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Surface energies of elemental crystals
Published in Scientific Data, 2016
The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal’s equilibrium shape. Such surface phenomena are especially important at the nanoscale, where the large surface area to volume ratios leads to properties that are significantly different from the bulk. In this work, we present the largest database of the calculated surface energies of elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous experimental and computational data where available. We will describe the methodology used in constructing the database, and how it can be accessed for further studies and design of materials.
Recommended citation: Tran, R., Xu, Z., Radhakrishnan, B., Winston, D., Sun, W., Persson, K. A., & Ong, S. P. (2016). Surface energies of elemental crystals. Scientific Data, 3, 160080. https://doi.org/10.1038/sdata.2016.80
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Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries
Published in Acta Materialia, 2016
Mo and its alloys have been widely used as refractory materials owing to their excellent high temperature properties, but a critical limitation is their low ductility. Doping the grain boundaries (GBs) of Mo with metals such as Zr or Al have previously been demonstrated as a promising approach to address this shortcoming, whereas other alloy elements are known to embrittle the GBs. In this work, we investigated the segregation and strengthening/embrittling effects of 29 metallic dopants at the Σ5(310) tilt and Σ5(100) twist Mo GBs using density functional theory (DFT) calculations and empirical continuum models. In agreement with previous works for other metals, we find that the strain, as measured by the relative metallic radius versus Mo, is a good predictor of the segregation tendency, while the difference in cohesive energies between the dopant and Mo is a good predictor of the strengthening/embrittling effect. However, we find that dopant chemistry also plays a significant role in affecting segregation behavior at GBs, particularly in driving the formation of intermetallic precipitates or 2-D interfacial phases (complexions). We also show that the site preference of a dopant in the GB can lead to strengthening effects that deviate from those predicted using simple bond-breaking arguments. Assuming a fast cleavage model of fracture, Ta, Re, Os and W are predicted to have a weak strengthening effect on Mo for the Σ5(310) tilt GB, and Mn, Fe, Co and Nb are predicted to have reasonable strengthening effects for the Σ5(100) twist GB.
Recommended citation: Tran, R., Xu, Z., Zhou, N., Radhakrishnan, B., Luo, J., & Ong, S. P. (2016). Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries. Acta Materialia, 117, 91–99. https://doi.org/10.1016/j.actamat.2016.07.005
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Role of Zr in strengthening MoSi2 from density functional theory calculations
Published in Acta Materialia, 2018
MoSi2 is an important intermetallic with excellent oxidation resistance at high temperatures above 1000 °C. However, its application at lower temperatures is limited by oxygen embrittlement, a phenomenon known as “pesting”. In this work, we comprehensively investigate the role of Zr in mitigating pesting in MoSi2 using density functional theory calculations. We show that Zr dopants reduce the embrittling effects of oxygen interstitials at MoSi2 grain boundaries by being a charge donor to oxygen. However, a more substantial effect is observed when Zr is present as a secondary getter nanoparticle phase. Oxygen interstitials have a strong thermodynamic driving force to migrate into the Zr subsurface at the Zr/MoSi2 interface, and the work of separation of the clean and oxygen-contaminated Zr/MoSi2 interfaces are much higher than that of MoSi2 grain boundaries. Finally, we present an efficient screening approach to identify other potential getter elements using simple thermodynamic descriptors, which can be extended to other alloy systems of interest. These findings provide crucial fundamental insights and further avenues to optimize Mo and other alloys.
Recommended citation: Zheng, H., Tran, R., Li, X. G., Radhakrishnan, B., & Ong, S. P. (2018). Role of Zr in strengthening MoSi2 from density functional theory calculations. Acta Materialia, 145, 470–476. https://doi.org/10.1016/j.actamat.2017.12.017
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Rational synthesis and electrochemical performance of LiVOPO4 polymorphs
Published in Journal of Materials Chemistry A, 2019
LiVOPO4 is a promising cathode material for Li-ion batteries due to its ability to intercalate up to two electrons per vanadium redox center. However, LiVOPO4 exhibits polymorphism, forming either the αI, β, or ε phase. A thorough comparison between the properties of these phases is difficult because they usually differ in synthesis methods. In this study, we synthesize all three polymorphs by annealing a single precursor, LiVOPO4·2H2O, thereby reducing the effect of synthesis on the properties of the materials. We show through in situ XRD with heating and DFT calculations that, in terms of stability, αI-LiVOPO4 ⋘ ε-LiVOPO4 ≤ β-LiVOPO4. We also show experimentally and through DFT calculations that the tolerance to O-interstitials and O-vacancies can explain the differences in stability, morphology, and electrochemical performance between β- and ε-LiVOPO4. β-LiVOPO4 is more stable in the presence of O-interstitials while ε-LiVOPO4 is favored in the presence of O-vacancies. These defects affect the surface energies and morphology of the products formed, which are confirmed in the Wulff shape calculations and transmission electron microscopy images. These imply that β-LiVOPO4 has an improved rate performance under an oxidizing atmosphere due to the increased presence of facets with superior ion diffusion at the surface. This improved performance is seen by the improved rate capability and capacity of β-LiVOPO4 in the high-voltage region. In contrast, synthesis conditions have little effect on improving the rate performance of ε-LiVOPO4.
Recommended citation: Hidalgo, M. F. V., Lin, Y.-C., Grenier, A., Xiao, D., Rana, J., Xin, H., Tran, R., Zuba, M. J., Donohue, J., Omenya, F. O., Chu, I.-H., Wang, Z., Li, X., Chernova, N., Chapman, K. W., Zhou, G., Piper, L. F. J., Ong, S. P., & Whittingham, M. S. (2019). Rational Synthesis and Electrochemical Performance of LiVOPO4 Polymorphs. Journal of Materials Chemistry A, 7(14), 8423–8432. https://doi.org/10.1039/C8TA12531G
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Anisotropic work function of elemental crystals
Published in Surface Science, 2019
The work function is a fundamental electronic property of a solid that varies with the facets of a crystalline surface. It is a crucial parameter in spectroscopy as well as materials design, especially for technologies such as thermionic electron guns and Schottky barriers. In this work, we present the largest database of calculated work functions for elemental crystals to date. This database contains the anisotropic work functions of more than 100 polymorphs of about 72 elements and up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. The database has been rigorously validated against previous experimental and computational data where available. We also propose a weighted work function based on the Wulff shape that can be compared to measurements from polycrystalline specimens, and show that this weighted work function can be modeled empirically using simple atomic parameters. Furthermore, for the first time, we were able to analyze simple bond breaking rules for metallic systems beyond a maximum Miller index of one, allowing for a more generalized investigation of work function anisotropy.
Recommended citation: Tran, R., Li, X.-G., Montoya, J., Winston, D., Persson, K. A., & Ong, S. P. (2019). Anisotropic work function of elemental crystals. Surface Science, 687(September), 48–55. https://doi.org/10.1016/j.susc.2019.05.002
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Chlorine-Doped Perovskite Oxide: A Platinum-Free Cathode for Dye-Sensitized Solar Cells
Published in ACS Applied Materials & Interfaces, 2019
Triiodide/iodide (I3–/I–) redox couple-mediated solar cells, batteries, and electrochromic devices require highly efficient and stable electrocatalysts for I3– reduction reaction (IRR) to overcome performance limitations, whereas the widely used platinum (Pt) cathode for IRR has limitations of high price and unfavorable durability. In this work, we present a halogen element (chlorine) doping strategy to design low-cost perovskite-type electrocatalysts with enhanced IRR activity and stability. The dye-sensitized solar cell (DSSC) assembled by the LaFeO2.965−δCl0.035 cathode delivers an attractive power conversion efficiency (PCE) of 11.4% with a remarkable PCE enhancement factor of 23% compared with Pt, which is higher than most of the reported non-Pt DSSC cathodes. Attractively, LaFeO2.965−δCl0.035 displays superior IRR activity/stability and structural stability in the I3–/I–-based electrolyte compared to pristine LaFeO3 because chlorine doping facilitates the creation of oxygen vacancies (active sites) and enhances surface acidity simultaneously. This study provides a new way for designing outstanding IRR electrocatalysts, which could be applied to many redox couple-mediated photo/electrochemical devices.
Recommended citation: Wang, W., Tran, R., Qu, J., Liu, Y., Chen, C., Xu, M., Chen, Y., Ong, S. P., Wang, L., Zhou, W., & Shao, Z. (2019). Chlorine-Doped Perovskite Oxide: A Platinum-Free Cathode for Dye-Sensitized Solar Cells [Research-article]. ACS Applied Materials and Interfaces, 11(39), 35641–35652. https://doi.org/10.1021/acsami.9b07966
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Grain boundary properties of elemental metals
Published in Acta Materialia, 2020
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the Σ7 [0001] twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of ~ 3–6. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represents a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.
Recommended citation: Zheng, H., Li, X. G., Tran, R., Chen, C., Horton, M., Winston, D., Persson, K. A., & Ong, S. P. (2020). Grain boundary properties of elemental metals. Acta Materialia, 186, 40–49. https://doi.org/10.1016/j.actamat.2019.12.030
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The Breakdown of Mott Physics at VO2 Surfaces
Published in pre-print (arXiv), 2020
Transition metal oxides such as vanadium dioxide (VO2), niobium dioxide (NbO2), and titanium sesquioxide (Ti2O3) are known to undergo a temperature-dependent metal-insulator transition (MIT) in conjunction with a structural transition within their bulk. However, it is not typically discussed how breaking crystal symmetry via surface termination affects the complicated MIT physics. Using synchrotron-based x-ray spectroscopy, low energy electron diffraction (LEED), low energy electron microscopy (LEEM), transmission electron microscopy (TEM), and several other experimental techniques, we show that suppression of the bulk structural transition is a common feature at VO2 surfaces. Our density functional theory (DFT) calculations further suggest that this is due to inherent reconstructions necessary to stabilize the surface, which deviate the electronic structure away from the bulk d1 configuration. Our findings have broader ramifications not only for the characterization of other “Mott-like” MITs, but also for any potential device applications of such materials.
Recommended citation: Wahila, M. J., Quackenbush, N. F., Sadowski, J. T., Krisponeit, J. O., Flege, J. I., Tran, R., Ong, S. P., Schlueter, C., Lee, T. L., Holtz, M. E., Muller, D. A., Paik, H., Schlom, D. G., Lee, W. C., & Piper, L. F. J. (2020). The breakdown of Mott physics at VO2 surfaces. ArXiv, 1–9.
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Inherent stochasticity during insulator–metal transition in VO2
Published in Proceedings of the National Academy of Sciences of the United States of America, 2021
Vanadium dioxide (VO2), which exhibits a near-room-temperature insulator–metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO2 at local areas in planar VO2/TiO2 device configuration under external biasing. After each resistive switching, different VO2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO2 as the key material of neuromorphic computing.
Recommended citation: Cheng, S., Lee, M. H., Tran, R., Shi, Y., Li, X., Navarro, H., Adda, C., Meng, Q., Chen, L. Q., Dynes, R. C., Ong, S. P., Schuller, I. K., & Zhu, Y. (2021). Inherent stochasticity during insulator-metal transition in VO2. Proceedings of the National Academy of Sciences of the United States of America, 118(37). https://doi.org/10.1073/pnas.2105895118
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Proton distribution visualization in perovskite nickelate devices utilizing nanofocused x rays
Published in Physical Review Materials, 2021
We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO3-based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-functional theory simulations show partial reduction of nickel valence in the region with high proton concentration, which leads to the insulating behavior. Concurrently, x-ray diffraction reveals only a small lattice distortion in the doped regions. Together, our results directly show that the knob which proton doping modifies is the electronic valency and not the crystal lattice. The studies are relevant to ongoing efforts to disentangle structural and electronic effects across metal-insulator phase transitions in correlated oxides.
Recommended citation: Zaluzhnyy, I. A., Sprau, P. O., Tran, R., Wang, Q., Zhang, H. T., Zhang, Z., Park, T. J., Hua, N., Stoychev, B., Cherukara, M. J., Holt, M. V., Nazaretski, E., Huang, X., Yan, H., Pattammattel, A., Chu, Y. S., Ong, S. P., Ramanathan, S., Shpyrko, O. G., & Frano, A. (2021). Proton distribution visualization in perovskite nickelate devices utilizing nanofocused x rays. Physical Review Materials, 5(9), 1–8. https://doi.org/10.1103/PhysRevMaterials.5.095003
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Screening of bimetallic electrocatalysts for water purification with machine learning
Published in The Journal of Chemical Physics, 2022
Electrocatalysis provides a potential solution to NO3− pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO3− reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.
Recommended citation: Tran, R., Wang, D., Kingsbury, R., Palizhati, A., Persson, K. A., Jain, A., & Ulissi, Z. W. (2022). Screening of bimetallic electrocatalysts for water purification with machine learning. The Journal of Chemical Physics, 157(7), 074102. https://doi.org/10.1063/5.0092948
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All-Electric Nonassociative Learning in Nickel Oxide
Published in Advanced Intelligent Systems, 2022
Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.
Recommended citation: Mondal, S., Zhang, Z., Islam, A. N. M. N., Andrawis, R., Gamage, S., Aghamiri, N. A., Wang, Q., Zhou, H., Rodolakis, F., Tran, R., Kaur, J., Chen, C., Ong, S. P., Sengupta, A., Abate, Y., Roy, K., & Ramanathan, S. (2022). All‐Electric Nonassociative Learning in Nickel Oxide. Advanced Intelligent Systems, 4(10). https://doi.org/10.1002/aisy.202200069
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Metal-insulator transition in V2O3 with intrinsic defects
Published in Physical Review B, 2021
Vanadium sesquioxide (V2O3) is a Mott insulator exhibiting a temperature-dependent metal-insulator transition (MIT) at 165 K accompanied by both a magnetic and structural transition. Although it is expected to be a metal under conventional band theory, electron interactions at low temperature cause it to behave like an insulator, making it difficult to accurately model its electronic properties with standard ab initio methods. As such, accurate theoretical assessments of the MIT with point defects requires special attention to the type of functionals used. In this study, we conclude that the PBE+𝑈 functional provides the best compromise between accuracy and efficiency in calculating the properties related to the MIT between low-temperature and high-temperature V2O3. We use this functional to explore the various influences that intrinsic point defects will have on the MIT in V2O3.
Recommended citation: Tran, R., Li, X. G., Ong, S. P., Kalcheim, Y., & Schuller, I. K. (2021). Metal-insulator transition in V2O3 with intrinsic defects. Physical Review B, 103(7), 1–7. https://doi.org/10.1103/PhysRevB.103.075134
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Morphology Control of Tantalum Carbide Nanoparticles through Dopant Additions
Published in The Journal of Physical Chemistry C, 2021
The control of powder morphology in metals and ceramics is of critical importance in applications such as catalysis and chemical sensing whereby specific crystal facets better facilitate chemical reactions. In response to this challenge, we present a combined experimental and computational approach that examines the principles behind dopant-induced crystallographic faceting in nanoparticles. We base our study on nanoparticles of tantalum carbide doped with nickel, iron, cobalt, niobium, and titanium and observe a very significant transition from round/irregular particle shapes to cubes and cuboctahedrons upon the addition of transition metal dopants. The presence of the dopants, which segregate toward the surface of the particles, results in atomic orbital hybridization, causing a significant decrease of up to 0.13 eV·Å–2 in the surface energy of the (100) facets, thus providing the driving force for the formation of nanocubes with exposed (100) surfaces. These principles can be generalized to other ceramics and serve as guidance for the optimized control of shape in powders. For example, if one seeks to produce highly faceted V-, Hf-, or Zr-carbide nanoparticles, doping strategies reported here can be applied. Other elements may also be effective in changing the growth habits of crystals based on surface segregation and dopant–host atomic orbital hybridization.
Recommended citation: Ren, T., Tran, R., Lee, S., Bandera, A., Herrera, M., Li, X.-G., Ong, S. P., & Graeve, O. A. (2021). Morphology Control of Tantalum Carbide Nanoparticles through Dopant Additions. The Journal of Physical Chemistry C, 125(19), 10665–10675. https://doi.org/10.1021/acs.jpcc.1c01387
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The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Published in ACS Catalysis, 2023
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of Oxygen Evolution Reaction (OER) catalysts. To address this, we developed the Open Catalyst 2022 (OC22) dataset, consisting of 62,331 Density Functional Theory (DFT) relaxations (∼9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide predefined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ∼36% improvement in energy predictions when combining the chemically dissimilar Open Catalyst 2020 Data set (OC20) and OC22 datasets via fine-tuning. Similarly, we achieved a ∼19% improvement in total energy predictions on OC20 and a ∼9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. Data set and baseline models are open sourced, and a public leaderboard is available to encourage continued community developments on the total energy tasks and data.
Recommended citation: Tran, R., Lan, J., Shuaibi, M., Wood, B. M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., Sriram, A., Therrien, F., Abed, J., Voznyy, O., Sargent, E. H., Ulissi, Z., & Zitnick, C. L. (2022). The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS Catalysis, 13(February), 3066–3084. https://doi.org/10.1021/acscatal.2c05426
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Rational design of nanoscale stabilized oxide catalysts for OER with OC22
Published in Nanoscale, 2024
The efficiency of H2 production via water electrolysis is limited by the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) framework has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction con- ditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime, respectively.
Recommended citation: Tran, R., Huang, L., Zi, Y., Wang, S., Comer, B. M., Wu, X., Raaijman, S. J., Sinha, N. K., Sadasivan, S., Thundiyil, S., Mamtani, K. B., Iyer, G., Grabow, L. C., Lu, L., & Chen, J. (2024). Rational design of nanoscale stabilized oxide catalysts for OER with OC22. Nanoscale. https://doi.org/10.1039/d4nr01390e
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NANO 106 - Crystallography of Materials
Undergraduate course, University of California, San Diego, 2018
Fundamentals of crystallography, and practice of methods to study material structure and symmetry. Curie symmetries. Tensors as mathematical description of material properties and symmetry restrictions. Introduction to diffraction methods, including X-ray, neutron, and electron diffraction. Close-packed and other common structures of real-world materials. Derivative and superlattice structures.