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 M41: Tools for 2D ScienceLive
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Sponsoring Units: DCMP Chair: Dante O'Hara, US Naval Research Lab |
Wednesday, March 17, 2021 11:30AM - 11:42AM Live |
M41.00001: High throughput search for polaronic materials Pedro Melo, Joao Carlos Abreu, Bogdan Guster, Matteo Giantomassi, Xavier Gonze, Matthieu Verstraete Over the past 10 years, immense progress has been made in the ab initio calculation of electron-phonon coupling and its consequences, in particular through improvements on the Allen Heine Cardona theory and exploiting the Frohlich model with its coupling constant "alpha". The simple Frohlich model shows qualitative agreement with many polar materials. However, with just one longitudinal optical phonon mode and a single isotropic parabolic electron it should have strong limits in real materials. We perform a high throughput scan of existing phonon and electron band structures (Materials Project), to identify trends and outliers. We sketch the limits of validity of the Frohlich model, through comparison of the strong-coupling localisation length with interatomic distances, signaling small polaron formation, and by tracking the breakdown of the lowest-order perturbation theory treatment. Our approach accounts for anisotropic and degenerate electronic bands, and multiple phonon modes. A large variety of behaviors is found, and more accurate fully ab initio calculations are performed to analyze extreme cases, with Frohlich alpha beyond 6 (when perturbation theory breaks down), and very large zero point motion renormalizations of the band gap. |
Wednesday, March 17, 2021 11:42AM - 11:54AM Live |
M41.00002: High-throughput searches for novel 2D and 1D materials Davide Campi, Nicolas Mounet, Marco Gibertini, Antimo Marrazzo, Thibault Sohier, Giovanni Pizzi, Nicola Marzari Low-dimensional materials have emerged as promising candidates for next-generation applications in the fields of electronics, optoelectronics and energy storage. In a previous study [1] we performed an extensive high-throughput screening of experimentally known inorganic materials, identifying more than 1800 compounds exfoliable into novel two-dimensional monolayers. Thanks to the inclusion of new structures obtained from an additional experimental database, new versions of the original sources and a refined screening procedure, we have added 1200 candidates to our portfolio. We also completed a broad characterization of their properties, focusing on relevant descriptors for field-effect applications, superconductivity and photocatalysis. Finally, using a similar screening procedure, we identified more than 800 1D or quasi-1D wires that could be isolated from their vdW-bonded parents. Despite being less studied that 2D monolayers, 1D wires are likely to show promising performances in many applications. Thanks to the use of the AiiDA (http://aiida.net) platform, all the calculations are stored in a searchable, reproducible and readily shared form via the Materials Cloud (http://www.materialscloud.org) portal. |
Wednesday, March 17, 2021 11:54AM - 12:06PM Live |
M41.00003: Discovering new materials with the AiiDAlab platform: examples from a combined computational/experimental research laboratory Aliaksandr V. Yakutovich, Kristjan Eimre, Ole Schütt, Leopold Talirz, Carl S. Adorf, Casper W. Andersen, Edward Ditler, Dou Du, Daniele Passerone, Berend Smit, Nicola Marzari, Giovanni Pizzi, Carlo Antonio Pignedoli In Computational Materials Science, discovery of new materials often entails complex workflows - sequences of interdependent computational tasks. As they get more convoluted, the risk that such sequences remain usable only by a minority of experts increases. It becomes essential to provide an environment that enables their definition and deployment in a manner advocated by open science standards, facilitating reproducibility, sharing of data as well as dissemination of software. |
Wednesday, March 17, 2021 12:06PM - 12:18PM Live |
M41.00004: Generating a FAIR crystal-structure database with the AiiDA informatics infrastructure Sebastiaan P. Huber, Marnik Bercx, Nicolas Hörmann, Giovanni Pizzi, Nicola Marzari Computer simulations that use powerful electronic-structure techniques are nowadays widely used to characterize or predict materials’ properties. Such efforts rely on databases of measured or calculated data, with structural data being especially useful. Here, we develop and validate a set of protocols to generate a comprehensive structural database of 3D materials abiding to the FAIR data principles. We start from structures taken from three major experimental databases: the Pauling file (MPDS), the inorganic crystal structure database (ICSD), and the crystallography open database (COD). After removal of non-stoichiometric compounds and duplicates, structures are refined with density-functional theory calculations using the open-source SIRIUS accelerated library together with Quantum ESPRESSO. Since calculations are driven by the AiiDA (http://aiida.net) materials’ informatics infrastructure, all curated workflows, the entire provenance of the simulations and the resulting structural data can be shared openly on the Materials Cloud (http://materialscloud.org). We present our protocols and their validation, together with the use of AiiDA's advanced automation and error handling features to create robust workflows for electronic-structure simulations. |
Wednesday, March 17, 2021 12:18PM - 12:30PM Live |
M41.00005: Capturing the full momentum dependency in diagrammatic calculations Simão João, Aires Ferreira, João Manuel Viana Parente Lopes The study of disorder in Condensed Matter Physics is as old as the field itself. Disorder can suppress desirable material properties such as the conductivity but it can play a fundamental role in quantum phase transitions and can even be shown to enhance superconductivity. The requirement of a realistic quantum description of disorder led to the development of diagrammatic techniques which are able to deal with disorder in a controlled way. Several approximation schemes may be employed in order to resum an infinite subseries of diagrams, such as the self-consistent T-matrix approximation and the self-consistent Born approximation. While nonperturbative, these approaches typically fail to capture any momentum dependency of the disorder-averaged Green's function coming from disorder. Here, we present an exact method to capture the momentum dependency due to disorder in the disorder-averaged Green's function, effectively summing a larger subset of diagrams. We use KITE to apply this method to several 2D systems such as the square lattice subject to Anderson disorder, graphene with vacancies and SrRuO3. Our method is valid for any amount of disorder and is in complete agreement with diagrammatic calculations in both the limit of very low and very high concentration of impurities. |
Wednesday, March 17, 2021 12:30PM - 12:42PM Live |
M41.00006: MechElastic: A Python Library for Analysis of Mechanical and Elastic Properties Sobhit Singh, Logan Lang, Viviana Dovale-Farelo, Uthpala Herath, Pedram Tavadze, Francois-Xavier Coudert, Aldo H Romero In this work, we present a friendly open-source python library to carry out the analysis of elastic properties of materials. A python package, MechElastic, has been built, which can parse the output elastic tensor data generated from several widely used DFT packages such as ABINIT, VASP, Quantum Espresso, and SIESTA, and compute various elastic and mechanical properties. It can also test the mechanical stability of a given material using the Born-Huang criteria and estimate hardness using six different semi-empirical relations. This package neatly puts all the employed equations and related references in one place for easy access for the apprentice researchers. With some additional inputs of energy/pressure versus volume data (theoretical or experimental), one can perform the equation of state (EOS) analysis using Vinet, Birch, Murnaghan, and Birch-Murnaghan models. Further, MechElastic has an interface with the online ELATE package for analyzing anisotropy in elastic properties, so, now users can access the features of the ELATE package directly in an offline mode. MechElastic can be used in a high-throughput manner for large scale DFT calculations. |
Wednesday, March 17, 2021 12:42PM - 12:54PM Live |
M41.00007: Two- and three-body tight-binding model for efficient materials design Kevin Garrity, Kamal Choudhary Over the past decade, semi-local density functional theory (DFT) calculations of small unit cells have become routine, with databases of such calculations regularly used for materials design. However, important but computationally expensive properties like surface and defect energies, thermal conductivity, molecular dynamics trajectories, etc, continue to be beyond the limits of automated databases. This highlights the continued need for simplified models that can combine large DFT datasets with physical principles to make predictions. In this talk, we detail our efforts to fit a self-consistent tight-binding model, including both two-body and three-body interaction terms, to a large dataset of DFT calculations that includes elements from across the periodic table. Our model, which calculates band structures and total energies, shows promise for future materials design applications. |
Wednesday, March 17, 2021 12:54PM - 1:06PM Live |
M41.00008: Structure prediction of ionic materials using the Minima Hopping method and the CENT machine learning potential Stefan A Goedecker, Hossein Tahmasbi, Ehsan Rahmatizad Khajehpasha, Samare Rostami, Hossein Asnaashari, Somayeh Faraji, Maximilian Amsler, S. Alireza Ghasemi Ionic materials and in particular oxides are the dominating class of materials on earth. Because of the huge number of structures in this class, a theoretical exploration of novel low-energy phases is desirable. Up to now, only density functional theory was sufficiently accurate for general purpose structure predictions. Hence they were numerically quite expensive and could only be applied to relatively small systems. The charge equilibration via neural network technique (CENT) potential allows to calculate energies and forces faster by several orders of magnitude and enables large scale structure predictions. Being based on a charge equilibration scheme, the CENT potential allows us to describe accurately the energy associated to the charge transfer that is the dominant bonding mechanism in ionic materials. Even though it is trained only on previously known structures it can reliably predict the energy of entirely new structures. The new structures are found by the Minima Hopping structure prediction method which can escape in an efficient way from the funnels of the known input structures. We will present novel structures for TiO2 sheets, bulk and surfaces of CaF2, stoichiometric and non-stoichiometric phases of MgO, as well as crystalline structures of SrTiO3 and LiCl. |
Wednesday, March 17, 2021 1:06PM - 1:18PM Live |
M41.00009: Finger prints based biasing for finding complex reaction pathways Deb De, Marco Krummenacher, Stefan A Goedecker Determining the pathway of a reaction/transformation |
Wednesday, March 17, 2021 1:18PM - 1:30PM Live |
M41.00010: Automated in silico optimisation of nanoporous material morphology for enhanced carbon dioxide adsorption Rodrigo Ferreira, Fausto Martelli, Binquan Luan, Tonia Elengikal, Anshul Gupta, Guojing Cong, Mathias Steiner, Thomas Peters, Flor Siperstein, Breanndan O Conchuir Millions of possible crystalline nanoporous materialsa,b have been identified for carbon capture, extending far beyond our capability to quantify the in silico adsorption performance of each individual nanopore by brute force calculations. Experimentally fabricating and measuring the adsorption properties of each framework is also unrealistic. A pre-screening step is required for better resource allocation. In this talk, we present our work which optimises the classification mechanisms for characterizing nanopore structures, enabling efficient high throughput nanopore screening. |
Wednesday, March 17, 2021 1:30PM - 1:42PM Live |
M41.00011: Measurement of sub pm/V in-plane piezo-coupling coefficients using lateral PFM Sai Saraswathi Yarajena, Akshay K Naik Piezoelectric effect, in combination with other properties of two-dimensional (2D) materials, has relevance for various electromechanical applications. 2D layers such as hBN, transition-metal dichalcogenides and many other emerging 2D materials exhibit in-plane piezoelectricity. We propose a technique to quantitatively measure the in-plane piezoelectric coupling for 2D materials. The method involves a novel approach for in-plane field excitation in lateral Piezoresponse force microscopy (PFM) to facilitate the measurement for the 2D materials. We utilize the contact resonance gain of the AFM probe to improve the detection sensitivity by more than an order of magnitude. Piezoelectric coupling coefficients as low as pm/V can be measured using the proposed method. The technique is verified by estimating the in-plane piezoelectric coupling coefficients for freely suspended MoS of one to five atomic layers. The technique is useful for quantitative characterization of the in-plane piezo-coupling in the emerging 2D-materials. |
Wednesday, March 17, 2021 1:42PM - 1:54PM Live |
M41.00012: Improved method for measuring crystal orientation of strained graphene using polarization dependence of Raman G band and 2D bands Hikari Tomori, Kazushi Nakamura, Akinobu Kanda The electron behavior in graphene is described by the Dirac equation, which gives graphene the unique property of generating a pseudo-field due to the lattice strain. The pseudo-magnetic field is predicted theoretically to induce phenomena such as band gap formation and electron confinement. On the other hand, such strain-induced phenomena are strongly dependent on the direction of strain. Therefore, it is important to accurately measure the crystal orientation and the strain direction in order to study the strain-related phenomena in graphene. However, in conventional methods for measuring crystal orientation of strained graphene using the polarization dependence of the Raman G-band position, the obtained strain angles include systematic errors due to the misorientation of the sample with respect to the Raman device system. In this study, we developed a method to obtain both the strain direction and the polarizer angle simultaneously from the polarized Raman spectroscopy of the G and 2D bands. |
Wednesday, March 17, 2021 1:54PM - 2:06PM Live |
M41.00013: Genarris 2.0: A Random Structure Generator for Molecular Crystals Rithwik Tom, Tim C Rose, Imanuel Bier, Harriet O'Brien, Alvaro Vazquez-Mayagoitia, Noa Marom Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. For all polymorphs, the final pools contained the experimental structure. Reference: Computer Physics Communications 250, 107170 (2020). |
Wednesday, March 17, 2021 2:06PM - 2:18PM Live |
M41.00014: Space group predictions in inverse materials search BIN XI, Kinfai Tse, Tsz Fung Kok, Junyi Zhu Inverse materials search aims to search of wanted material system with desired properties that is often done with the help of modern searching algorithms. However, the searching speed is often limited by the huge parameter space. In this work, we proposed a machine-learning-assisted approach to enhance the material search speed. The dataset contains 51000 theoretically calculated electronic band structures of different crystals that includes metals, semiconductors, and insulators from Materials Project. A supervised learning is adopted to train neural networks to give predictions of 7 crystal systems and 230 space groups with bands degeneracies as input. As a result, over 90% and over 70% prediction accuracies are obtained for 7 crystal systems and 230 space groups respectively. Thus the predicted crystal system and space groups information are as extra input parameters to accelerate material search. The factors that affects neural network training are mainly due to orbital degeneracies and the lifted degeneracies by spin-orbital coupling. |
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