Showing papers by "Seoul National University published in 2022"

ColabFold: making protein folding accessible to all

Abstract: ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .

Unbiasing fermionic quantum Monte Carlo with a quantum computer

Abstract: Interacting many-electron problems pose some of the greatest computational challenges in science, with essential applications across many fields. The solutions to these problems will offer accurate predictions of chemical reactivity and kinetics, and other properties of quantum systems1-4. Fermionic quantum Monte Carlo (QMC) methods5,6, which use a statistical sampling of the ground state, are among the most powerful approaches to these problems. Controlling the fermionic sign problem with constraints ensures the efficiency of QMC at the expense of potentially significant biases owing to the limited flexibility of classical computation. Here we propose an approach that combines constrained QMC with quantum computation to reduce such biases. We implement our scheme experimentally using up to 16 qubits to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed with the help of quantum computers, while achieving accuracy that is competitive with state-of-the-art classical methods without burdensome error mitigation. Compared with the popular variational quantum eigensolver7,8, our hybrid quantum-classical computational model offers an alternative path towards achieving a practical quantum advantage for the electronic structure problem without demanding exceedingly accurate preparation and measurement of the ground-state wavefunction.

Antiferromagnetic Kitaev interaction in Jeff=1/2 cobalt honeycomb materials Na3Co2SbO6 and Na2Co2TeO6

Abstract: Finding new materials with antiferromagnetic (AFM) Kitaev interaction is an urgent issue for quantum magnetism research. We conclude that Na3Co2SbO6 and Na2Co2TeO6 are new honeycomb cobalt-based systems with AFM Kitaev interaction by carrying out inelastic neutron scattering experiments and subsequent analysis. The spin-orbit excitons observed at 20-28 meV in both compounds strongly support the idea that Co2+ ions of both compounds have a spin-orbital entangled Jeff=1/2 state. Furthermore, we found that a generalized Kitaev-Heisenberg Hamiltonian can describe the spin-wave excitations of both compounds with additional 3rd nearest-neighbor interaction. Our best-fit parameters show significant AFM Kitaev terms and off-diagonal symmetric anisotropy terms of a similar magnitude in both compounds. We also found a strong magnon-damping effect at the higher energy part of the spin waves, entirely consistent with observations in other Kitaev magnets. Our work suggests Na3Co2SbO6 and Na2Co2TeO6 as rare examples of the AFM Kitaev magnets based on the systematic studies of the spin waves and analysis.

Non‐Electrode Components for Rechargeable Aqueous Zinc Batteries: Electrolytes, Solid‐Electrolyte‐Interphase, Current Collectors, Binders, and Separators

Abstract: Rechargeable aqueous zinc batteries (AZBs) are one of the promising options for large-scale electrical energy storage owing to their safety, affordability and environmental friendliness. During the past decade, there have been remarkable advancements in the AZBs technology, which are achieved through intensive efforts not only in the area of electrode materials but also in the fundamental understandings of non-electrode components such as electrolytes, solid electrolyte interphase (SEI), current collectors, binders, and separators. In particular, the breakthroughs in the non-electrode components should not be underestimated in having enabled the AZBs to attain a higher energy and power density beyond that of the conventional AZBs, proving their critical role. In this article, the recent research progress is comprehensively reviewed with respect to non-electrode components in AZBs, covering the new-type of electrolytes that have been introduced, attempts for the tailoring of SEI, and the design efforts for multi-functional current collectors, binders and separators, along with the remaining challenges associated with these non-electrode components. Finally, perspectives are discussed toward future research directions in this field. This extensive overview on the non-electrode components is expected to guide and spur further development of high-performance AZBs.

Highly efficient blue InGaN nanoscale light-emitting diodes

Abstract: Indium gallium nitride (InGaN)-based micro-LEDs (μLEDs) are suitable for meeting ever-increasing demands for high-performance displays owing to their high efficiency, brightness and stability1-5. However, μLEDs have a large problem in that the external quantum efficiency (EQE) decreases with the size reduction6-9. Here we demonstrate a blue InGaN/GaN multiple quantum well (MQW) nanorod-LED (nLED) with high EQE. To overcome the size-dependent EQE reduction problem8,9, we studied the interaction between the GaN surface and the sidewall passivation layer through various analyses. Minimizing the point defects created during the passivation process is crucial to manufacturing high-performance nLEDs. Notably, the sol-gel method is advantageous for the passivation because SiO2 nanoparticles are adsorbed on the GaN surface, thereby minimizing its atomic interactions. The fabricated nLEDs showed an EQE of 20.2 ± 0.6%, the highest EQE value ever reported for the LED in the nanoscale. This work opens the way for manufacturing self-emissive nLED displays that can become an enabling technology for next-generation displays.

Application of the MTT-based colorimetric method for evaluating bacterial growth using different solvent systems

Abstract: Although the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay is widely used to measure the growth of animal cells, several interfering factors and obstacles for analyzing bacterial growth and for solubilizing bacterial-formazan have been reported. In this study, growth of Gram-negative Escherichia coli and Gram-positive Leuconostoc mesenteroides were analyzed using the MTT assay, and different solvents, specifically dimethyl sulfoxide, 1% Triton X-100, 1% Bile bovine, 10% sodium dodecyl sulfate, and sodium hydroxide (NaOH) were evaluated for solubilizing bacterial-formazan. MTT formazan that had been reduced by L. mesenteroides had fully developed color within 15 min in all the solvents tested. The solvent with the highest peak absorbance was 1 N NaOH. MTT formazan did not have a fully developed color response in the 0.001–0.1 N NaOH range, and its color intensity was not further enhanced at concentrations higher than 1 N. For all solvents, the color intensity of dissolved formazan was significantly higher in E. coli than in L. mesenteroides. The MTT assay showed sufficient sensitivity to detect bacterial cells during early growth phase that could not be detected by the plate counting method. These results suggest that NaOH (1 N) is a suitable solvent for analyzing bacterial growth in MTT assays.

Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery

Abstract: Despite the recent success of deep-learning-based fault diagnosis of rotating machinery, to enable accurate and robust diagnosis models, existing approaches proceed with the assumption that training and test data follow the same distribution. However, in practical industrial settings, variations in operating conditions and environmental noise can cause changes in the characteristics of the training and test data, called domain shift, resulting in performance degradation of the test data. To deal with these issues, this paper proposes an asymmetric inter-intra domain alignments (AIIDA) approach for fault diagnosis under various levels of domain shift. First, inter-domain alignment is conducted by minimizing the maximum mean discrepancy loss and domain adversarial loss. Next, intra-domain alignment is performed by adjusting the inconsistency loss. This approach allows the proposed AIIDA method to learn features that have lower inter-domain distance and higher intra-domain distance; thus, the fault diagnosis performance in the target domain can be significantly improved. Extensive experimental assessment that examines various scenarios across three bearing datasets is performed to validate the effectiveness of the proposed approach. Furthermore, a study comparing the proposed method with other existing methods demonstrates that the proposed method outperforms other methods.