Session Z18: Invited Session: Development of Computational Techniques for Accelerated Materials Development

Structuring intuition with theory: The high-throughput way

First principles methodologies have grown in accuracy and applicability to the point where large databases can be built, shared, and analyzed with the goal of predicting novel compositions, optimizing functional properties, and discovering unexpected relationships between the data. In order to be useful to a large community of users, data should be standardized, validated, and distributed. In addition, tools to easily manage large datasets should be made available to effectively lead to materials development. Within the AFLOW consortium we have developed a simple frame to expand, validate, and mine data repositories: the MTFrame. Our minimalistic approach complement AFLOW and other existing high-throughput infrastructures and aims to integrate data generation with data analysis. We present few examples from our work on materials for energy conversion. Our intent s to pinpoint the usefulness of high-throughput methodologies to guide the discovery process by quantitatively structuring the scientific intuition.   

The magnetic genome project

Magnetic materials underpin a vast and diverse range of modern technologies, going from data storage to energy production and use. However, the choice of magnets for mainstream applications is limited to a few dozens and the development of a new high-performance magnetic compound is a long and often unpredictable process. Here we describe a systematic pathway to the discovery of novel magnetic materials for multiple applications, which demonstrates an unprecedented throughput and speed up in the discovery process. We have constructed a massive electronic structures library for Heusler alloys containing 236,856 materials. We have then extracted those magnetic compounds with specific electronic properties, such as half-metallicity and large magnetization density, and finally established whether these can be fabricated at thermodynamical equilibrium. Based on our analysis we have identified 249 stable new intermetallic Heuslers, including 21 new magnets. Our work paves the way for large scale design of novel magnetic materials at unprecedented speed.   

Distributed databases for materials study of thermo-kinetic properties

High-throughput computational materials science provides researchers with the opportunity to rapidly generate large databases of materials properties. To rapidly add thermal properties to the AFLOWLIB consortium [1, 2, 3, 4] and Materials Project repositories [5], we have implemented an automated quasi-harmonic Debye model, the Automatic GIBBS Library (AGL) [6, 7]. This enables us to screen thousands of materials for thermal conductivity, bulk modulus, thermal expansion and related properties. The search and sort functions of the online database can then be used to identify suitable materials for more in-depth study using more precise computational or experimental techniques. AFLOW-AGL source code is public domain and will soon be released within the GNU-GPL license. \\ $\left[1\right]$ S. Curtarolo et al., Comp. Mat. Sci. \textbf{58}, 218 (2012). \\ $\left[2\right]$ S. Curtarolo et al., Comp. Mat. Sci, \textbf{58}, 227 (2012). \\ $\left[3\right]$ www.aflowlib.org \\ $\left[4\right]$ R. H. Taylor, F. Rose, C. Toher, O. Levy, K. Yang, M. Buongiorno Nardelli and S. Curtarolo, Comp. Mat. Sci. \textbf{93}, 178 (2014). \\ $\left[5\right]$ A. Jain et al., APL Mater. \textbf{1}, 011002 (2013). \\ $\left[6\right]$ C. Toher, J. J. Plata, O. Levy, M. de Jong, M. Asta, M. Buongiorno Nardelli and S. Curtarolo, Phys. Rev. B \textbf{90}, 174107 (2014). \\ $\left[7\right]$ M. A. Blanco, E. Francisco and V. Lua{\~n}a, Comput. Phys. Comm. \textbf{158}, 57 (2004).   

High-throughput evaluation of descriptors for thermoelectric materials

Achieving optimal carrier and minimal thermal conductivity is necessary for a given material to be suitable for thermoelectric energy conversion. Both properties are computationally too demanding for brute force approaches which demands that simplified descriptors are developed. Based on the recent computational discovery of favorable thermoelectric performance in the commercially viable an environmentally friendly Ag:SnS [1], we discuss how doping limits can be computationally screened. We will discuss the effects of two ubiquitous effects that can result in decreasing the hole concentration and show how the surprising results of Li doping can be rationalized based on data made available through on-line repositories. Furthermore, we show how the lattice thermal conductivity can be rapidly and reliably screened based on the quasi harmonic approximation [2]. We contrast this to the information covered by the available phase space for three-phonon scattering processes. \\[4pt] [1] Bera et al Phys. Chem. Chem. Phys.p19894, 16, 2014\\[0pt] [2] Bjerg et al Phys. Rev. B., p024304,89,2014   

Predicting lattice thermal conductivity with help from \textit{ab initio} methods

The lattice thermal conductivity is a fundamental transport parameter that determines the utility a material for specific thermal management applications. Materials with low thermal conductivity find applicability in thermoelectric cooling and energy harvesting. High thermal conductivity materials are urgently needed to help address the ever-growing heat dissipation problem in microelectronic devices. Predictive computational approaches can provide critical guidance in the search and development of new materials for such applications. \textit{Ab initio} methods for calculating lattice thermal conductivity [1] have demonstrated predictive capability, but while they are becoming increasingly efficient [2], they are still computationally expensive particularly for complex crystals with large unit cells . In this talk, I will review our work on first principles phonon transport for which the intrinsic lattice thermal conductivity is limited only by phonon-phonon scattering arising from anharmonicity. I will examine use of the phase space for anharmonic phonon scattering and the Gr\"{u}neisen parameters as measures of the thermal conductivities for a range of materials and compare these to the widely used guidelines stemming from the theory of Liebfried and Sch\"{o}lmann [3]. [1] D. A. Broido, M. Malorny, G. Birner, N. Mingo, and D. A. Stewart, Appl. Phys. Lett. 91, 231922 (2007); [2] Wu Li, J. Carrete, N. A. Katcho, and N. Mingo, Comp. Phys. Comm. 185, 1747 (2014); [3] G. Leibfried and E. Schl\"{o}mann, Nach. Akad. Wiss. Gottingen,Math. Phyz. Klasse \textbf{4}, 71 (1954).