Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based Neural Network Potential: Effect of Composition and Temperature on Crystallization Behavior

Structure and diffusion dynamics of silicon carbide (Si1–xCx) are investigated via molecular dynamics computer simulations with ab initio-based neural network potentials, exploring the effect of composition and temperature on crystallization behaviors. A neural network potential is developed to describe high-dimensional potential energy surfaces of silicon carbide (SiC) systems, reproducing first-principles results on their potential energies and forces. The phase behavior of amorphous Si1–xCx below its experimental melting point is systematically demonstrated by analyzing the structural and dynamic properties as a function of temperature and carbon concentration x in the composition range 0 ≤ x ≤ 0.5 and the temperature range T = 2000–2600 K, compared to available experiments. The phase of Si1–xCx is characterized by analyzing the pair correlation function, coordination number, tetrahedral order parameter, SiC tetrahedron fraction, Si disordered fraction, and excess entropy. Our results indicate that the system undergoes the crystallization by organizing the short- and medium-range order as the carbon content increases, where the critical carbon fraction for crystallization increases with temperature. The addition of carbon to silicon results in the phase separation into liquid Si and crystal SiC as well as the partial crystallization of Si1–xCx. The self-diffusivity of Si1–xCx is also evaluated to understand how the structural change caused by the crystallization works on diffusion dynamics. The diffusion dynamics of Si1–xCx becomes slower with increasing carbon content and decreasing temperature, which significantly slows down with onset of the crystallization

Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based Neural Network Potential: Effect of Composition and Temperature on Crystallization Behavior

Structure and diffusion dynamics of silicon carbide (Si1–xCx) are investigated via molecular dynamics computer simulations with ab initio-based neural network potentials, exploring the effect of composition and temperature on crystallization behaviors. A neural network potential is developed to describe high-dimensional potential energy surfaces of silicon carbide (SiC) systems, reproducing first-principles results on their potential energies and forces. The phase behavior of amorphous Si1–xCx below its experimental melting point is systematically demonstrated by analyzing the structural and dynamic properties as a function of temperature and carbon concentration x in the composition range 0 ≤ x ≤ 0.5 and the temperature range T = 2000–2600 K, compared to available experiments. The phase of Si1–xCx is characterized by analyzing the pair correlation function, coordination number, tetrahedral order parameter, SiC tetrahedron fraction, Si disordered fraction, and excess entropy. Our results indicate that the system undergoes the crystallization by organizing the short- and medium-range order as the carbon content increases, where the critical carbon fraction for crystallization increases with temperature. The addition of carbon to silicon results in the phase separation into liquid Si and crystal SiC as well as the partial crystallization of Si1–xCx. The self-diffusivity of Si1–xCx is also evaluated to understand how the structural change caused by the crystallization works on diffusion dynamics. The diffusion dynamics of Si1–xCx becomes slower with increasing carbon content and decreasing temperature, which significantly slows down with onset of the crystallization

Computer Simulation Study of Graphene Oxide Supercapacitors: Charge Screening Mechanism

Graphene oxide supercapacitors in the parallel plate configuration are studied via molecular dynamics (MD) simulations. The full range of electrode oxidation from 0 to 100% is examined by oxidizing the graphene surface with hydroxyl groups. Two different electrolytes, 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI⁺BF₄^–) as an ionic liquid and its 1.3 M solution in acetonitrile as an organic electrolyte, are considered. While the area-specific capacitance tends to decrease with increasing electrode oxidation for both electrolytes, its details show interesting differences between the organic electrolyte and ionic liquid, including the extent of decrease. For detailed insight into these differences, the screening mechanisms of electrode charges by electrolytes and their variations with electrode oxidation are analyzed with special attention paid to the aspects shared by and the contrasts between the organic electrolyte and ionic liquid

Graphene Oxide Supercapacitors: A Computer Simulation Study

Supercapacitors with graphene oxide (GO) electrodes in a parallel plate configuration are studied with molecular dynamics (MD) simulations. The full range of electrode oxidation from 0% (pure graphene) to 100% (fully oxidized GO) is investigated by decorating the graphene surface with hydroxyl groups. The ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI⁺BF₄⁻) is examined as an electrolyte. Capacitance tends to decrease with increasing electrode oxidation, in agreement with several recent measurements. This trend is attributed to the decreasing reorganization ability of ions near the electrode and a widening gap in the double layer structures as the density of hydroxyl groups on the electrode surface increases

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 (ρ), 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

Modulation of the Dirac Point Voltage of Graphene by Ion-Gel Dielectrics and Its Application to Soft Electronic Devices

We investigated systematic modulation of the Dirac point voltage of graphene transistors by changing the type of ionic liquid used as a main gate dielectric component. Ion gels were formed from ionic liquids and a non-triblock-copolymer-based binder involving UV irradiation. With a fixed cation (anion), the Dirac point voltage shifted to a higher voltage as the size of anion (cation) increased. Mechanisms for modulation of the Dirac point voltage of graphene transistors by designing ionic liquids were fully understood using molecular dynamics simulations, which excellently matched our experimental results. It was found that the ion sizes and molecular structures play an essential role in the modulation of the Dirac point voltage of the graphene. Through control of the position of their Dirac point voltages on the basis of our findings, complementary metal–oxide–semiconductor (CMOS)-like graphene-based inverters using two different ionic liquids worked perfectly even at a very low source voltage (<i>V</i>_DD = 1 mV), which was not possible for previous works. These results can be broadly applied in the development of low-power-consumption, flexible/stretchable, CMOS-like graphene-based electronic devices in the future