A5083957299- Advanced Chemical Physics Studies
- Machine Learning in Materials Science
- Metal-Organic Frameworks: Synthesis and Applications
- Graphene research and applications
- Advanced NMR Techniques and Applications
- Advancements in Battery Materials
- Computational Drug Discovery Methods
- Spectroscopy and Quantum Chemical Studies
- Catalysis and Oxidation Reactions
- Surface and Thin Film Phenomena
- Insurance, Mortality, Demography, Risk Management
- Glass properties and applications
- Transition Metal Oxide Nanomaterials
- MXene and MAX Phase Materials
- High-Voltage Power Transmission Systems
- MRI in cancer diagnosis
- Zeolite Catalysis and Synthesis
- Integrated Circuits and Semiconductor Failure Analysis
- Innovative Microfluidic and Catalytic Techniques Innovation
- Material Dynamics and Properties
- Power System Optimization and Stability
- 2D Materials and Applications
- Insurance and Financial Risk Management
- Advanced MRI Techniques and Applications
- Magnetic confinement fusion research
Korea Advanced Institute of Science and Technology
2013-2021
Mokpo National University
2019
Government of the Republic of Korea
2017
California Institute of Technology
2013
Siemens (Germany)
2012
A summary of the technical advances that are incorporated in fourth major release Q-Chem quantum chemistry program is provided, covering approximately last seven years. These include developments density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster perturbation theories, for electronically excited open-shell species, tools treating extended environments, algorithms walking on potential surfaces, analysis tools, energy...
This article summarizes technical advances contained in the fifth major release of Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library exchange-correlation functionals, along with a suite correlated many-body methods, continues to be hallmark software. The methods include novel variants both coupled-cluster and configuration-interaction approaches based on algebraic diagrammatic construction variational reduced density-matrix methods. Methods...
A family of transition metal dichalcogenide (TMD) nanosheets has recently shown its potential as negative electrodes in lithium ion batteries (LIBs). Herein, Na adsorption and migration properties well the possibility phase induced by on TiS2, VS2, CrS2, CoTe2, NiTe2, ZrS2, NbS2, MoS2 are predicted using first-principles calculations. In terms average voltage capacity, M = Ti, Zr, Nb, Mo found to be suitable anodes for sodium (SIBs) with voltages 0.49–0.95 V theoretical capacities 260–339 mA...
MXenes are predicted to be a family of promising Na anode materials with desirable electrochemical properties using density functional theory.
Crystal water mediated phase transition: the underlying thermodynamic and kinetic role of crystal is investigated using <italic>ab initio</italic> calculations.
We examined pretraining tasks leveraging abundant labeled data to effectively enhance molecular representation learning in downstream tasks, specifically emphasizing graph transformers improve the prediction of ADMET properties. Our investigation revealed limitations previous and identified more meaningful training targets, ranging from 2D descriptors extensive quantum chemistry simulations. These were seamlessly integrated into supervised tasks. The implementation our strategy multitask...
Analytic first derivative expression of opposite-spin (OS) ansatz adapted quartic scaling doubly hybrid XYGJ-OS functional is derived and implemented into Q-Chem. The resulting algorithm scales quartically with system size as in OS-MP2 gradient, by utilizing the combination Laplace transformation density fitting technique. performance geometry optimization assessed comparing bond lengths intermolecular properties reference coupled cluster methods. For selected nonbonded complexes S22 S66...
We propose a grid-based local representation of electronic quantities that can be used in machine learning applications for molecules, which is compact, fixed size, and able to distinguish different chemical environments. apply the proposed approach represent external potential density functional theory with modified pseudopotentials demonstrate its proof concept by predicting Perdew-Burke-Ernzerhof approximation exchange-correlation potentials kernel ridge regression. For 16 small molecules...
Abstract The significant amount of attention that has been directed toward metal–organic frameworks (MOFs) for a wide spectrum applications can be attributed to their variety and tunability, which are precisely the aspects computational modeling offer by systematically exploring chemical space. In this minireview, we describe density functional theory calculations gas adsorption on MOFs, mainly focusing interaction CO 2 with MOF‐74. generalized gradient approximation (GGA) level studies...
A machine learning approach based on the artificial neural network (ANN) is applied for configuration problem in solids. The proposed method provides a direct mapping from vectors to energies. benchmark conducted M1 phase of Mo-V-Te-Nb oxide showed that only fraction configurations needs be calculated, thus computational burden significantly decreased, by factor 20-50, with R2 = 0.96 and MAD 0.12 eV. It shown ANN can also handle effects geometry relaxation when properly trained, resulting 0.95 0.13
We revisit a dangling theoretical question of whether the surface reconstruction Si(100) would energetically favor symmetric or buckled dimers on intrinsic potential energy surfaces at 0 K. This seemingly simple is still unanswered definitively since all existing density functional based calculations predict to be buckled, while most wavefunction correlated treatments prefer configurations. Here, we use doubly hybrid (DHDF) geometry optimizations, in particular, XYGJ-OS, complete active...
The use of damping functions in empirical dispersion correction schemes is common and widespread. These contain scaling parameters, they are usually optimized for the best performance practical systems. In this study, it shown that overfitting problem can be present current functions, which sometimes yield erroneous results real applications beyond nature training sets. To end, we a function called linear soft (lsd) suffers less from overfitting. This damps asymptotic curve more softly than...
The conventional TCR have been controlled on the premise that inductances of reactors in TCRs a same value. However, are different; they will produce unbalanced three-phase current. In this paper, Current compensation method for is proposed, and based control firing angles TCR.