Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session C21: Machine Learning for Quantum Matter IIIFocus Live
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Sponsoring Units: DCOMP GDS DMP Chair: Muratahan Aykol, Toyota Research Institute |
Monday, March 15, 2021 3:00PM - 3:36PM Live |
C21.00001: Neural networks for atomistic modelling - are we there yet? Invited Speaker: Emine Kucukbenli
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Monday, March 15, 2021 3:36PM - 3:48PM Live |
C21.00002: Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks Tess Smidt, Mario Geiger, Benjamin Kurt Miller Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions into simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry-breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites. |
Monday, March 15, 2021 3:48PM - 4:00PM Live |
C21.00003: Machine learning dielectric screening for the simulation of excited state properties of molecules and materials Sijia Dong, Marco Govoni, Giulia Galli Accurate and efficient predictions of absorption spectra of materials and molecules at finite temperature are essential for the understanding and rational design of broad classes of systems. We present an approach to improve the efficiency of first principles calculations of absorption spectra of complex materials at finite temperature, based on the solution of the Bethe-Salpeter Equation in finite-field (FF) [1]. We demonstrate that methods using convolutional neural networks (CNN) may be efficiently used to compute the screened Coulomb interaction and to predict finite-temperature absorption spectra of solids, liquids, nanoparticles, and heterogeneous systems, such as solid/liquid interfaces. In addition, we show that our approach may be used to derive model dielectric functions for complex systems [2]. |
Monday, March 15, 2021 4:00PM - 4:12PM Live |
C21.00004: Generative Model Learning For Molecular Electronics Andrew Mitchell, Jonas Rigo, Sudeshna Sen The use of single-molecule transistors in nanoelectronics devices requires a deep understanding of the generalized 'quantum impurity' models describing them. Microscopic models comprise molecular orbital complexity and strong electron interactions while also treating explicitly conduction electrons in the external circuit. No single theoretical method can treat the low-temperature physics of such systems exactly. To overcome this problem, we use a generative machine learning approach to formulate effective models that are simple enough to be treated exactly by methods such as the numerical renormalization group, but still capture all observables of interest of the physical system. We illustrate the power of the new methodology by application to the single benzene molecule transistor. |
Monday, March 15, 2021 4:12PM - 4:24PM Live |
C21.00005: An assessment of the structural resolution of various fingerprints commonly used in machine learning Behnam Parsaeifard, Deb De, Anders Christensen, Felix A Faber, Emir Kocer, Sandip De, Jorg Behler, O. Von Lilienfeld, Stefan A Goedecker Atomic environment fingerprints are widely used in computational materials science. In this work, we compare the performance of fingerprints based on the Overlap Matrix(OM), the Smooth Overlap of Atomic Positions (SOAP), Behler-Parrinello atom-centered symmetry functions (ACSF), modified Behler-Parrinello symmetry functions (MBSF) used in the ANI-1ccxpotential and the Faber-Christensen-Huang-Lilienfeld (FCHL) fingerprint under various aspects. We study their ability to resolve differences in local environments and in particular examine whether there are certain atomic movements that leave the fingerprints exactly or nearly invariant. For this purpose, we introduce a sensitivity matrix whose eigenvalues quantify the effect of atomic displacement modes on the fingerprint. Further, we check whether these displacements correlate with the variation of localized physical quantities such as forces. Finally, we extend our examination to the correlation between molecular fingerprints obtained from the atomic fingerprints and global quantities of entire molecules. |
Monday, March 15, 2021 4:24PM - 4:36PM Live |
C21.00006: Vestigial nematic order in Pd-RTe3 studied using X-ray diffraction TEmperature Clustering (X-TEC) Krishnanand Mallayya, Michael Matty, Joshua Straquadine, Matthew Krogstad, Raymond Osborn, Stephan Rosenkranz, Ian R Fisher, Eun-Ah Kim Nematic order can arise from a number of physical origins. A vestigial nematic order associated with a disordered uni-directional charge density wave (CDW) has been a topic of much theoretical interest with the relatively little direct experimental investigation. Here, we use diffuse x-ray scattering to study the effects of Pd-intercalation, which introduces controlled disorder, on CDW formation in ErTe3, a weakly orthorhombic material for which CDW fluctuations are present in both in-plane directions. For this, three-dimensional reciprocal space volumes were collected covering 20000 Brillouin Zones using the Pilatus 2M CdTe detector on Sector 6-ID-D at the Advanced Photon Source. We then use our recently developed machine learning tool, X-TEC [1] to explore these comprehensive data sets. |
Monday, March 15, 2021 4:36PM - 4:48PM Live |
C21.00007: Reactive Machine Learning Potential Models for the NO Formation Reaction Andrew Johannesen, Jason Goodpaster Machine learning potentials provide a method for accurate energy assessment at affordable computational cost. In this work, we produce machine learning potentials for CO2, O2, N2, and NO, with the goal of modeling reactive equilibria between the latter three species. |
Monday, March 15, 2021 4:48PM - 5:00PM Live |
C21.00008: Achieving Smaller Effective Spot Sizes in nano-ARPES with Machine Learning Conrad Stansbury, Alessandra Lanzara Nano-ARPES is emerging as a critical and interpretable technique for the study of van der Waals heterostructures and devices, but extending the applicability of the nano-ARPES to traditional quantum materials and stretching the reach of ARPES experiments with larger beams remains a practical challenge for the time being. We demonstrate that convex optimization and machine learning enhanced nano-ARPES allows for resolving the individual contributions of sub-beam domains to the ARPES spectra of discrete domain patterned materials. By using this method instead of naïve averaging—the typical approach for photoemission experiments—arbitrarily larger beams can be used. We explore the conditions for applicability of our technique, improvements that can be made by leveraging the physical invariants of the photoemission measurement, and the applicability of the technique to the study of phase transitions in quantum materials. |
Monday, March 15, 2021 5:00PM - 5:12PM Live |
C21.00009: INVESTIGATING BAND GAP DIRECTNESS USING MACHINE LEARNING Elton Ogoshi de Melo, Mário Popolin Neto, Carlos Mera Acosta, Gabriel M. Nascimento, João Rodrigues, Osvaldo N. Oliveira Jr., Fernando V. Paulovich, Gustavo M. Dalpian Bandgap directness is the basis of most optoelectronic device applications, e.g., direct band gaps materials are expected to have high efficient light emission. However, no unified theory exists to explain why a given material has direct or indirect band gaps. Using all semiconductors’ band structures from Materials Project, a total of 18,372 materials, we have used classification Machine Learning methods for general prediction of band gap directness and, more importantly, for extraction of interpretable knowledge of direct-indirect transitions. Here we used a visualization tool for the rules in the decision trees method of machine learning, referred to as Explainable Matrix (ExMatrix), with the main purpose of deepening the current understanding of such transitions. We applied this methodology for prototypical groups of semiconductors such as zincblende, rocksalt, wurtzite and perovskites, leading to insights regarding the influence of chemical composition, structural parameters and individual atomic properties on band structures. Such results might lead to new strategies for engineering band gap directness, thus widening the applicability of indirect band gap semiconductors. |
Monday, March 15, 2021 5:12PM - 5:24PM On Demand |
C21.00010: Unsupervised machine learning of quantum phase transitions using diffusion maps Alex Lidiak Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available. Unsupervised machine learning methods are particularly promising in overcoming this challenge. For the specific task of learning quantum phase transitions, unsupervised machine learning methods have primarily been developed for phase transitions characterized by simple order parameters, typically linear in the measured observables. However, such methods often fail for more complicated phase transitions, such as those involving incommensurate phases, valence-bond solids, topological order, and many-body localization. We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning such complex phase transitions unsupervised. This method may work for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators as a versatile tool for learning various quantum phases and phase transitions. |
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