A Computer Simulation Study of Thermal and Mechanical Properties of Poly(Ionic Liquid)s

Thermal and mechanical properties of poly(ionic liquid)s (PILs), an epoxidized ionic liquid-amine network, are studied via molecular dynamics simulations. The poly(ionic liquid)s are designed with two different ionic liquid monomers, 3-[2-(Oxiran-2-yl)ethyl]-1-{4-[(2-oxiran-2-yl)ethoxy]phenyl}imidazolium (EIM2) and 1-{4-[2-(Oxiran-2-yl)ethyl]phenyl}-3-{4-[2-(oxiran-2-yl)ethoxy]benzyl}imidazolium (EIM1), each of which is networked with tris(2-aminoethyl)amine, paired with different anions, bis(trifluoromethanesulfonyl)imide (TFSI−) and chloride (Cl−). We investigate how ionic liquid monomers with high ionic strength affect structures of the cross-linked polymer networks and their thermomechanical properties such as glass transition temperature (Tg) and elastic moduli, varying the degree of cross-linking. Strong electrostatic interactions between the cationic polymer backbone and anions build up their strong structures of which the strength depends on their molecular structures and anion size. As the anion size decreases from TFSI− to Cl−, both Tg and elastic moduli of the PIL increase due to stronger electrostatic interactions present between their ionic moieties, making it favorable for the PIL to organize with stronger bindings. Compared to the EIM2 monomer, the EIM1 monomers and TFSI− ions generate a PIL with higher Tg and elastic moduli. This attributes to the less flexible structure of the EIM1 monomer for the chain rotation, in which steric hindrance by ring moieties in the EIM1-based PIL enhances their structural rigidity. The π-π stacking structures between the rings are found to increase in EIM1-based PIL compared to the EIM2-based one, which becomes stronger with smaller Cl− ion rather than TFSI−. The effect of the degree of the cross-linking on thermal and mechanical properties is also examined. As the degree of cross-linking decreases from 100% to 60%, Tg also decreases by a factor of 10–20%, where the difference among the given PILs becomes decreased with a lower degree of cross-linking. Both the Young’s (E) and shear (G) moduli of all the PILs decrease with degree of cross-linking, which the reduction is more significant for the PIL generated with EIM2 monomers. Transport properties of anions in PILs are also studied. Anions are almost immobilized globally with very small structural fluctuations, in which Cl− presents lower diffusivity by a factor of ~2 compared to TFSI− due to their stronger binding to the cationic polymer backbone

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (Tg), melting temperature (Tm), density (??), and elastic modulus (E) with substantial dependence on the dataset, which is the best for Tg (R2 ??? 0.9) and worst for E (R2 ??? 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with Tg, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property

Simulator acceleration and inverse design of fin field-effect transistors using machine learning

The simulation and design of electronic devices such as transistors is vital for the semiconductor industry. Conventionally, a device is intuitively designed and simulated using model equations, which is a time-consuming and expensive process. However, recent machine learning approaches provide an unprecedented opportunity to improve these tasks by training the underlying relationships between the device design and the specifications derived from the extensively accumulated simulation data. This study implements various machine learning approaches for the simulation acceleration and inverse-design problems of fin field-effect transistors. In comparison to traditional simulators, the proposed neural network model demonstrated almost equivalent results (R-2 = 0.99) and was more than 122,000 times faster in simulation. Moreover, the proposed inverse-design model successfully generated design parameters that satisfied the desired target specifications with high accuracies (R-2 = 0.96). Overall, the results demonstrated that the proposed machine learning models aided in achieving efficient solutions for the simulation and design problems pertaining to electronic devices. Thus, the proposed approach can be further extended to more complex devices and other vital processes in the semiconductor industry

Bridging TCAD and AI: Its Application to Semiconductor Design

There is a growing consensus that the physics-based model needs to be coupled with machine learning (ML) model relying on data or vice versa in order to fully exploit their combined strengths to address scientific or engineering problems that cannot be solved separately. We propose several methodologies of bridging technology computer-aided design (TCAD) simulation and artificial intelligence (AI) with its application to the tasks for which traditional TCAD faces challenges in terms of simulation runtime, coverage, and so on. AI-emulator that learns fine-grained information from rigorous TCAD enables simulation of process technologies and device in real-time as well as large-scale simulation such as full-pattern analysis of stress without high demand on computational resource. To accelerate atomistic molecular dynamics (MD) simulation, we have done a comparison study of descriptor-based and graph-based neural net potential, and also show their capability with large-scale and long-time simulation of silicon oxidation. Finally, we discuss the use of hybrid modeling of AI- and physics-based model for the case where physical equations are either fully or partially unknown