Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications.

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning

Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications.

Normal Mode Analysis of The Thermal conductivity in Amorphous Polymers: The Importance of Localized Modes

Polymers are a unique class of materials from the perspective of normal mode analysis. Polymers consist of individual chains with repeating units and strong intra-chain covalent bonds, and amorphous arrangements among chains with weak inter-chain van der Waals and for some polymers also electrostatic interactions. Intuitively, this strong heterogeneity in bond strength can give rise to interesting features in the constituent phonons, but such effects have not been studied deeply before. Here, we use lattice dynamics and molecular dynamics to perform modal analysis of the thermal conductivity in amorphous polymers for the first time. We find an abnormally large population of localized modes in amorphous polymers, which is dramatically different from amorphous inorganic materials. Contrary to the common picture of thermal transport, localized modes in amorphous polymers are found to be the dominant contributors to thermal conductivity. We find that a significant portion of the localization happens within individual chains, but heat is dominantly conducted when localized modes involve two chains. These results suggest that even though each polymer is different, localized modes play a key role. The results provide new perspective on why polymer thermal conductivity is generally quite low and gives insight into how to potentially change it.

DYNAMIC THREE-POINTS-BENDING TEST MODE OF TWO AMORPHOUS POLYMER MATERIALS (PMMA, PC) FOR THEIR VISCOELASTIC AND MECHANICAL CHARACTERIZATION

This paper presents an approach to classifying amorphous polymer materials. Temperature is This classification involves the determination of mechanical and viscoelastic characteristics considered a descriptive variable to clarify the specific field of practical applications of amorphous polymers. according to the reference temperature characterizing the behaviour of polymer materials. The mechanical and viscoelastic characteristics of amorphous polymers such as methyl poly-methacrylate (PMMA), polycarbonate (PC) and imide poly ether (PEI) are determined through the three-point dynamically embedded test carried out in an adiabatic close enclosure. The complex dissipative or conservative modules according to the temperature are represented. The results obtained show that the fluidity index of these materials is linked to their viscosity, which is a determining property which is decisive for the choice of the technique of the application of the material. Our method of measuring properties is therefore, in principle, comparable to the techniques used in industrial development.

Recent progress in electro-optic polymer for ultra-fast communication

The rocketed development concerning electro-optic polymers fundamentally motivated by its pragmatic application in envisioning second-order nonlinear optics and waveguiding are cardinal. Modern synthetic strategies consigned an outstanding optical quality amorphous polymers with enhanced properties. Documented data revealed a huge progress in understanding their implementation, however challenges still exist regarding their temporal stabilities etc. This review delivers a brief investigation of nonlinear optical (NLO) polymer materials demonstrated over previous decades. Besides, their categorical explanation along with their structural architecting via engineering polymeric backbone or functionalization of the molecular entities have been reviewed. Correspondingly, their temporal and thermal stabilities accompanied by NLO characteristics features are also discussed.