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 Bex Mg1-x 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. The dynamically unstable structures, however, resemble rock-salt BeO in their local structural properties around the Be atom. High dielectric constants and band gaps indicating insulating behavior have been found for those.

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'. Furthermore, by considering stepwise reactions accordingly, we developed a two-parameter-based prediction model predicting the exposure dose of the resist, which outperformed the traditional LUMO-based prediction model. Our model suggests that one should not focus only on the LUMO energies but also on the energy change during the rearrangement process of the activated triphenylsulfonium (TPS) species. We also believe that the model is well suited for computational materials screening and/or inverse design of novel PAG materials with high lithographic performances.

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). We manufactured four identical battery cells and employed an electrolyte mixture with four different DVSF concentrations (0%, 0.5%, 1.0%, and 2.0%). By measuring the generated gas volume from each battery cell, we demonstrated the potential of DVSF additives as an effective approach for reducing the gas generation in LIB cells. We found that a DVSF concentration of only 1% was necessary to reduce the gas generation by approximately 50% while simultaneously experiencing a negligible impact on the cycle life. To better understand this effect on a molecular level, we examined possible electrochemical reactions through ab initio molecular dynamics (AIMD) based on the density functional theory (DFT). From the electrolyte mixture's exposure to either an electrochemically reductive or an oxidative environment, we determined the reaction pathways for the generation of CO2 gas and the mechanism by which DVSF additives effectively blocked the gas's generation. The key reaction was merging DVSF with cyclic carbonates, such as FEC. Therefore, we concluded that DVSF additives could offer a relatively simplistic and effective approach for controlling the gas generation in lithium-ion batteries.

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. As a result, the electron is stored in a multi-center orbital belonging to multiple atoms including the newly formed Si─Si bond. It is also observed that hole trapping accompanies the creation of an N+ center by forming a new N─Si bond, which represents the reverse process. To further support and validate this model by means of core-level calculations, it is also shown that an N+ center's 1s core level is 1.0-2.5 eV deeper in energy than those of the threefold coordinated N atoms, in harmony with experimental X-ray photoelectron spectroscopy data.

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. As an illustrative example, we calculate the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the ωB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems.

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. We utilized this method to investigate the adhesion characteristics of tungsten deposited on titanium nitride barrier metal (W/TiN) as a representative metal interconnect structure in devices. Pulling tests that pull two materials apart and sliding tests that pull them against each other in a shear direction were implemented to investigate the failure mode and adhesive strength depending on TiN facet orientation. We found that the W/TiN interface showed an adhesive failure where they separate from each other when tested with pulling force on Ti-rich (111) or (001) facets while cohesive failures occurred where W itself was destroyed on N-rich (111) facet. The adhesion strength was defined as the maximum force causing failure during the pulling test for consistent interpretation and the strengths of tungsten were predicted to be strongest when deposited onto N-rich (111) facet while weakest on Ti-rich (111) facet.

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.

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 (R2 = 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 (R2 = 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.

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 (T g), melting temperature (T m), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T g (R 2 ∼ 0.9) and worst for E (R 2 ∼ 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 T g, 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.

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 (EQEmax ) TADF devices, they exhibit very short operational lifetimes. In contrast, long-lifespan blue TADF devices employing stable hosts such as 3',5-di(9H-carbazol-9-yl)-[1,1'-biphenyl]-3-carbonitrile (mCBP-CN) exhibit much lower EQEmax than the DPEPO-employed devices. Here, an elaborative approach for designing host molecules is suggested to achieve simultaneously stable and efficient blue TADF devices. The approach is based on engineering the molecular geometry, ground- and excited-state dipole moments of host molecules. The engineered hosts significantly enhance delayed fluorescence quantum yields of TADF emitters, as stabilizing the charge-transfer excited states of the TADF emitters and suppressing exciton quenching, and improve the charge balance. Moreover, they exhibit both photochemical and electrochemical stabilities. The best device employing one of the engineered hosts exhibits 79% increase in EQEmax compared to the mCBP-CN-employed device, together with 140% and 92-fold increases in operational lifetime compared to the respective mCBP-CN- and the DPEPO-based devices.

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 (ΔE ST). Indeed, understanding the reverse intersystem crossing (RISC) dynamics behind this peculiar TADF will allow judicious molecular designs toward achieving better performing OLEDs. Explaining the underlying nonadiabatic spin-flip mechanism, however, has often been equivocal, and how the sufficiently fast RISC takes place even with the sizable ΔE ST and vanishingly small spin-orbit coupling is not well understood. Here, we show that a vibronic resonance, namely the frequency matching condition between the vibration and the electronic energy gap, orchestrates three electronic states together and this effect plays a major role in enhancing RISC in a typical organoboron emitter. Interestingly, the mediating upper electronic state is quite high in energy to an extent that its thermal population is vanishingly small. Through semiclassical quantum dynamics simulations, we further show that the geometry dependent non-Condon coupling to the upper triplet state that oscillates with the frequency ΔE ST/ℏ is the main driving force behind the peculiar resonance enhancement. The existence of an array of vibrational modes with strong vibronic rate enhancements provides the ability to sustain efficient RISC over a range of ΔE ST in defiance of the energy gap law, which can render the MR-emitters peculiar in comparison with more conventional donor-acceptor type emitters. Our investigation may provide a new guide for future blue emitting molecule developments.