Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session EE09: ICTP-SAIFR Satellite Meeting: Condensed Matter Theory in São Paulo |
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Chair: Alexandre Rocha, Instituto de Fisica Teorica - UNESP Room: Virtual Room 9 |
Monday, March 20, 2023 10:00AM - 10:30AM |
EE09.00001: Tuning Spin Splittings in 2D Compounds Gustavo Dalpian Materials databases have shown to be fundamental to workflows in Materials Informatics, serving as a starting point for the search of new materials and new physical insights. With the intent of contributing to the growing ecosystem of high-throughput databases of DFT calculations, here we provide a highly structured catalogue of spin splitting (SS) properties of 2D materials. SS effects, resulting from breaking of spin degeneracy, are the main target functionalities in spintronics applications. Identification and design of SS prototypes (Rashba, Dresselhaus and Zeeman) are fundamental, and mechanisms for the SS control are also indispensable for applications in devices. The growing number of proposed and fabricated 2D materials may offer novel routes for SS control. Here we used an inverse design process, based on the enabling design principles of SS, to select 437 materials from the C2DB database. DFT calculations with spin-orbit coupling were carried out to identify and classify them according to their SSs. The result is an extensive and descriptive catalogue of SSs, from which we employed a Bayesian analysis to investigate chemical and structural trends, contributing to a rationalized design of 2D materials and assessing the potential usage of the new generated data. |
Monday, March 20, 2023 10:30AM - 11:00AM |
EE09.00002: Emergent Parafermionic Zero Modes in Fermionic Systems Luis G Dias Da Silva Parafermionic bound states, Zn-symmetric generalizations of Majorana zero modes, can emerge as edge states in strongly correlated systems displaying fractionalized excitations. The non-trivial fractional nature of Z3 parafermions, in particular, can be used to produce Fibonacci anyons, a key ingredient in a universal topological quantum computer. |
Monday, March 20, 2023 11:00AM - 11:30AM |
EE09.00003: Perturbing the Kitaev Model Eric Andrade The Kitaev model is a fascinating example of an exactly solvable model displaying a spin-liquid ground state in two dimensions. In real materials, however, deviations from the original Kitaev model are expected to appear. In this talk, I will discuss the fate of Kitaev's spin-liquid in the presence of disorder -- bond defects or vacancies -- and other magnetic couplings. Considering static flux backgrounds, we observe a power-law divergence in the low-energy limit of the density of states with a non-universal exponent. We link this power-law distribution of energy scales to weakly coupled droplets inside the bulk, in an uncanny similarity to the Griffiths phase often present in the vicinity of disordered quantum phase transitions. If time-reversal symmetry is broken, we find that power-law singularities are tied to the destruction of the topological phase of the Kitaev model in the presence of bond disorder alone. There is a transition from this topologically trivial phase with power-law singularities to a topologically non-trivial one for weak to moderate site dilution. Therefore, diluted Kitaev materials can potentially host Kitaev's chiral spin-liquid phase. |
Monday, March 20, 2023 11:30AM - 12:00PM |
EE09.00004: Enhancing Molecular Dynamics Simulations of Aqueous Systems with Deep Neural Network Force Fields Márcio Sampaio Molecular dynamics simulations have been widely utilized in various scientific fields to study a variety of physical systems. However, the accuracy of these type of simulations strongly depends on the model used to describe the atomic interactions. Although ab initio molecular dynamics (AIMD) based on density functional theory (DFT) offers high accuracy, it is limited to small systems and relatively short simulation times. In this context, Neural Network Force Fields (NNFFs) play an important role by providing a way to overcome these limitations. In this study, we investigate NNFFs designed at the DFT level to describe liquid water, with a focus on the size and quality of the training data set. We show that training data set should be sampled from uncorrelated snapshots with various system conditions and configurations, which provides a good distribution over the phase space allowing one to significantly reduce the amount of data and the size of the NN required to have accurate NNFF. Furthermore, our results demonstrate that structural properties of water are less dependent on the size of the training data set compared to dynamical properties, such as the diffusion coefficient. |
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