Developing Highly Tough, Heat-Resistant Blend Thermosets Based on Silicon-Containing Arylacetylene: A Material Genome Approach

Abstract

Silicon-containing arylacetylene (PSA) resins exhibit excellent heat resistance, yet their brittleness limits the applications. We proposed using acetylene-terminated polyimides (ATPI) as an additive to enhance the toughness of the PSA resins and maintain excellent heat resistance. A material genome approach (MGA) was first established for designing and screening the acetylene-terminated polyimides, and a polyimide named ATPI was filtered out by using this MGA. The ATPI was synthesized and blended with PSA resins to improve the toughness of the thermosets. Influences of the added ATPI contents and prepolymerization temperature on the properties were examined. The result shows that the blend resin can resist high temperature and bear excellent mechanical properties. The molecular dynamics simulations were carried out to understand the mechanism behind the improvement of toughness. The present work provides a method for the rapid design and screening of high-performance polymeric materials.

Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers

“…The MGA, which comprises gene definition, gene combination into candidates, high-throughput screening, and experimental verification, has proven to be a powerful method for designing and screening polymers. , Figure a shows the procedure of our proposed MGA for the rapid design of EPs. Epoxide and amine were first defined as “genes” since they are two reactants in the synthesis of EPs.…”

Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning

“…In our previous work, several high-performance polymers screened using property surrogates were demonstrated to have remarkable properties. 17,18 For instance, Zhu et al developed a propertysurrogate-driven MGA to design heat-resistant poly(silane arylacetylene) (PSA) with low curing energies. In their study, they extracted two key features, the bond dissociation energy of the weakest bond and the highest occupied molecular orbitallowest unoccupied molecular orbital (HOMO-LUMO) gap, from the available data, which can represent the thermal stability and curing behavior of PSAs, respectively.…”

Efficiently predicting and synthesizing intrinsic highly fire-safe polycarbonates with processability

“…38 In the case of polymers, the key step using the MGA is the rational choice of a chemical group as a "gene", and different gene combinations will endow the polymers with various properties. 39,40 With reasonable theoretical and computational prediction, highthroughput screening can be performed before experiments to predict polymers with optimized chemical structures, thereby accelerating the entire process of developing new materials. [41][42][43] To date, a MGA has not been reported yet for molecular design of highly re-safe polymers.…”