Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session F47: Computational Design and Discovery of Novel Materials IIFocus Recordings Available

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Sponsoring Units: DCOMP DMP Chair: Robert Wexler, Princeton University Room: McCormick Place W470B 
Tuesday, March 15, 2022 8:00AM  8:12AM 
F47.00001: Quantifying uncertainty in DFT energy corrections Amanda X Wang, Ryan Kingsbury, Matthew McDermott, Matthew Horton, Anubhav Jain, Shyue Ping Ong, Shyam Dwaraknath, Kristin Persson Density functional theory (DFT) is routinely used to estimate enthalpies of formation, phase stability, and other energyderived properties. The accuracy of these estimates can be improved by using experimental thermodynamic data to develop energy corrections that remove some of the systematic error in DFTcomputed properties. In this work, we demonstrate a method to quantify uncertainty in these energy correction values which captures uncertainty propagated from the underlying experimental data as well as sensitivity to the selection of fit parameters. We then incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidationstate and compositiondependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. 
Tuesday, March 15, 2022 8:12AM  8:24AM 
F47.00002: Generalized Gibbs' Phase Rule Wenhao Sun Gibbs’ Phase Rule describes the nature of phase boundaries on phase diagrams and is a foundational principle in materials thermodynamics. In Gibbs’ original derivation, he stipulates that the Phase Rule applies only to “simple systems”—defined to be homogeneous, isotropic, uncharged, and large enough that surface effects can be neglected; and not acted upon by electric, magnetic or gravitational fields. Modern functional materials—spanning nanomaterials, multiferrorics, materials for energy storage and conversion, colloidal crystals, etc.—are decidedly nonsimple, often leveraging additional forms of thermodynamic work to achieve their functionality. Adding thermodynamic variables into a freeenergy expression increases the dimensionality of its corresponding thermodynamic space. Here we revisit Gibbs’ original arguments on phase coexistence and show that phase boundaries in highdimensional Internal Energy space, U(S,X_{i},…), are simplicial convex polytopes—which are Ndimensional analogues of triangles and tetrahedra. From this geometric description we derive a generalized form of Gibbs’ Phase Rule; which can be combined with highthroughput DFT calculations of solidstate entropies, strain tensors, surface energies, magnetic structures, and polarization displacements to build entirely new classes of phase diagrams. These generalized phase diagrams can exist in multiple (≥3) thermodynamic dimensions, and exhibit elastic, surface, electromagnetic or electrochemical work on their axes. New phase diagrams are poised to expand the thermodynamic toolkit beyond the common TP and Tx phase diagrams, enabling materials scientists to fully interrogate the complex thermodynamic environments of modern materials. 
Tuesday, March 15, 2022 8:24AM  8:36AM 
F47.00003: Extracting effective spinorbit coupling from first principles calculations via the Wannier representation Jinwoong Kim, David Vanderbilt The construction of Wannier Hamiltonians (WHs) from densityfunctional calculations can play an important role in theoretical investigations of condensedmatter systems by combining the virtues of an intuitive tightbinding representation with underlying parameterfree firstprinciples calculations. Such WHs are in widespread use for Wannier interpolation and extraction of topological properties of band structures. Here, instead, we focus on useful local chemical and physical information that can be extracted from the WH. Site energies and interatomic hoppings are easily extracted from the matrix elements of the WH, but more complicated terms such as spinorbit coupling (SOC) and crystal field splitting require more care. Here we propose a systematic approach for extracting the strength of such complex operators from a WH, and illustrate its power by applying it to several example systems. These include Fe_{2}S_{2}, in which the effective SOC was recently reported [1] to exhibit a giant dependence on the Hubbard onsite U. We quantitatively confirm that this is largely an artifact of an instability to the formation of orbital magnetic order at large U values, and that the dependence of the true SOC strength on U is more modest, though still significant. 
Tuesday, March 15, 2022 8:36AM  8:48AM 
F47.00004: Phonon anharmonicity via harmonic ensemble lattice dynamics (HELD) Jorge A Munoz The vibrations of atoms about their equilibrium positions critically influence the thermal properties of the material, and the vibrations themselves are affected by volume and temperature. The effects of volume (quasiharmonicity) can be handled accurately and efficiently by modern techniques, but the effects of temperature (anharmonicity) are more challenging to compute and much more computationally expensive. We present a method that calculates ‘instantaneous’ phonon dispersion curves in the harmonic approximation from structures with thermal disorder that are then aggregated into intensity maps to produce finite phonon linewidths that approximate anharmonic phonon broadening. The severity of the approximations and the advantages of this technique will be discussed, and the results for several BCC elements and BCCbased alloys will be shared. 
Tuesday, March 15, 2022 8:48AM  9:24AM 
F47.00005: Ab initio structure prediction accelerated with machine learning interatomic potentials Invited Speaker: Aleksey Kolmogorov Prescreening candidate structures with inexpensive classical potentials is gaining renewed interest due to recent advances in machine learning (ML) modeling methodology. For effective acceleration of ab initio prediction, ML interatomic potentials must provide accurate description of diverse configurations probed in global structure searches. I will overview an automated framework implemented in our MAISE package that constructs practical neural network interatomic models for multielement chemical systems. Recent applications of the approach to wellstudied crystalline and nanoscale materials have led to the prediction of stable structures overlooked in previous investigations. Our studies highlight evident advantages and remaining limitations of the ML methodology in guiding ab initio structure prediction. 
Tuesday, March 15, 2022 9:24AM  9:36AM 
F47.00006: Useful MachineLearned Interatomic Potentials Gus L Hart Interatomic Potentials have long been used for atomistic modeling where the interesting questions are out of reach by firstprinciples approaches. Traditional empirical potentials are typically fitted to experimental data. They typically have poor general accuracy but are physically wellbehaved. On the other hand, machinelearned interatomic potentials are far more expressive than physically motivated interatomic potentials like LennardJones, StillingerWeber, Embedded Atom Potentials, etc., but they are also more likely to be completely wrong outside of the training domain, are more difficult to train reliably, and are computationally expensive. We have developed MLIPs for the HfNiTi shape memory alloy. We share cautionary tales, best practices for generating training sets, and demonstrate how community tools make for "easy entry" to realistic thermodynamic modeling with these potentials. 
Tuesday, March 15, 2022 9:36AM  9:48AM 
F47.00007: Development of neural network interatomic potentials for accelerated prediction of stable compounds Saba Kharabadze, Aidan Thorn, Ernesto D Sandoval, Samad Hajinazar, Aleksey Kolmogorov Construction of machine learning interatomic potentials suitable for guiding unconstrained ab initio 
Tuesday, March 15, 2022 9:48AM  10:00AM 
F47.00008: Firstprinciples modeling and machine learning of the Gibbs free energy of the FeC system in a magnetic field Ming Li, Stephen R Xie, Ajinkya C Hire, Richard G Hennig, Luke Wirth, Dallas Trinkle Modeling the thermodynamics and kinetics of steels for designing processes in high magnetic fields requires knowledge of the magnetic Gibbs free energy. Monte Carlo and thermodynamic perturbation or integration methods require integrals over configuration space involving millions of accurate potential energy evaluations. Densityfunctional theory (DFT) calculations provide sufficient accuracy to describe the FeC phases. However, the high computational cost of DFT hinders the direct application to thermodynamic and kinetic modeling. 
Tuesday, March 15, 2022 10:00AM  10:12AM 
F47.00009: ColabFit: Collaborative Development of DataDriven Interatomic Potentials Joshua Vita Atomic interactions in classical molecular simulations are modeled using a function called an interatomic potential (IP). Traditionally, IPs have used functional forms that explicitly represent aspects of the bonding and/or geometry of the system and are fitted to small datasets of key material properties. Recently, interest has grown in datadriven IPs (DDIPs) which use machine learning methods to interpolate first principles calculations. Due to the lack of explicit physics, DDIPs must be trained on large datasets and frequently retrained when applications fall outside the original dataset. To facilitate this and allow research groups to easily exchange DDIPs and their training datasets, it is important to develop a standard for archiving and retrieving datasets. The ColabFit project (https://colabfit.openkim.org/) aims to address this need by enabling the development, exchange, and deployment of DDIPs and their datasets through the OpenKIM framework (https://openkim.org/). In this work we outline a standard for distributing DDIP training sets and showcase the functionality of the ColabFit tools and data repository for providing open access to a large collection of high quality training data. 
Tuesday, March 15, 2022 10:12AM  10:24AM 
F47.00010: Pressuredependent layerbylayer oxidation of ZrS_{2}(001) surface Liqiu Yang, Subodh C Tiwari, Seong S Jo, Sungwook Hong, Ankit Mishra, Aravind Krishnamoorthy, Rajiv K Kalia, Aiichiro Nakano, Rafael Jaramillo, Priya Vashishta ZrS_{2}is an important semiconductor and shows superior chemical catalytic properties among transition metal dichalcogenides family. Recently, ZrS_{2}has been reported to be easily oxidized under native environment. To understand their longterm stability, oxidation of ZrS_{2}has been studied experimentally and computationally. In this work, reactive molecular dynamics simulations are performed using a previously developed reactive force field to study the pressure dependence of oxidation of ZrS_{2}(001) surface. Oxidation rate is found to be tuned under different oxygen partial pressure at a temperature of 300 K. The observed pressure dependence is analyzed in terms of the DealGrove model. To quantify the initial oxidation behavior, the binding energy of oxygen is calculated. We also find alayerbylayer oxidation in the initial oxidation stage. Such atomistic details are indispensable for controlling the oxidation of ZrS_{2}in actual device processing. 
Tuesday, March 15, 2022 10:24AM  10:36AM 
F47.00011: Threebody tightbinding for the periodic table Kevin F Garrity, Kamal Choudhary Over the past two decades, density functional theory calculations (DFT) have become the workhorse method for electronic structure calculations; however, there are always limitations in terms of the number of calculations or sizes of unitcells. Tightbinding calculations fit to DFT are a way to bridge this gap, but appropriate models are not always available, especially for materials design applications where many materials may be studied. In this work, we introduce a versatile tightbinding model that builds the Hamiltonian from both the typical twobody interactions between atoms plus threebody interactions. The threebody terms allow the local neighborhood of a pair of atoms to modify their interaction. These terms allow the model to fit a wide range of structures and stoichiometries without loss of accuracy. We fit this model to a database of over 800,000 DFT calculations of elemental and binary systems, allowing users of the model to immediately begin calculations. To ensure the fitting produces accurate predictions outofsample, we use the model to relax new structures from random starting structures, and then compare the energies to new DFT calculations. These new calculations are added to the fitting until the performance improves. 
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