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 D44: Computational Design, Understanding and Discovery of Novel Materials IFocus
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Sponsoring Units: DMP Chair: Rodrigo Freitas, MIT; Mauro Del Ben, LBNL Room: Room 316 |
Monday, March 6, 2023 3:00PM - 3:36PM |
D44.00001: Designing magnesium alloys from density-functional theory and atomistic models Invited Speaker: Dallas R Trinkle Magnesium alloys have a high strength-to-weight ratio and are therefore of interest to transportation industries as light weight alternatives to heavier non-ferrous and ferrous alloys. However, broader application of wrought Mg alloys in ground transportation vehicles, for example, has been limited by low room temperature ductility, poor formability, and corrosion. The low polycrystalline ductility is a result of the yield strength anisotropy of the HCP crystal structure of Mg. The search for possible alloying routes involve understanding how dislocations--fundamental carriers of plastic deformation--interact with solute elements. Over the past decade, significant effort has gone into the modeling of dislocations with first principles density-functional theory along with interactions with solutes. This includes flexible boundary condition methods to accurately predict dislocation cores, density-functional theory energy density methods to compute dislocation core energies, direct computation of solute interactions as well as intermediate modeling approaches to accelerate the computation of solute interactions. In addition to accurate data, we can understand the effect of solutes their environment and the correlation with dislocation interactions. This fundamental data directly parameterizes larger scale models to identify possible alloying routes beyond what has been explored by traditional metallurgical approaches. |
Monday, March 6, 2023 3:36PM - 3:48PM |
D44.00002: Machine learning and Monte Carlo simulations of the Gibbs free energy of the Fe-C system in a magnetic field Ming Li, Richard G Hennig, Luke Wirth, Dallas R Trinkle, Ajinkya C Hire, Stephen R Xie, Michele Campbell To model the thermodynamics and kinetics of steels in high magnetic fields requires knowledge of the magnetic Gibbs free energy, G, which involves millions of energy evaluations for the potential energy landscapes as a function of the applied field. Density-functional theory (DFT) calculations provide sufficient accuracy but are computationally very demanding. To overcome this barrier, we apply the ultra-fast force field (UF3) machine learning model [1] to approximate the DFT energy landscape. A DFT database is assembled through VASP, focusing on the energies and forces as a function of magnetic field for bcc and fcc Fe(C) with different structural and magnetic configurations. The UF3 models are trained and validated on this database to quickly evaluate the energies of ensembles. The resulting UF3 models are then utilized in the subsequent Monte Carlo simulations. Thermodynamic integration is utilized to combine the simulations at different temperatures to achieve the magnetic G models for the two Fe(C) phases as a function of temperature, atomic fraction of carbon, and magnetic field. Our calculations show that the applied magnetic field of around 10 T results in a change in the transition temperature of tens of kelvins. |
Monday, March 6, 2023 3:48PM - 4:00PM |
D44.00003: Computational prediction of the chemical order-disorder phase diagram of FeV Cesar Diaz, Jorge A Munoz We report a computational prediction of the chemical order-disorder metastable phase diagram of iron-vanadium (Fe-V) binary alloys throughout the whole composition range. The temperature-dependent energetic and entropic contributions to the free energy were calculated using quantities derived from equations of state obtained from molecular dynamics simulations with a classical potential. The cluster expansion method was used to isolate the effects of composition and of chemical order in the first and second coordination shells and study their relative importance to phase stability. The uncertainty of the predictions was quantified based on distributions and propagation of errors. The phase boundaries are in good agreement with experimental measurements. |
Monday, March 6, 2023 4:00PM - 4:12PM |
D44.00004: Large-scale search for stable tin alloys with machine learning potentials Daviti Gochitashvili, Aidan Thorn, Saba Kharabadze, Aleksey Kolmogorov We have recently developed an automated framework for generating accurate machine learning potentials (MLPs) to accelerate ab initio structure prediction. Following our predictions of new thermodynamically stable Li-Sn compounds, we have expanded the MLP-guided evolutionary ground state searches to several M-Sn binary systems (M = Na, Mg, Ca, Cu, Pd, and Ag). The systematic exploration of the full binary composition ranges has uncovered a number of new crystal structure phases thermodynamically stable at different pressures and temperatures. |
Monday, March 6, 2023 4:12PM - 4:24PM |
D44.00005: First-principles elastic and mechanical properties from Born perturbation expansion Changpeng Lin, Samuel Poncé, Davide Campi, Nicola Marzari Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present an efficient and accurate approach for calculating the elastic tensor of crystalline solids based on interatomic force constants and Born perturbation expansion, applicable for any dimension [1]. We have implemented the theory in the first-principles Quantum ESPRESSO distribution [2] and performed an extensive validation against conventional finite-difference calculations and experimental measurements for Si, NaCl, graphene and monolayer MoS2. We then apply our computational approach to the high-throughput screening of elastic properties of two-dimensional materials from the MC2D database [3], and we identify various candidates with outstanding or unique mechanical properties. The methodology developed and the elastic properties computed in this work will benefit the discovery and design of novel functional materials. |
Monday, March 6, 2023 4:24PM - 4:36PM |
D44.00006: Mechanism governing electronic charge rearrangements in random alloys Wai-Ga D Ho, Mariia Karabin, Yang Wang, Markus Eisenbach, George M Stocks, Xianglin Liu, Wasim R Mondal, Hanna Terletska, Ka-Ming Tam, Liviu Chioncel, Vladimir Dobrosavljevic Recent work [1] has computationally investigated the statistics of internal charge distribution and transfers in metallic high entropy alloys using various first-principle approaches. It demonstrated that fluctuating internal electrostatic (“Madelung”) potentials arise generically in random alloys, due to the random environments characterizing any given lattice site. In this work, we establish the physical mechanism governing the statistics of these fluctuations, by analytically formulating an appropriate self-consistent screening theory for random system. Our theory is based on perturbative approaches to disorder, as appropriate for metallic alloys. It suggests a path to formulate an appropriate extension of standard KKR-CPA methods, which is capable of capturing the fluctuations of the internal Madelung potentials, thus providing a computationally cheap yet accurate first-principle description of high entropy alloys. |
Monday, March 6, 2023 4:36PM - 4:48PM |
D44.00007: Electrical resistivity of disordered systems using first-principles LSMS calculations Vishnu Raghuraman, Markus Eisenbach, Yang Wang, Michael Widom The Kubo-Greenwood equation in combination with first-principles locally self-consistent multiple scattering (LSMS) theory can be used to calculate the residual resistivity of disordered systems. The disorder in this framework is modelled using large unit cells. We implement this method in the open source software package MuST (https://github.com/mstsuite/MuST) and validate it by calculating pure element and binary random alloy resistivities, which are well-studied. We then apply this to more exotic systems like metallic glasses and quasicrystals. The results are compared with experiment. |
Monday, March 6, 2023 4:48PM - 5:00PM |
D44.00008: Action space and features for complex multicomponent alloys and ceramics property prediction with deep reinforcement learning Artem Pimachev, Sanghamitra Neogi Multicomponent alloys and ceramics including alloys for thermoelectrics, ultra-high-temperature ceramics for aerospace applications, and refractory high entropy alloys (HEA) have seen rapid growth due to their superior mechanical, thermal, and magnetic properties. The large configurational space offers unique opportunities for discovery of new configurations with wide range of physical properties. Describing the unique atomic environments is crucial for establishing material-property relationship. We develop and implement a set of descriptors based on the elemental properties and ordering of atomic species. In combination with the actions derived from the engineered descriptors we propose a deep reinforcement learning approach to reach the target property. We demonstrate the approach to predict physical properties for discover configurations for ternary compounds such as (AlxGayInz)2NO3N and three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. |
Monday, March 6, 2023 5:00PM - 5:12PM |
D44.00009: Multi-objective optimization of High-entropy alloy properties Franco Moitzi, Oleg E Peil, Max Hodapp, Lorenz Romaner Body-centered cubic (bcc) high-entropy alloys possess high strength retention at high temperatures, while suffering |
Monday, March 6, 2023 5:12PM - 5:24PM |
D44.00010: Efficient generation of doped crystal structure model by combinatorial mathematics Ryo Maezono, Genki I Prayogo, Andrea Tirelli, Keishu Uchimura, Kenta Hongo, Kousuke Nakano A common approach for studying a solid solution or disordered system within a periodic ab initio framework is to create a supercell in which certain amounts of target elements are substituted with other elements. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from many substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. Our developed Python software package [1], which is called Shry (Suite for High-throughput generation of models with atomic substitutions implemented by Python), allows the selection of only symmetry-inequivalent structures from the vast number of candidates based on the canonical augmentation algorithm. Shry is implemented in Python3 and uses the CIF format as the standard for both reading and writing the reference and generated sets of substituted structures. Shry can be integrated into another Python program as a module or can be used as a stand-alone program. The implementation was verified through a comparison with other codes with the same functionality, based on the total numbers of symmetry-inequivalent structures, and also on the equivalencies of the output structures themselves. The provided crystal structure data used for the verification are expected to be useful for benchmarking other codes and also developing new algorithms in the future. |
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