Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session B22: Building the Bridge to Exascale: Applications and Opportunities for Materials, Chemistry, and Biology IIFocus

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Sponsoring Units: DCOMP DMP DCP DBIO Chair: Jack Wells, Oak Ridge National Laboratory Room: BCEC 157C 
Monday, March 4, 2019 11:15AM  11:27AM 
B22.00001: Towards catalysis modeling with QMC Viraaj Jayaram, Ryan Pederson, Liang Li, Anouar Benali, Ye Luo, Maria Chan Predictive modeling of catalytic processes on surfaces is challenging because of large uncertainties in computed energetics using density functional theory (DFT), and exponential dependence of catalyst performance on energies. In particular, the use of various DFT functionals and approximations results in qualitatively different results. The problem is compounded when treating transition metal oxides where a host of DFT errors persist. In this work, made possible by leadership scale high performance computers, we use quantum Monte Carlo (QMC) to determine the adsorption energies of the CO molecule on Cu$_2$O (110) surface for various geometries determined by different DFT approximations. The relationships between geometry and energies, and between DFT and QMC results, will be discussed. 
Monday, March 4, 2019 11:27AM  11:39AM 
B22.00002: Accelerating quantum Monte Carlo simulations on manycore processors via OpenMP nested threading Ye Luo

Monday, March 4, 2019 11:39AM  11:51AM 
B22.00003: Structure and magnetism in bulk and uniaxially strained LaCoO_{3x }through abinitio diffusion quantum Monte Carlo Kayahan Saritas, Jaron Krogel, Ho Nyung Lee, Fernando Reboredo Advances in highperformance computing (HPC) is allowing the use of ambitious methods in materials science with unprecedented accuracy. Density Functional Theory (DFT) accuracy is not sufficient for challenging problems, as it depends critically on the empirical corrections applied, such as HubbardU and exchange mixing. For example, DFT has not yet been able to discern the microscopic origin of the ferromagnetic (FM) state in uniaxially strained LaCoO_{3 }thin films. In contrast, Diffusion quantum Monte Carlo (DMC) method treats electrons explicitly, solving the manybody Schrodinger equation with minimum approximations. DMC has been applied to an increasingly larger set of materials with excellent agreement. In this presentation, we report ground state energies, magnetism, defect formation energies in uniaxially strained LaCoO_{3x }using DMC. DMC yields an antiferromagnetic ground state for bulk uniaxially strained LaCoO_{3}which agrees with the recent experiments. A transition between highspin AFM and FM structures is also identified in the uniaxially strained structures, which may help explain the spin transition in thin films. 
Monday, March 4, 2019 11:51AM  12:03PM 
B22.00004: Accelerating LargeScale GW Calculations on ManyCore and Hybrid CPU+GPU HPC Systems Mauro Del Ben, Felipe Da Jornada, Andrew Canning, Steven G. Louie, Jack Deslippe The novel electronic and optical properties found in complex materials represent the basis for the development of many emerging technologies. The rational design of such technologies requires an accurate quantum mechanical predictive capability (e.g., the GW approximation within manybody perturbation theory and beyond) that can run in reasonable timescales on available high performance computing (HPC) facilities. In this talk we summarize the advances in method development and code optimization of the BerkeleyGW software package targeted for manycore and hybrid CPU+GPU architectures. In particular showing how we have combined methods to reduce the prefactor of the calculations with an optimal implementation suitable for manycore architectures which allowed us to achieve excellent parallel scalability (>500k cores), high fraction of peak performance and thus excellent time to solution for systems up to thousands of atoms. We show how these developments can be combined to take advantages of GPU accelerators. 
Monday, March 4, 2019 12:03PM  12:15PM 
B22.00005: Large scale GW and BSE calculations using interoperable software building blocks Marco Govoni, He Ma, Ngoc Linh Nguyen, Francois Gygi, Giulia Galli The modeling of lightmatter interaction in complex heterogenous systems is key to several materials design problems. A microscopic modeling of twoparticle correlation functions requires the solution of the BetheSalpeter equation (BSE) based on many body perturbation theory. However, the application of BSEGW calculations to complex nanostructured, disordered or defective materials has been hindered by high computational costs. We present a method, implemented into interoperable software building blocks, to compute optical spectra and exciton binding energies based on the solution of the Liouville equation and the calculation of the screened Coulomb interaction in finite field. The method does not require the explicit evaluation of dielectric matrices nor of virtual electronic states, and can be easily applied to large systems, beyond the random phase approximation. Localized orbitals obtained from Bloch states using bisection techniques are used to reduce the complexity of the calculation and enable the efficient use of hybrid functionals. We will discuss the advantages of this paradigm, and provide results for the calculation of spectroscopic features of promising materials for spin qubits. 
Monday, March 4, 2019 12:15PM  12:27PM 
B22.00006: Pushing the Accuracy Limits of Scalable FixedNode Diffusion Monte Carlo for Noncovalent Interactions Matus Dubecky Singledeterminant (SD) fixednode diffusion Monte Carlo (FNDMC) gains popularity as a benchmark tool scalable to large noncovalent systems although its accuracy limits are not yet fully mapped out. Some of the recent results suggest that accuracy of SD FNDMC is capable of achieving benchmark accuracy for a rich class of noncovalent systems, larger than expected before. The talk will summarize recent progress and current accuracy limits of biascancellationbased SD FNDMC for molecules and noncovalent crystals. The best available "recipe" suitable for a wide class of noncovalent systems will be sketched out. 
Monday, March 4, 2019 12:27PM  1:03PM 
B22.00007: Deep Machine Learning for AtomicallyResolved Imaging Experiments: Physics Extraction and Feedback Invited Speaker: Maxim Ziatdinov Development of the imaging tools such as electron, optical, and scanning probe microscopy in the last decade of 20th century has opened floodgates of imaging data, in the form of images, movies, and hyperspectral data sets. These contain information on the minute details of atomic structure and electronic, magnetic, and phonon functionalities, chemical transformation mechanisms, and quantum phenomena. However, the bottleneck in analysis and knowledge extraction from large volumes of raw imaging data is often human domain expert. Here I will show how utilization of deep artificial neural networks (aka deep learning) offers a path to overcome limitations of human analysis. I will specifically discuss applications of deep learning for rapid fully automated identification of individual atomic types and positions from scanning transmission electron microscopy images, using theoretical or labeled experimental images as a training set. We used this approach to construct reaction pathways for point defects in 2D materials, trace the structural evolution of atomic species during the electron beam manipulation, and create the library of defect configurations in silicon and vacancy doped graphene. I will discuss specific examples where we showed that coupling of sulphur vacancy to molybdenum dopants in tungsten disulfide and reactions of silicon impurity on the edge and in the bulk of graphene can be explored quantitatively and mapped on the Markov model, giving rise to the transition probabilities on single atomic defect level. The work on genetic engineering optimization framework for automatic design of deep learning network architecture and hyperparameters optimal specifically for electron microscopy datasets will be addressed. Finally, I will discuss how a synergy of deep learning image analytics and realtime feedback allows harnessing beaminduced atomic and bond dynamics to enable direct atombyatom fabrication. 
Monday, March 4, 2019 1:03PM  1:15PM 
B22.00008: Decoding Inverse Imaging Problems in Materials with Distributed Deep Learning Nouamane Laanait, Albina Y Borisevich, Alexander Sergeev, Sean Treichler, Michael A. Matheson Materials physics abounds with challenging inverse problems, from inverse materials design to reconstruction of material properties from imaging/scattering data. One of the oldest inverse problems in materials is the reconstruction of the local atomic scattering potential from convergent beam electron diffraction (CBED). In this talk, we will present results on a potential reconstruction approach based on deep neural networks (DNN). In particular, we used data parallelism to distribute the training of a DNN over 12,000 GPUs on the Summit supercomputer. By efficient utilization of Summit's burst buffer and halfprecision arithmetics, the data processing rates of the DNN reached upwards of 100 GB/s, allowing for the processing of local CBED patterns from 60,000+ crystal structures in the matter of minutes. We will present challenges encountered in distributed training at these scales, such as I/O bottlenecks and DNN convergence and how they can be mitigated. Finally, initial results on our DNNbased reconstruction of local atomic potentials of strontium irridate superlattices will be presented. 
Monday, March 4, 2019 1:15PM  1:27PM 
B22.00009: Convolutional neural network based super resolution for brain cell mapping Prabhat KC, Vincent De Andrade, Narayanan Kasthuri, JussiPetteri Suuronen Synchrotron based fullfield nanoCT (computed tomography) holds the key to map the cellular composition of the entire brain cells. In particular, our inhouse Transmission Xray Microscope (TXM) at the Advanced Photon Source (sector 32ID) in the Argonne National Laboratory is designed to achieve a spatial resolution of 20 nm. But our ability to resolve features of nervous system in few nanometers  required to properly discern the connectivity in the whole brain  is constrained by the laws of optics of the Xray microscope (namely depth of focus). Accordingly, in this contribution, we propose the use of deep convolutional neural network (CNN) based superresolution to achieve a resolution beyond the limit of the TXM. The mapping from low resolution to high resolution will be deduced accounting for degradations such as blur kernel and noise level to faithfully model the artifacts observed in TXM based CT results. Finally, the L2 based loss function will be combined with regularization to preserve the edges of minute structure seen in brain cells. 
Monday, March 4, 2019 1:27PM  1:39PM 
B22.00010: Towards Exascale Biomolecular Simulations with Artificial Intelligence Workflows Arvind Ramanathan, Debsindhu Bhowmik, heng ma, Michael Todd Young, Chris Stanley Molecular simulations take significant supercomputing resources, roughly between 40 and 60% of time. While emerging Exascale compute architectures promise to provide an unprecedented capability to simulate complex physical phenomena, they also pose a number of computational challenges for effective scaling such simulations. We describe our research in scaling biomolecular simulations by tightly integrating artificial intelligence (AI) approaches using our Molecules library. We demonstrate Molecules can extract biophysically meaningful reaction coordinates from long timescale simulations (postprocessing and/or in situ) that can be interpreted with respect to experimental data. We then show that the AIderived reaction coordinates can be used to accelerate molecular simulations, especially in discovering novel conformational states that can be used to drive additional exploration through high dimensional landscapes. Using reinforcement learning techniques we demonstrate that these states can sample novel folding pathways that have not been explored previously. Together, we show that our integrated workflow can effectively accelerate sampling of high dimensional conformational landscapes while simultaneously making use of emerging hardware features of Exascale architectures. 
Monday, March 4, 2019 1:39PM  1:51PM 
B22.00011: Dynamical Implications of Hyperparameters in Reinforcement Learning Hyun Jin Kim, Daniel Shams, David Schwab Reinforcement learning has been gaining popularity within the physics research community over past several years. Many of the algorithms require selection of hyperparameters, and it is unclear how these choices affect the learned policy. Often in practice, researchers choose hyperparameters for their simulations using brute force methods such as exhaustive grid search or heuristics without fully understanding how they may alter the effective reward structure and value function landscape. Here, we investigate the possibility of using a nonlinear dynamics approach to guide our selection and adaptive optimization of appropriate hyperparameters that are better aligned to achieve particular aims of the agent. We then extend our investigation to the setting of multiagent reinforcement learning and game theory to see how the choice of agents hyperparameters affect the interplay between each other in competitive, cooperative, and mixed settings. 
Monday, March 4, 2019 1:51PM  2:03PM 
B22.00012: ExaTN  A Scalable Exascale Math Library for Hierarchical Tensor Network Representations and Simulations Alexander McCaskey, Eugen Dumitrescu, Dmitry Liakh, Gonzalo Alvarez, Tiffany Mintz A problem that will benefit from exascale computing is predictive simulation of two and threedimensional quantum manybody Hamiltonians. Enabling efficient numerical simulations requires new insights into wavefunction compression that scales on extremely heterogeneous HPC architectures. To address this need, we develop ExaTN: a math library of parallel numerical primitives for processing operations based on hierarchical tensor representations. Our library provides a scalable infrastructure for building a performance portable framework for simulating strongly correlated quantum systems on heterogeneous HPC platforms such as Titan and Summit. Our framework is the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a promising hierarchical tensor decomposition scheme capable of expressing local expectation values in strongly entangled quantum systems efficiently. In this talk, we will present our integrated framework for processing hierarchical tensor representations on exascale HPC systems via a multilevel asynchronous taskbased programming model. This provides scientists with a capability for simulating strongly entangled systems in condensed matter physics. 
Monday, March 4, 2019 2:03PM  2:15PM 
B22.00013: UV/vis absorption spectra database autogenerated for optical applications via the Argonne data science program Edward J. Beard, Ganesh Sivaraman, Alvaro VazquezMayagoitia, Venkatram Vishwanath, Jacqueline M Cole A large corpus of material and experimental data exists in historic scientific literature. Natural language processing and datamining approaches can be applied to curated scientific literature to extract chemical information for targeted functionality and applications. This talk focuses on development of a UV/vis absorption spectra database by means of a complex quantum chemistry workflow built on the top of the ChemDataExtractor tool [1] by leveraging DOE Argonne leadership computing facilities as a part of the data science project allocation. We show results of retrieving chemical information and experimental properties from a large sample of scientific literature (~ 400,000) with chemdataextractor. Some electronic structure properties of a large subset of compounds are modeled using quantum chemistry workflows for benchmarking and validation. Finally, the quality of the database is discussed based on validation metrics and its applicability to optical applications. 
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