more similar to that of Worm-like BBPs compared to the Worm-like and Dumbbell-like BBPs. |
Monday, March 15, 2021 1:54PM - 2:30PM On Demand |
B01.00005: Polymer Informatics: Current Status & Critical Next Steps Invited Speaker: Rampi Ramprasad The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction models, and algorithms for designing polymers meeting target property requirements. These efforts have culminated in the creation of an online Polymer Informatics platform (https://www.polymergenome.org) to guide ongoing and future polymer discovery and design. Challenges that remain will be examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains will be discussed. These include strategies to deal with the data bottleneck, new methods to represent polymer morphology and processing conditions, and the applicability of emerging AI algorithms for materials design. |
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