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 M28: Computational Design, Understanding and Discovery of Novel Materials IVFocus
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Sponsoring Units: DMP Chair: Jorge Munoz, University of Texas at El Paso; Rodrigo Freitas, MIT Room: Room 220 |
Wednesday, March 8, 2023 8:00AM - 8:36AM |
M28.00001: Predicting solid-state synthesis recipes for computationally-designed materials Invited Speaker: Wenhao Sun DFT is widely used to predict structure-property relationships for materials design, but the Schrodinger equation provides little guidance on how to actually synthesize newly-predicted materials. Here, I will show how we can guide solid-state synthesis planning using information that is largely-available in high-throughput materials databases like the Materials Project. First, I will discuss a conceptual strategy to navigate high-dimensional convex hulls in the search of reactive precursors for more-efficient materials syntheses. Using this strategy, we design novel precursors for 32 target quaternary oxide materials, and validate with a high-throughput robotic synthesis laboratory that our DFT-guided precursors are substantially more successful at synthesizing complex multicomponent oxides than traditional precursors. Next, I will show that the onset temperature of a solid-state reaction derives from the extension of liquidus curves into the metastable region of a Temperature-Composition phase diagram. In order to predict this metastable liquidus curve, I will present a strategy to train CALPHAD models on DFT convex hulls and ASM phase diagrams, which enables us to rapidly estimate the high-temperature liquidus curves of phase diagrams at minimal computational cost. |
Wednesday, March 8, 2023 8:36AM - 8:48AM |
M28.00002: Design Rules and Predictive Descriptor for High-Entropy Materials Design and Discovery Liping Yu, Dibyendu Dey, Liangbo Liang High entropy materials (HEMs) constitute an enormously large but least explored compositional space and have gained significant and increasing interest due to their potential applications and remarkable functional properties. Despite considerable progress has been made in understanding the salient features of these materials, the prediction of new HEMs remains a grand challenge. In this work, we propose a Mixed Enthalpy-Entropy Descriptor (MEED) that can enable robust high-throughput screening for new HEMs over large chemical spaces. This descriptor can be easily and quickly calculated from first principles, requiring no experimental or empirically derived inputs. Taking metal carbides as the benchmark system, the MEED successfully identifies all experimentally reported single-phase high-entropy metal carbides. By this descriptor, these high-entropy carbides are not only clearly separated from those element combinations that are experimentally known to form multiple phases, but also predicted with correct relative magnitudes of their growth temperatures. With MEED, additional four- and six-metal carbide materials and a set of new 2D high entropy transition metal dichalcogenides are also predicted, and the predicted top candidates also perfectly agree with available experimental results. |
Wednesday, March 8, 2023 8:48AM - 9:00AM |
M28.00003: Probabilistic Analysis of Entropy Stabilized Oxides using DFT and Machine Learning Lily J Joyce, Kristen E Johnson, Christina M Rost, Kendra L Letchworth-Weaver Entropy Stabilized Oxides (ESOs) are a novel class of materials which are enthalpically unfavorable, but entropically favorable due to high configurational disorder. Though not able to directly predict formation energies of ESOs, enthalpy based methods such as Density Functional Theory (DFT) remain useful for gathering bond length data, oxidation states, and other statistics from the microstates representing the local environments of these materials. These statistics are useful for comparison to experimental methods such as XAS, provided that the microstates are representative of the real material. For systems large enough to be representative of the material, DFT can be quite computationally expensive, so instead we utilize Machine Learning (ML) algorithms to identify structural and energetic descriptors based on DFT. We aim to use these ML algorithms to scan through potential ESO candidates and predict which ones will be the best for more in-depth study using the more accurate but computationally expensive DFT. |
Wednesday, March 8, 2023 9:00AM - 9:12AM |
M28.00004: A song of energy scales and technology: The elusive challenge of Heusler alloys Adam Hauser, Ridwan Nahar, Ka Ming Law Fabrication of ternary intermetallic alloys with high atomic ordering is a critical step to realizing their predicted functional properties. For instance, Heusler alloys have long been predicted with high spin polarization, low spin damping, and high ferromagnetic ordering temperatures. Unfortunately, experimental realization and applications have proven stubbornly elusive: Models and rules-of-thumb have found limited success when expanded to a wider range of elemental choices, and atomic ordering has typically been lower than predicted, with deleterious effect on their material properties. In this talk, I will outline our theoretical and experimental efforts to understand the root causes behind the experimental disconnect with theory in Heusler systems, and our efforts to find and realize materials that do not have deleterious atomic disorder or melting temperatures too high to allow layered growth. |
Wednesday, March 8, 2023 9:12AM - 9:24AM |
M28.00005: Electronic Properties and Phase Stability of Full and Inverse Heusler X2FeAl (X=20 transition metal elements) by First-Principle Calculations Ka Ming Law, Ridwan Nahar, Sujan Budhathoki, Michael Zengel, Thomas Roden, Justin Lewis, Adam Hauser Before intermetallic Heusler alloys can be best applied in spintronic devices, materials must be found that either (1) have strong energetic preference for atomic configurations with high spin polarization, or (2) exhibit spin polarizations that remain high even in the face of atomic disorder. Thus, these properties are targeted in our search for functional spintronic Heusler compounds, including both ab initio calculations and experimental verification. |
Wednesday, March 8, 2023 9:24AM - 9:36AM |
M28.00006: Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements Weiyi Xia, Huaijun Sun, Chao Zhang, Ling Tang, Renhai Wang, Georgiy Akopov, Nethmi W Hewage, Kai-Ming Ho, Kirill Kovnir, Cai-Zhuang Wang We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La–Si–P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La–Si–P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La–Si–P structures from our ML-guided predictions, a number of low-energy La–X–P phases (X = Ge, Sn, Pb) are predicted. |
Wednesday, March 8, 2023 9:36AM - 9:48AM |
M28.00007: High-throughput Design of Interfacial Perpendicular Magnetic Anisotropy at Materials Interfaces Kesong Yang, Sicong Jiang Perpendicular magnetic anisotropy (PMA) at ferromagnet/insulator interfaces has important technological applications in spintronic devices like magnetic recording and sensing devices. In recent years, perpendicular magnetic tunnel junctions (p-MTJs) with strong PMA have attracted increasing interest because of their high stability and low energy consumption. Heusler alloys are a family of compounds with promising magnetic properties for the development of p-MTJs. However, choosing appropriate Heusler ferromagnets and insulators with desirable interfacial properties is challenging. In this talk, I will discuss our recent research progress to search for candidate Heusler/MgO material interfaces with strong PMA and other desired material properties for spintronic technologies using a high-throughput screening approach. |
Wednesday, March 8, 2023 9:48AM - 10:00AM |
M28.00008: Integrated machine learning assisted high-throughput discovery of novel magnetic double perovskite oxides Shuping Guo, Jeroen van den Brink, Oleg Janson Magnetic properties of double perovskites (DPs) are rich but underlain by complex microscopic exchange mechanisms. Machine learning (ML) assisted high-throughput (HT) methods have witnessed great success but employ either simplified atomic inputs or exhaustive band-structure calculations which fall short where strong electron correlations play a significant role. Here we take advantage of hopping parameters generated by Wannierization and existing experimental data to train the integrated machine learning (ML) model capable of predicting DPs without experimental observations. We use classification learning to distinguish between anti-ferromagnetic (AFM) and ferromagnetic (FM) exchange dominated DPs with the high accuracy of 82%, regression learning to fit magnetic transition temperatures (Tc) with the mean square error of 19 and 67 K, respectively, and propose two AFM and four FM candidates with high Tc. Our methodology is able to replace resource-demanding HT calculations, open up the possibility of ML assisted HT computations and provide new insights into better understanding the underlying physical mechanisms of complex magnetic properties. |
Wednesday, March 8, 2023 10:00AM - 10:12AM |
M28.00009: A data-driven interpretation of the stability of molecular crystals Rose K Cersonsky, Michele Ceriotti Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, in this talk I will propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. I will introduce a structural descriptor tailored to the prediction of the binding energy for a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. I will then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights that can be extracted from this analysis, showcasing a complete database to guide the design of molecular materials. |
Wednesday, March 8, 2023 10:12AM - 10:24AM |
M28.00010: Ab initio workflow for high-throughput multiferroic materials discovery Francesco Ricci, Ella Banyas, Stephanie Mack, Jeffrey B Neaton Ferroics are an important and exciting class of compounds with great potential for applications that exhibit ferroelectricity, magnetism, or both. High-throughput ab initio approaches, in conjunction with materials databases, have been shown effective for screening and discovery of a broad variety of new materials. Here, we build on a previously-developed automated high-throughput workflow [1] to identify a large number of new ferroelectric, antiferroelectric, and multiferroic compounds using density functional theory and the Materials Project. Importantly, our newly-developed workflow does not require the existence of nonpolar insulating “reference” structures in the database. Instead, our more general approach identifies polar insulating materials on or near the convex hull, and assesses possible ferroelectric order, (antiferro)magnetic order, or both by (i) constructing a prospective nonpolar parent phase using a pseudosymmetries-based approach; and (ii) determining the stable magnetic ground state ordering for potential magnetic compounds. The results of applying our new workflow to more than 1500 polar and magnetic materials will be discussed. [1] doi:10.1038/s41597-020-0407-9. |
Wednesday, March 8, 2023 10:24AM - 10:36AM |
M28.00011: First-principles prediction of the phase stability of high-entropy oxides Francisco Marques dos Vieira, Ismaila Dabo, Saeed S I Almishal, Jon-Paul Maria, Sai Venkata Gayathri Ayyagari, Nasim Alem, Simon Gelin, Tara Karimzadeh Sabet The fundamental thermodynamic driving forces enabling the formation of high-entropy oxides (HEOs) are not clearly understood. Since their discovery by Rost et al. [1], studies of the stability of HEOs have primarily focused on the role of ideal mixing entropies with comparatively little attention paid to the configurational influence of mixing enthalpies. In a recent paper, Bokas et al. [2] presented a model capable of predicting the mixing enthalpies of high-entropy alloys from the mixing enthalpies of their constituent binaries. Herein, we apply and further develop this methodology to HEOs by parameterizing a regular solution model using the mixing enthalpies of pairs of constituent end members. Using this model, one can identify HEO compositions most thermodynamically amenable to the formation of solid solutions. This method is tested on two systems of interest: Sr(Ti,V,Nb,Cr,Mo,W)O3 perovskites and (Fe,Co,Ni,Cu,Zn)Al2O4 spinels. |
Wednesday, March 8, 2023 10:36AM - 10:48AM |
M28.00012: Piezoelectric ferromagnetism in two-dimensional materials via computational materials screening Kayahan Saritas, Sohrab Ismail-Beigi A two-dimensional (2D) ferromagnetic system that is also piezoelectric will permit electric field control of magnetism via field-induced strain and structural distortions. Using ab initio computational materials screening, we predict that monolayer FeCl2 is a two-dimensional piezoelectric ferromagnet (PFM) with easy-axis magnetism and a Curie temperature of 260 K [1]. Our density functional theory (DFT) calculations combined with data mining reveal 2H-FeCl2 as the only easy-axis 2D monolayer PFM, and that its magnetic anisotropy increases many-fold with moderate hole doping. We develop post-processing analysis tools using magnetic anisotropy densities that explain the magnetic and doping-dependent behavior of FeCl2, as well as VSe2 and CrI3, and can enable the design of future 2D magnetically ordered materials. |
Wednesday, March 8, 2023 10:48AM - 11:00AM |
M28.00013: High-throughput computational screening of 2D materials for transverse thermoelectrics Rifky Syariati, Hikaru Sawahata, Susumu Minami, Naoya Yamaguchi, Fumiyuki Ishii Thermoelectric power generation has attracted interest due to converting the thermal losses in various technology and solving environmental issues. High-performance thermoelectric power generation might be achieved by designing thermoelectric devices compatible with any heat source[1]. Transverse thermoelectricity in 2D materials can be used to fulfilling a high degree of design freedom due to their unique and controllable thermal and electronic transport properties[2]. Some databases provide the electronic properties of 2D materials [3,4] and thermoelectric materials[5]. However, there are no databases about the transverse thermoelectric properties such as anomalous Hall conductivity and anomalous Nernst conductivity and their relation with 2D magnetic materials. In this study, we have investigated the transverse thermoelectric effect in 2D magnetic materials by using automated high throughput density functional theory calculations and the semiclassical Boltzmann transport theory[6]. We treat the conductivity tensors using the Berry connection defined on a discretized Brillouin zone to accelerate the calculation of transverse thermoelectric properties[7,8]. With this high-throughput screening, we found that there are 2D crystals that have not been previously classified as favorable transverse thermoelectric materials. We predict that 2D Chern insulator materials are promising transverse thermoelectric materials due to their Seebeck-driven effect. We also predict that 2D materials possessing van Hove singularity could boost the thermoelectric coefficients[9]. |
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