Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals.

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme.

Switchable Chemical-Bond Reorganization for the Stable Charge Trapping in Amorphous Silicon Nitride.

Despite the widespread use of charge-trap flash (CTF) memory, the atomistic mechanism behind the exceptionally stable charge storage at the localized trap sites is still controversial. Herein, by combining first-principles calculations and orbital interaction analysis, a charge-dependent switchable chemical-bond reorganization is elucidated as the underpinning chemistry in the working mechanism of CTF. Especially, positively charged fourfold-coordinated nitrogen (dubbed N center), unappreciated until now, is the decisive component of the entire process; once an electron occupies this site, the N center disappears by breaking one N─Si bond, simultaneously forming a new Si─Si bond with a nearby Si atom which, in turn, creates fivefold coordinated Si.

Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential.

As transistor integration accelerates and miniaturization progresses, improving the interfacial adhesion characteristics of complex metal interconnect has become a major issue in ensuring semiconductor device reliability. Therefore, it is becoming increasingly important to interpret the adhesive properties of metal interconnects at the atomic level, predict their adhesive strength and failure mode, and develop computational methods that can be universally applied regardless of interface properties. In this study, we propose a method for theoretically understanding adhesion characteristics through steering molecular dynamics simulations based on machine learning interatomic potentials.

Novel Mechanism-Based Descriptors for Extreme Ultraviolet-Induced Photoacid Generation: Key Factors Affecting Extreme Ultraviolet Sensitivity.

Predicting photolithography performance in silico for a given materials combination is essential for developing better patterning processes. However, it is still an extremely daunting task because of the entangled chemistry with multiple reactions among many material components. Herein, we investigated the EUV-induced photochemical reaction mechanism of a model photoacid generator (PAG), triphenylsulfonium cation, using atomiC-Scale materials modeling to elucidate that the acid generation yield strongly depends on two main factors: the lowest unoccupied molecular orbital (LUMO) of PAG cation associated with the electron-trap efficiency 'before C-S bond dissociation' and the overall oxidation energy change of rearranged PAG associated with the proton-generation efficiency 'after C-S bond dissociation'.

On the atomistic origin of the polymorphism and the dielectric physical properties of beryllium oxide.

Polymorphic beryllium oxide has been theoretically investigated from first principles as regards orbital occupancies, chemical bonding, polarization, as well as dielectric properties. By means of Crystal-Orbital Bond Index (COBI) analysis, the important role of the 2p orbitals on beryllium has been elucidated, in particular in terms of the correlation between polarization and beryllium-atom displacement, including the impact of the latter on the covalency of the BeO bond. In addition, several structural possibilities for a Be Mg O solid solution have been investigated for a Be content between 6% and 22%; for those, dynamically stable structures have been found, displaying large polarization values, more covalent BeO bonds, and a tendency for tetrahedral Be coordination.

Controlling Gas Generation of Li-Ion Battery through Divinyl Sulfone Electrolyte Additive.

The focus of mainstream lithium-ion battery (LIB) research is on increasing the battery's capacity and performance; however, more effort should be invested in LIB safety for widespread use. One aspect of major concern for LIB cells is the gas generation phenomenon. Following conventional battery engineering practices with electrolyte additives, we examined the potential usage of electrolyte additives to address this specific issue and found a feasible candidate in divinyl sulfone (DVSF).

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 (T) and elastic moduli, varying the degree of cross-linking.

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.

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 ( ), melting temperature ( ), density (ρ), and elastic modulus () with substantial dependence on the dataset, which is the best for ( ∼ 0.9) and worst for ( ∼ 0.

Dipole Moment- and Molecular Orbital-Engineered Phosphine Oxide-Free Host Materials for Efficient and Stable Blue Thermally Activated Delayed Fluorescence.

To utilize thermally activated delayed fluorescence (TADF) technology for future displays, it is necessary to develop host materials which harness the full potential of blue TADF emitters. However, no publication has reported such hosts yet. Although the most popular host for blue TADF, bis[2-(diphenylphosphino)phenyl]ether oxide (DPEPO) guarantees high-maximum external quantum efficiency (EQE ) TADF devices, they exhibit very short operational lifetimes.

Three States Involving Vibronic Resonance is a Key to Enhancing Reverse Intersystem Crossing Dynamics of an Organoboron-Based Ultrapure Blue Emitter.

The recently developed narrow-band blue-emitting organoboron chromophores based on the multiple-resonance (MR) effect have now become one of the most important components for constructing efficient organic light emitting diodes (OLEDs). While they basically emit through fluorescence, they are also known for showing substantial thermally activated delayed fluorescence (TADF) even with a relatively large singlet-triplet gap (Δ ). Indeed, understanding the reverse intersystem crossing (RISC) dynamics behind this peculiar TADF will allow judicious molecular designs toward achieving better performing OLEDs.