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.

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.

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.

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.

Multiple-Resonance Extension and Spin-Vibronic-Coupling-Based Narrowband Blue Organic Fluorescence Emitters with Over 30% Quantum Efficiency.

Achieving narrow-bandwidth emission and high external quantum efficiency (EQE) simultaneously is a challenge for next-generation blue-emitting organic light-emitting diodes (OLEDs). In this study, novel multiple-resonance thermally activated delayed fluorescence (MR-TADF) emitters are developed by fusing an indolocarbazole unit with two carbazole skeletons using para-oriented nitrogen atoms. The resulting rigid and planar π-system without electron-accepting atoms exhibits pure blue photoluminescence at 470 nm, reaching a 100% quantum yield with a full-width-at-half-maximum (FWHM) of 25 nm. Higher-level quantum chemistry calculations confirm an MR effect within the extended π-conjugation and an enhanced triplet-to-singlet crossover (104 s-1 ) through a reduced energy gap (ΔEST ) coupled with large spin-vibronic coupling mediated by low-lying triplet excited states. An OLED fabricated using the MR-TADF emitter with CIE color coordinates of (0.12, 0.16) exhibits a record high EQE of 30.9% and a small FWHM of 23 nm. With further optimization of the device structure, a high EQE of 33.8% is achieved without additional outcoupling enhancements owing to the near-perfect horizontal alignment of the emitting dipoles.

A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values).

MOTIVATION: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. RESULTS: Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spin-Peierls Distortion of TiPO4 Causing a Transition from a Magnetic to a Nonmagnetic Insulating State and Its Effect on the Thermoelectric Properties: Density Functional Theory Analysis.

Density functional theory calculations were carried out to probe the nature of the electronic structure change in TiPO4 before and after its spin-Peierls distortion at 74.5 K, which is characterized by the dimerization in the chains of Ti3+ (d1) ions present in TiPO4. These calculations suggest strongly that the electronic state of TiPO4 is magnetic insulating before the distortion, but becomes nonmagnetic insulating after the distortion. Consistent with this suggestion, the phonon dispersion relations calculated for TiPO4 show that the undistorted TiPO4 is stable, while the distorted TiPO4 is not, if each Ti3+ ion has a spin moment, and that the opposite is true if each Ti3+ ion has no spin moment. These observations suggest that the driving force for the spin-Peierls distortion is the formation of direct metal-metal bonds leading to the dimerized chains of Ti3+ ions. The abrupt change in the electronic structures from a magnetic insulating state to a nonmagnetic insulating state explains why the spin-Peierls distortion of TiPO4 exhibits a first-order character. Although the two electronic states of TiPO4 before and after the distortion have a band gap, the substantial spin-Peierls distortion is found to enhance the thermoelectric properties of TiPO4.

Exploring the Role of Surface States in Emissive Carbon Nanodots: Analysis at Single-Particle Level.

Fluorescent carbon nanodots (CDs) have been highlighted as promising semiconducting materials due to their outstanding chemical and optical properties. However, the intrinsic heterogeneity of CDs has impeded a clear understanding of the mechanisms behind their photophysical properties. In this study, as-prepared CDs are fractionated via chromatography to reduce their structural and chemical heterogeneity and analyzed through ensemble and single-particle spectroscopies. Many single particles reveal fluorescence intensity fluctuations between two or more discrete levels with bi-exponential decays. While the intrinsic τ1 components are uniform among single particles, the τ2 components from molecule-like emissions spans a wider range of lifetimes, reflecting the inhomogeneity of the surface states. Furthermore, it is concluded that the relative population and chemical states of surface functional groups in CDs have a significant impact on emissive states, brightness, blinking, stability, and lifetime distribution of photoluminescence.

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.

Purely Spin-Vibronic Coupling Assisted Triplet to Singlet Up-Conversion for Real Deep Blue Organic Light-Emitting Diodes with Over 20% Efficiency and y Color Coordinate of 0.05.

Finding narrow-band, ultrapure blue thermally activated delayed fluorescence (TADF) materials is extremely important for developing highly efficient organic light-emitting diodes (OLEDs). Here, spin-vibronic coupling (SVC)-assisted ultrapure blue emitters obtained by joining two carbazole-derived moieties at a para position of a phenyl unit and performing substitutions using several blocking groups are presented. Despite a relatively large singlet-triplet gap (∆EST ) of >0.2 eV, efficient triplet-to-singlet crossover can be realized, with assistance from resonant SVC. To enhance the spin crossover, electronic energy levels are fine-tuned, thereby causing ∆EST to be in resonance with a triplet-triplet gap (∆ETT ). A sizable population transfer between spin multiplicities (>103 s-1 ) is achieved, and this result agrees well with theoretical predictions. An OLED fabricated using a multiple-resonance-type SVC-TADF emitter with CIE color coordinates of (0.15, 0.05) exhibits ultrapure blue emissions, with a narrow full-width-at-half-maximum of 21 nm and a high external quantum efficiency of 23.1%.

Improved Efficiency and Lifetime of Deep-Blue Hyperfluorescent Organic Light-Emitting Diode using Pt(II) Complex as Phosphorescent Sensitizer.

Although the organic light-emitting diode (OLED) has been successfully commercialized, the development of deep-blue OLEDs with high efficiency and long lifetime remains a challenge. Here, a novel hyperfluorescent OLED that incorporates the Pt(II) complex (PtON7-dtb) as a phosphorescent sensitizer and a hydrocarbon-based and multiple resonance-based fluorophore as an emitter (TBPDP and ν-DABNA) in the device emissive layer (EML), is proposed. Such an EML system can promote efficient energy transfer from the triplet excited states of the sensitizer to the singlet excited states of the fluorophore, thus significantly improving the efficiency and lifetime of the device. As a result, a deep-blue hyperfluorescent OLED using a multiple resonance-based fluorophore (ν-DABNA) with Commission Internationale de L'Eclairage chromaticity coordinate y below 0.1 is demonstrated, which attains a narrow full width at half maximum of ≈17 nm, fourfold increased maximum current efficiency of 48.9 cd A-1 , and 19-fold improved half-lifetime of 253.8 h at 1000 cd m-2 compared to a conventional phosphorescent OLED. The findings can lead to better understanding of the hyperfluorescent OLEDs with high performance.

Magnetic Structure and Origin of Insulating Behavior in the Ba2CuOsO6 System, and the Role of A-Site Ionic Size in Its Bandgap Opening: Density Functional Theory Approaches.

The magnetic structure and the origin of band gap opening for Ba2CuOsO6 were investigated by exploring the spin exchange interactions and employing the spin-orbit coupling effect. It revealed that the double-perovskite Ba2CuOsO6, composed of the 3d (Cu2+) and 5d (Os6+) transition metal magnetic ions is magnetic insulator. The magnetic susceptibilities of Ba2CuOsO6 obey the Curie-Weiss law, with an estimated Weiss temperature of -13.3 K, indicating AFM ordering. From the density functional theory approach, it is demonstrated that the spin exchange interaction between Cu ions plays a major role in exhibiting an antiferromagnetic behavior in the Ba2CuOsO6 system. An important factor to understand regarding the insulating behavior on Ba2CuOsO6 is the structural distortion shape of OsO6 octahedron, which should be closely connected with the ionic size of the A-site ion. Since the d-block of Os6+ (d2) ions of Ba2CuOsO6 is split into four states (xy < xz, yz < x2-y2 < z2), the crucial key is separation of doubly degenerated xz and yz levels to describe the magnetic insulating states of Ba2CuOsO6. By orbital symmetry breaking, caused by the spin-orbit coupling, the t2g level of Os6+ (d2) ions is separated into three sublevels. Two electrons of Os6+ (d2) ions occupy two levels of the three spin-orbit-coupled levels. Since Ba2CuOsO6 is a strongly correlated system, and the Os atom belongs to the heavy element group, one speculates that it is necessary to take into account both electron correlation and the spin-orbit coupling effect in describing the magnetic insulating states of Ba2CuOsO6.

Holistic Approach to the Mechanism Study of Thermal Degradation of Organic Light-Emitting Diode Materials.

The design of stable organic light-emitting diode materials is the key to long lifetime displays under various stressful conditions. Elucidating the degradation mechanism of the materials at the molecular level provides useful information for securing high stability. Previous works based on experiments or computations disclosed only a part of the whole degradation process. Here, we propose a holistic approach to the systematic analysis of the degradation mechanism by combining experimental mass analysis and computation in a semi-automated fashion. The mass analysis identifies molecular weights of feasible products from degradation reactions. Then, the computational analysis goes through initiation, propagation, and termination phases. The initiation phase determines radical fragments and reactive sites, triggering the propagation process. In the propagation phase, we subsequently perform intermediate sampling, reaction network construction, and kinetic analysis. As a proof of concept, this approach was applied to the thermal degradation problem during the sublimation purification process. Two major pathways were successfully elucidated with full atomistic details.

Accurate Vapor Pressure Prediction for Large Organic Molecules: Application to Materials Utilized in Organic Light-Emitting Diodes.

We present a theoretical method based on molecular dynamics (MD) simulations for the prediction of saturated vapor pressure of liquids constituting of large organic molecules at various temperatures. The approach is based on free energy calculations at a fixed temperature and a subsequent Gibbs-Duhem integration over the interval of temperatures. Results of the theoretical approach are compared with experimental measurements for a set of six molecules utilized in organic light-emitting diodes (OLEDs). Good correspondence of theoretical and experimental results suggests the developed methodology as a useful tool in various areas of molecular design, which require prediction of vapor pressures for liquids of large and chemically diverse compounds.

Ancillary ligand increases the efficiency of heteroleptic Ir-based triplet emitters in OLED devices.

The excellent contrast ratio, visibility, and advantages in producing thin and light displays let organic light emitting diodes change the paradigm of the display industry. To improve future display technologies, higher electroluminescence efficiency is needed. Herein, the detailed study of the non-radiative decay mechanism employing density functional theory calculations is carried out and a simple, general strategy for the design of the ancillary ligand is formulated. It is shown that steric bulk properly directed towards the phenylisoquinoline ligands can significantly reduce the non-radiative decay rate.

Ternary selenides A2Sb4Se8 (A = K, Rb and Cs) as an n-type thermoelectric material with high power factor and low lattice thermal conductivity: importance of the conformationally flexible Sb-Se-Se-Sb bridges.

We investigated the thermoelectric properties of the layered ternary selenides A2Sb4Se8 (A = K, Rb and Cs) and the lattice thermal conductivity of K2Sb4Se8 on the basis of DFT calculations, to find that these selenides are a high-performance n-type thermoelectric material. The Seebeck coefficients and power factors calculated for the electron carriers of A2Sb4Se8 (A = K, Rb and Cs) are greater than those of the well-known thermoelectric materials Bi2Te3 and PbTe. The lattice thermal conductivity κ latt of K2Sb4Se8 is comparable to that of PbTe, well-known for its low lattice thermal conductivity. In terms of both electronic and phonon structures, the structural parts of the A2Sb4Se8 (A = K, Rb and Cs) phases crucial for their thermoelectric properties are the conformationally-flexible Sb-Se-Se-Sb bridges that interlink between their structurally rigid units.

Spin-Vibronic Model for Quantitative Prediction of Reverse Intersystem Crossing Rate in Thermally Activated Delayed Fluorescence Systems.

Computationally predicting reverse intersystem crossing (RISC) rates is important for designing new thermally activated delayed fluorescence (TADF) materials. We report a method that can quantitatively predict RISC rates by explicitly considering the spin-vibronic coupling mechanism. The coupling element of the spin-vibronic Hamiltonian is obtained by expanding the spin-orbit and the non-Born-Oppenheimer terms to second order and is then brought into the Golden Rule rate under the Condon approximation. The rate equation is solved directly in the time domain using a correlation function approach. The contributions of the first-order direct spin-orbit coupling and the second-order spin-vibronic coupling to an RISC rate can be quantitatively analyzed in a separate manner. We demonstrate the utility of the method by applying it to a representative TADF system, where we observe that the spin-vibronic portion is substantial but not dominant especially with a relatively small triplet-singlet energy gap. Likewise, our method may elucidate the physical background of efficient nonradiative transitions from the lowest triplet to a higher lying singlet in other purely organic TADF systems, and it will be of great utility toward designing new such molecules.

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation.

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions.

Enhancing the Kinetic Stability and Lifetime of Organic Light-Emitting Diodes based on Bipolar Hosts by using Spiroconjugation.

We examined how to enhance the lifetime of organic light-emitting diodes (OLEDs) based on bipolar host molecules ET-HT, where ET and HT refer to electron- and hole-transporting units, respectively, by analyzing their thermodynamic and kinetic stabilities. Our DFT calculations reveal that the thermodynamic stability of ET-HT is determined by that of its anion, which is difficult to improve by chemical modifications of ET and HT. The kinetic stability of ET-HT can be enhanced by the spiroconjugation between ET and HT, which occurs when their π-frameworks are extended and have an orthogonal arrangement. Green OLED devices were fabricated by using ET-HTs with and without spiroconjugation, to find that the device with spiroconjugation has a lifetime that is approximately 6 times longer than the one without spiroconjugation.