Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

Abstract

We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches. File list (2) download file view on ChemRxiv Accelerated Discovery of High-Refractive-Index Polyimide... (1.02 MiB) download file view on ChemRxiv R1_R2_PI_screening_results.xlsx (10.96 MiB)

Elucidating the Relationship between Multiradical Character and Predicted Singlet Fission Activity

“…Our previous work attempted to address this by testing, theoret-ically, a large set of known organic compounds with reduced or no pre-design, [24] similar to other high-throughput virtual screening efforts. [25][26][27][28][29][30][31][32][33][34][35] The 40 K organic molecules were taken from the Cambridge Structural Database (CSD), [36] reduced from a total set of almost one million structures. The outcome of the high-throughput screening yielded hundreds of novel structures which satisfy the main energy criterion E(S 1 ) � 2•E(T 1 ) with optically active S 1 .…”

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

“…A monomer structure of PIs is determined by two basic blocks called R1 and R2 [99], as shown in Figure 6a. The properties of PIs can be tuned by controlling these two basic blocks [100]. In the design of basic blocks, there are still problems to solve.…”

“…Second, screening all the candidates by traditional methods is cumbersome and not cost-effective. Recent publications have showed that utilization of ML algorithm and first principle calculations can accelerate the discovery of promising PIs [100].…”

“…As mentioned in the case study of polymer dielectrics, seven building blocks were used for combinatorial generation of monomers [9]. Additionally, in the case study of polyimides design with high RIs, two types of building fragments were adopted to generate PIs [100,102]. The idea is that some building units or functional groups characterize main features of specific materials with desired properties.…”

“…Accelerated design of polyimides (PIs) with high refractive index. (a) The core structure of PI with R1 and R2 group (blue and red nodes denote nitrogen and oxygen atoms, respectively); (b) the performance of the support vector regression (SVR) model (adapted from Reference[102] with permission, copyright 2018 AIP publishing); (c) 29 building blocks; (d) the distribution of molecules versus the RI values for R1, R2, and PIs; (e) RI values in terms of each building block for R1 and R2; (f) Z-score of building pairs for R1 and R2 (c-f) are adapted from Reference[100] with permission, copyright 2019 American Chemical Society.…”