TS12. Computational Nano Science & Technology for Nanomaterials 
· Session Information
Computational Science and Technology for Nanomaterials

This session aims at introducing innovative research paradigm for new discovery and optimal design of functional nanomaterials. It encompasses largely two areas: development of new theory or computational methods and applications to wide range of devices to substantially improve operational efficiency. Machine learning and neural network theory and multiscale computational models will be discussed over a wide range of applications such as renewable energy devices, catalysts, electronic devices, and so on.

- DFT & beyond DFT methods, molecular dynamic simulation, thermodynamic and kinetic Monte Carlo simulations, phase field models.
- Computational optimization and smart design of nanomaterials.
- Materials genome approach for chemical and materials informatics.
- Computational framework or prototype for nanomaterial development
- Novel phenomena at interfaces and heterostructures: Theory and modeling
- Atomic-scale understanding of stoichiometric and polymorphic control in advanced functional materials
- Advances in theory and modeling of optoelectronic properties in low-dimensional nanomaterials

· Symposium Invited Speakers
Speech Date 2019/07/03 09:00~09:30   Speech PlaceRoom 205B 
Speaker Ju Li   CV
AffiliationMassachusetts Institute of Technology, USA 
TitleElastic Strain Engineering for Unprecedented Properties 
Speech Date 2019/07/04 09:00~09:30   Speech PlaceRoom 205B 
Speaker Vidvuds Ozolins   CV
AffiliationYale University, USA 
TitleCarrier lifetime effects on thermoelectric efficiency 
Recent developments in electronic structure algorithms based on the Wannier function interpolation of electronic wave functions have enabled accurate first-principles calculations of electron-phonon interactions and intrinsic carrier lifetimes in the relaxation time approximation. This has supplied the final missing piece of the puzzle for predicting the thermoelectric figure of merit zT=σS^2 T/κ, where the conductivity σ, the Seebeck coefficient S, and the total thermal conductivity κ now can all be obtained from the density-functional theory (DFT). This opens up exciting possibilities for theoretically understanding and reliably predicting new materials with high values of zT. We will review several examples from our recent work, including a Li-intercalated analogue of lead telluride (Li2TlBi), an intermetallic compound with unexpectedly high value of thermopower S (CoSi), and a theoretically predicted full Heusler compound with ultrahigh zT (Ba2BiAu). General factors for high thermoelectric power factors in these compounds include energy dependence of carrier lifetimes for high S, high degeneracy of carrier pockets at the Fermi level, weak electron-phonon scattering for high mobility, and concomitantly low Lorentz numbers for low electronic thermal conductivity.

Y. Xia, J. Park, F. Zhou, and V. Ozolins, "High-thermoelectric power factor in intermetallic CoSi arising from energy filtering of electrons by phonon scattering," to appear in Physical Review Applied (2019).
J. He, Y. Xia, S. S. Naghavi, V. Ozolins, and C. Wolverton, “Designing Chemical Analogs to PbTe with Intrinsic High Band Degeneracy and Low Lattice Thermal Conductivity,” to appear in Nature Communications (2019).
J. Park, Y. Xia, and V. Ozolins, "High thermoelectric power factor and efficiency from a highly dispersive band in Ba2BiAu," Physical Review Applied 11, 014058 (2019). URL: https://doi.org/10.1103/PhysRevApplied.11.014058. 
· Invited Speakers
Speech Date 2019/07/03 11:00~11:30   Speech PlaceRoom 205B 
Speaker Jiamian Hu   CV
AffiliationUniversity of Wisconsin-Madison, USA 
TitleComputational Design of Magnetoelectric Nanostructures for beyond CMOS computing 
Magnetoelectric nanostructures permits addressing nanoscale magnets with a voltage, and has been envisaged for ultralow-energy beyond CMOS computing. However, an outstanding challenge is to realize voltage-controlled 180 magnetization reversal, which seems to be thermodynamically forbidden. In this talk, I will present three different routes to achieving this seemingly improbable goal. These routes are all based on a rational design of the magnetoelectric nanostructure (size, shape, operation conditions) and demonstrated using phase-field modeling. I will also present our most recent design of voltage-controlled switching of magnetic skyrmions in magnetoelectric nanostructures, which may provide new perspectives for beyond CMOS computing. 
Speech Date 2019/07/03 15:45~16:15   Speech PlaceRoom 205B 
Speaker Woosun Jang   CV
AffiliationFHI, Germany 
TitleNature-inspired algorithms for surface structure predictions 
Predicting the atomic structure of materials and investigating the emergent physical and chemical property is the primary challenge in the field of nanoscience. Currently, most atomic structures used in the field of computational surface science are often represented by a low Miller index surface slab, lacking critical structural features like steps, facets, (co)adsorbates, and surface reconstructions. Another commonly used approach in designing the surfaces is based on the conventional knowledge, e.g. using only the surface with the lowest surface energy in vacuum, though these surfaces may be less intersting. Nature-inspired algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), which mimics and adopts the characteristic behavior found in nature to solve optimization problem, is in a limelight as an alternative method to survey vast configurational space without confronting the limits mentioned above. Especially, Firefly algorithm, one class of nature-inspired algorithm, has been suggested to outperform other algorithms in finding many interesting surface structures due to its automatic subgrouping around the local minima. In this talk, surface structure predictions based on the nature-inspired algorithms will be presented, as well as the efficiency and reliability of algorithms will be discussed. 
Speech Date 2019/07/03 16:45~17:15   Speech PlaceRoom 205B 
Speaker Donghun Kim   CV
AffiliationKorea Institute of Science and Technology, Korea 
TitleSlab Graph Convolutional Neural Network for Discovery of N2 Electroreduction Catalysts 
The development of catalysts for the electrochemical N2 reduction reaction (NRR) with a low onset potential and high Faradaic efficiency is highly desired but remains challenging. Machine learning (ML) has recently emerged as a complementary tool to accelerate material discovery; however, an ML model for the NRR has yet to be developed. In this talk, I will introduce a machine learning model, namely, slab graph convolutional neural network (SGCNN), an accurate and flexible ML model that is suited for probing surface reactions in catalysis. With a self-accumulated database of 2,699 surface calculations, SGCNN predicted the binding energies, ranging over 8 eV, of five key adsorbates (H, N2, N2H, NH, NH2) related to NRR performance with a mean absolute error of only 0.23 eV. Unlike previously available models, SGCNN avoids using ab initio level inputs and instead is solely based on elemental properties that are all readily available on the periodic table of elements; true accelerations can be realized. A few binary intermetallics or core-shell-type alloy will be discussed as strong candidates likely exhibiting both a low onset potential and high Faradaic efficiency in the NRR. 
Speech Date 2019/07/03 18:00~18:30   Speech PlaceRoom 205B 
Speaker Sangtae Kim   CV
AffiliationKorea Institute of Science and Technology, Korea 
TitleSearch for Nitrogen Dimer-Stabilized Oxynitrides 
Despite the wide applicability of oxynitrides from photocatalysis to refractory coatings, our understanding of the materials has been limited in terms of their thermodynamics. The configurational entropy via randomly mixed O/N or via cation vacancies are known to stabilize oxynitrides, despite the positive formation enthalpies. Here, using tin oxynitrides as a model system, we show by ab initio computations that oxynitrides in seemingly charge-unbalanced composition stabilize by forming pernitrides among metal-(O,N)6 octahedra. The nitrogen pernitride dimer, =(N-N)=, results in the effective charge of -4, facilitating the formation of nitrogen-rich oxynitrides. We report that the dimer forms only in structures with corner-sharing octahedra, since the N-N bond formation requires sufficient rotational degrees of freedom among the octahedra. X-ray photoemission spectra of the synthesized tin oxynitride films reveal two distinct nitrogen bonding environments, confirming the computation results. This work opens the search space for a novel kind of oxynitrides stabilized by N dimer formation, with specific structural selection rules.

1. S. Kim*, H.J. Gwon, S.W. Paek, S.K. Kim, J.-W. Choi, J.-S. Kim, J.-H. Choi, C.-Y. Kang*, S.-H. Baek*, Sci. Rep. 8, 14471 (2018). 
Speech Date 2019/07/04 10:30~11:00   Speech PlaceRoom 205B 
Speaker Nicola Gaston   CV
AffiliationUniversity of Auckland, New Zealand 
TitleHow stable is 2D gallium, aka gallenene?: electronic and thermodynamic aspects 
Atomically thin gallium layers have been experimentally produced via solid-melt exfoliation, and have been shown to have promise as robustly metallic 2D materials for electronic applications [1]. However the extent to which the experimental technique can be extended to other metals relies on understanding how the 2D structures relate to the bulk form of gallium, which is itself unique as an elemental ‘molecular metal’ (Fig. 1). We relate the experimentally formed 2D materials to the theoretically predicted ’bilayer gallium’ which has previously been shown to be stable in vacuum at the nanoscale, via density functional theory calculations [2,3]. We also study the variation of electronic structure with temperature, to assess the stability of this novel, metallic 2D material under a range of experimentally relevant conditions. 
Speech Date 2019/07/04 14:00~14:30   Speech PlaceRoom 205B 
Speaker Seung Soon Jang   CV
AffiliationGeorgia Institute of Technology, USA 
TitleMultiscale Modeling of Multicompartment Micelle Nanoreactors 
In recent years, research in industrial applications of polymeric materials has begun to explore the field of immobilized catalysis. In particular, the idea of catalysts bound to a micelle backbone, creating a nanoscale molecular reactor (commonly referred to as nanoreactor), has become an area of great interest. From a computational perspective, investigating the potential of micelles as nanoreactors requires analyzing the miscibility of block copolymers, both on a fully atomistic and on a mesoscale basis. The model proposed by Flory and Huggins offers an interaction parameter χ which quantifies the favorability of mixing between two polymers. This interaction parameter depends on many process conditions, not least of which are the temperature and composition of a solution, in order to properly estimate the strength of the interaction between a given pair of polymer molecules. Extensive work has already been completed in this group to establish a robust method of estimating the χ-value for a given pair of molecules; this information is necessary for preparing coarse-grained modeling and simulations (e.g., micellization simulations). In our present work, we apply miscibility analyses to a relatively nascent technology in immobilized catalysis science, viz. the multicompartment micelle nanoreactor. This technology offers a way to harness both the enhanced reactivity of homogeneous catalysis and the ease of separation traditionally enjoyed by heterogeneous catalysis. Through the use of mesoscale calculations, we will study the feasibility of a three-compartment micelle nanoreactor. For this purpose, we have developed a systematic strategy to calculate χ parameters, which has been applied and validated through mesoscale simulations of micelle consisting of triblock copolymers. We hope to demonstrate that this triblock copolymer can form a micelle capable of reaction compartmentalization and tandem catalysis, two hugely promising capabilities for highly selective multistep-catalyzed reactions. 
Speech Date 2019/07/04 15:45~16:15   Speech PlaceRoom 205B 
Speaker Nongnuch Artrith   CV
AffiliationColumbia University, USA 
TitleDevelopment of efficient and accurate machine-learning potentials for the simulation of complex materials 
Many complex materials for energy applications such as heterogeneous catalysts and battery cathode materials have compositions with multiple chemical species and properties that are determined by complex structural features. This complexity makes them challenging to model directly with first principles methods. As an alternative, machine-learning techniques can be used to interpolate first principles calculations. Such machine-learning potentials (MLPs) enable linear-scaling atomistic simulations with an accuracy that is close to the reference method at a fraction of the computational cost. Here, I will give an overview of recent applications of MLPs based on artificial neural networks (ANNs) [1] to the modeling of challenging materials classes, e.g., nanoalloys in solution [2], oxide nanoparticles [3], and amorphous materials [4, 5, 6].
The original multi-species ANN potential formalism [7] scales quadratically with the number of chemical species. This has previously prevented the modeling of compositions with more than a few elements. To overcome this limitation, we have recently developed an alternative mathematically simple and computationally efficient descriptor with a complexity that is independent of the number of chemical species [8,9]. The new methodology has been implemented in our free and open source atomic energy network (aenet) package (http://ann.atomistic.net) [9]. This development creates new opportunities for the modeling of complex materials for example in the field of catalysis and materials for energy applications.

1. J. Behler and M. Parrinello, Phys. Rev. Lett. 98 146401 (2007).
2. N. Artrith and A. M. Kolpak, Nano Lett. 14 2670-2676 (2014);
Comput. Mater. Sci. 110 20-28 (2015).
3. J. S. Elias, N. Artrith, M. Bugnet, L. Giordano, G. A. Botton, A. M. Kolpak,
and Y. Shao-Horn, ACS Catal. 6, 1675-1679 (2016).
4. N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148, 241711 (2018).
5. N. Artrith, A. Urban, Y. Wang, G. Ceder, arXiv 1901.09272 (2019).
6. V. Lacivita, N. Artrith, G. Ceder, Chem. Mater. 30, 7077–7090 (2018).
7. N. Artrith, T. Morawietz, and J. Behler, Phys. Rev. B 83, 153101 (2011).
8. N. Artrith, A. Urban, and G. Ceder, Phys. Rev. B 96, 014112 (2017).
9. N. Artrith and A. Urban, Comput. Mater. Sci. 114, 135-150 (2016). 
· Oral Presentation
Name Joon Kyo Seo 
Affiliation LG Chem、 Ltd. 
Date 2019/07/03 09:30~09:45   PlaceRoom 205B 
Title (O1912_0113) Intercalation and Conversion Reactions of Nanosized β-MnO2 Cathode in the Secondary Zn/MnO2 Alkaline Battery 
Name Jeemin Hwang 
Affiliation Yonsei University 
Date 2019/07/03 09:45~10:00   PlaceRoom 205B 
Title (O1912_0593) Single atom supported on transition metal dichalcogenides as a bifunctional catalyst 
Name Moloud Kaviani 
Affiliation University of Milano-Bicocca 
Date 2019/07/03 10:00~10:15   PlaceRoom 205B 
Title (O1912_0588) Unravelling Dynamical and Light Effects on Functionalized TitaniumDioxide Nanoparticles for Bioconjugation 
Name Kunok Chang 
Affiliation Kyung Hee University 
Date 2019/07/03 10:15~10:30   PlaceRoom 205B 
Title (O1912_0114) Role of magnetic ordering on the spinodal decomposition of Fe-Cr system: A GPU accelerated phase-field study 
Name Heeyuen Koh 
Affiliation Korea Institute of Science and Technology 
Date 2019/07/03 10:30~10:45   PlaceRoom 205B 
Title (O1912_0902) Multilayered network for the optimization of ReaxFF parameters 
Name Arijit Roy 
Affiliation Kookmin University 
Date 2019/07/03 10:45~11:00   PlaceRoom 205B 
Title (O1912_0803) Formation of conducting filament in solid electrolyte due to the electrochemical decomposition: a phase field study 
Name Youngho Kang 
Affiliation Korea Institute of Materials Sicence 
Date 2019/07/03 16:15~16:30   PlaceRoom 205B 
Title (I1912_0117) Computational discovery of indirect band gap semiconductors for photovoltaics using data mining and high-throughput calculations 
Name Byungchan Han 
Affiliation Yonsei University 
Date 2019/07/03 16:30~16:45   PlaceRoom 205B 
Title (O1912_0595) Machine learning driven computational framework for high throughput design of active nanomaterials for energy devices 
Name Joonhee Kang 
Affiliation Korea Institute of Energy Research 
Date 2019/07/03 17:30~17:45   PlaceRoom 205B 
Title (I1912_0119) Reaction barrier informatics on heterogeneous catalysts 
Name Wonseok Jeong 
Affiliation Seoul National University 
Date 2019/07/03 17:45~18:00   PlaceRoom 205B 
Title (O1912_1036) Dynamic bond breaking and forming simulations on advanced material systems by neural network potential 
Name Kyung-Ah Min 
Affiliation Yonsei University 
Date 2019/07/04 09:30~09:45   PlaceRoom 205B 
Title (O1912_0737) First-principles study of reduced Fermi level pinning at metal/MoS2 interfaces 
Name So-Hye Cho 
Affiliation Korea Institute of Science and Technology 
Date 2019/07/04 09:45~10:00   PlaceRoom 205B 
Title (I1912_0122) Theoretical design of heterojunction chalcogenides and their applications for photocatalytic water splitting 
Name Sungwoo Kang 
Affiliation Seoul National University 
Date 2019/07/04 10:00~10:15   PlaceRoom 205B 
Title (O1912_0990) Computational study of CO2 reduction into multi-carbon species at anion vacancy of transition-metal dichalcogenides 
Name Jeong Woo Han 
Affiliation Pohang University of Science and Technology 
Date 2019/07/04 10:15~10:30   PlaceRoom 205B 
Title (I1912_0123) Computational Performance Design of High Performance Electrode for Solid Oxide Electrochemical Cells 
Name Hyung-Kyu Lim 
Affiliation Kangwon National University 
Date 2019/07/04 14:30~14:45   PlaceRoom 205B 
Title (I1912_0126) DFT-CES: Multi-Scale Simulation Framework for Active Interfacial Applications 
Name Min Ho Seo 
Affiliation Korea Institute of Energy Research 
Date 2019/07/04 14:45~15:00   PlaceRoom 205B 
Title (I1912_0127) Computational approaches for durable electro-catalyst in the PEM Fuel Cell 
Name Min-Cheol Kim 
Affiliation Korea Institute of Science and Technology 
Date 2019/07/04 15:00~15:15   PlaceRoom 205B 
Title (O1912_0876) Anisotropic Growth of Pt on Pd Nanocubes Promotes Direct Synthesis of Hydrogen Peroxide 
Name Sangheon Lee 
Affiliation Ewha Womans University 
Date 2019/07/04 15:15~15:30   PlaceRoom 205B 
Title (I1912_0128) Ab initio calculation based characterization of the hydrogen deparation ceramic membranes 
Name Miso Lee 
Affiliation Seoul National University 
Date 2019/07/04 16:15~16:30   PlaceRoom 205B 
Title (O1912_1037) First-principles study of the conduction mechanism of CuI:Sn 
Name Hyung Chul Ham 
Affiliation Korea Institute of Science and Technology 
Date 2019/07/04 16:30~16:45   PlaceRoom 205B 
Title (I1912_0130) Density Functional Theory Study of Nanocatalysts for Fuel Cell Applications 
Name Dong-Hee Lim 
Affiliation Chungbuk National University 
Date 2019/07/04 16:45~17:00   PlaceRoom 205B 
Title (I1912_0131) Reversible dehydrogenation of formate on Pd nanoparticles supported on N-doped graphene 
Name Aloysius Soon 
Affiliation Yonsei University 
Date 2019/07/04 17:00~17:15   PlaceRoom 205B 
Title (O1912_1113) Polymorphic Expressions of Ultrathin Two-Dimensional MoOx on Au(111) 
· Poster Presenter
pdf PosterPresentationSchedule_TS12.pdf