Shyam Dwaraknath

Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection

With the ultimate goal of finding new polymorphs through targeted synthesis conditions and techniques, we outline a computational framework to select optimal substrates for epitaxial growth using first principle calculations of formation energies, elastic strain energy, and topological information. To demonstrate the approach, we study the stabilization of metastable VO2 compounds which provides a rich chemical and structural polymorph space. We find that common polymorph statistics, lattice matching, and energy above hull considerations recommends homostructural growth on TiO2 substrates, where the VO2 brookite phase would be preferentially grown on the a-c TiO2 brookite plane while the columbite and anatase structures favor the a-b plane on the respective TiO2 phases. Overall, we find that a model which incorporates a geometric unit cell area matching between the substrate and the target film as well as the resulting strain energy density of the film provide qualitative agreement with experimental observations for the heterostructural growth of known VO2 polymorphs: rutile, A and B phases. The minimal interfacial geometry matching and estimated strain energy criteria provide several suggestions for substrates and substrate-film orientations for the heterostructural growth of the hitherto hypothetical anatase, brookite, and columbite polymorphs. These criteria serve as a preliminary guidance for the experimental efforts stabilizing new materials and/or polymorphs through epitaxy. The current screening algorithm is being integrated within the Materials Project online framework and data and hence publicly available.

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.

Thermodynamic limit for synthesis of metastable inorganic materials

Amorphous forms serve as thermodynamic upper bounds on the free energy scale for synthesis of metastable crystalline polymorphs. Realizing the growing number of possible or hypothesized metastable crystalline materials is extremely challenging. There is no rigorous metric to identify which compounds can or cannot be synthesized. We present a thermodynamic upper limit on the energy scale, above which the laboratory synthesis of a polymorph is highly unlikely. The limit is defined on the basis of the amorphous state, and we validate its utility by effectively classifying more than 700 polymorphs in 41 common inorganic material systems in the Materials Project for synthesizability. The amorphous limit is highly chemistry-dependent and is found to be in complete agreement with our knowledge of existing polymorphs in these 41 systems, whether made by the nature or in a laboratory. Quantifying the limits of metastability for realizable compounds, the approach is expected to find major applications in materials discovery.

High-throughput density-functional perturbation theory phonons for inorganic materials

The knowledge of the vibrational properties of a material is of key importance to understand physical phenomena such as thermal conductivity, superconductivity, and ferroelectricity among others. However, detailed experimental phonon spectra are available only for a limited number of materials, which hinders the large-scale analysis of vibrational properties and their derived quantities. In this work, we perform ab initio calculations of the full phonon dispersion and vibrational density of states for 1521 semiconductor compounds in the harmonic approximation based on density functional perturbation theory. The data is collected along with derived dielectric and thermodynamic properties. We present the procedure used to obtain the results, the details of the provided database and a validation based on the comparison with experimental data.

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state (EOS) for crystalline solids describe material behaviors under changes in pressure, volume, entropy and temperature, making them fundamental to scientific research in a wide range of fields including geophysics, energy storage and development of novel materials. Despite over a century of theoretical development and experimental testing of energy–volume (E–V) EOS for solids, there is still a lack of consensus with regard to which equation is indeed optimal, as well as to what metric is most appropriate for making this judgment. In this study, several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature. Our findings do not indicate a clear “best” EOS, but we identify three which consistently perform well relative to the rest of the set. Furthermore, we find that for the aggregate data set, the RMSrD is not strongly correlated with the nature of the compound, e.g., whether it is a metal, insulator, or semiconductor, nor the bulk modulus for any of the EOS, indicating that a single equation can be used across a broad range of classes of materials.Equations of State: which are best?A systematic comparison between the performances of several thermodynamic equations of state revealed the superiority of three equations. Equations of state are widely used to describe materials properties based on variables like temperature, pressure, volume, etc. Now, a team from University of California Berkeley and the Lawrence Berkeley National Lab aim to determine the most suitable one for various conditions. The authors used DFT calculations to model the properties of hundreds of elemental, binary and ternary crystalline solids and subsequently fit them with the most commonly-used equations of state. The Birch, Tait and Vinet equations showed the lowest deviation from calculated points, while fitting reasonably well experimental data; this holistic approach underlines that there is not one equation of state to fit all cases.

The Materials Project: Accelerating Materials Design Through Theory-Driven Data and Tools

The Materials Project (MP) is a community resource for theory-based data, web-based materials analysis tools, and software for performing and analyzing calculations. The MP database includes a variety of computed properties such as crystal structure, energy, electronic band structure, and elastic tensors for tens of thousands of inorganic compounds. At the time of writing, over 40,000 users have registered for the MP database. These users interact with this data either through the MP web site (https://www.materialsproject.org) or through a REpresentational State Transfer (REST) application programming interface (API). MP also develops or contributes to several open-source software libraries to help set up, automate, analyze, and extract insight from calculation results. Furthermore, MP is developing tools to help researchers share their data (both computational and experimental) through its platform. The ultimate goal of these efforts is to accelerate materials design and education by providing new data and software tools to the research community. In this chapter, we review the history, theoretical methods, impact (including user-led research studies), and future goals for the Materials Project. 1 History and Overview of the Materials Project Materials scientists and engineers have always depended on materials property data to inform, guide, and explain research and development. Traditionally, such data originated almost solely from experimental studies. In the past 10–15 years, it has become possible to rapidly generate reliable materials data using scalable computer simulations of the fundamental equations of physics such as the Schrödinger equation. This paradigm shift was induced by a combination of theoretical advances, most notably the development of density functional theory (DFT), algorithmic improvements, and low-cost computing. The Materials Project (MP, or “The Project”) was founded in 2011 as a collaborative effort to leverage ongoing advances in theory and computing to accelerate the research and design of new materials. The Project rests on a comprehensive database of predicted properties of materials that is the result of executing millions of DFT simulations on supercomputing resources. At the time of writing, this database includes >69,000 inorganic materials with crystal structures and total energies, >57,000 materials with electronic band structures, >48,000 with electronic transport properties (Fig. 1) (Ricci et al. 2017), >30,000 with XANES The Materials Project: Accelerating Materials Design Through Theory-Driven. . . 3 Fig. 1 Example of a large electronic transport data set in MP generated through computations. Each point represents one compound, with Seebeck coefficient versus electron conductivity (divided by τ ) plotted. The color represents the thermoelectric power factor (S2σ ), and the point size is proportional to the bandgap (Ricci et al. 2017). This data set is available through the MPContribs platform (see Section 6.2) at: https://materialsproject.org/mpcontribs/boltztrap k-edge spectra (Dozier et al. 2017), >15,000 with conversion battery properties, >6000 with elastic tensors (de Jong et al. 2015a), >3,000 with intercalation battery properties, >1,000 with piezoelectric tensors (de Jong et al. 2015b), >1,000 with dielectric tensors (Petousis et al. 2017), and > 1000 elemental surface energies (Tran et al. 2016). This database is continually expanding with more materials and more properties (see Fig. 2 for an example of properties listed in the current iteration). The Project launched its publicly accessible web site in October 2011 and has since grown into a multi-institution collaboration as part of the US Department of Energy Office of Basic Energy Sciences (BES). The web site provides access to the database as well as applications (or “apps”) that combine and visually present the data for specific analyses such as phase diagram generation or battery electrode evaluation. The MP web site hosts more than 40,000 registered users worldwide consisting of a diverse set of researchers and students from academia, industry, and educational institutions (Figs. 3 and 4). The diversity of the audience base highlights the usefulness of a theory-based materials database across the spectrum of education, research, and development activities. Apart from the core data and web site, MP helps develop and maintain a set of open-source software libraries for setting up, executing, analyzing, and deriving

Theory-Guided Synthesis of a Metastable Lead-Free Piezoelectric Polymorph

Many technologically critical materials are metastable under ambient conditions, yet the understanding of how to rationally design and guide the synthesis of these materials is limited. This work presents an integrated approach that targets a metastable lead-free piezoelectric polymorph of SrHfO3 . First-principles calculations predict that the previous experimentally unrealized, metastable P4mm phase of SrHfO3 should exhibit a direct piezoelectric response (d33 ) of 36.9 pC N-1 (compared to d33 = 0 for the ground state). Combining computationally optimized substrate selection and synthesis conditions lead to the epitaxial stabilization of the polar P4mm phase of SrHfO3 on SrTiO3 . The films are structurally consistent with the theory predictions. A ferroelectric-induced large signal effective converse piezoelectric response of 5.2 pm V-1 for a 35 nm film is observed, indicating the ability to predict and target multifunctionality. This illustrates a coupled theory-experimental approach to the discovery and realization of new multifunctional polymorphs.

A Multi-Pinhole Faraday Cup Device for Measurement of Discrete Charge Distribution of Heavy and Light Ions

A new multi-pinhole Faraday cup (MPFC) device was designed, fabricated and tested to measure ion beam uniformity over a range of centimeters. There are 32 collectors within the device, and each of those is used as an individual Faraday cup to measure a fraction of the beam current. Experimental data show that the device is capable of measuring a charged particles distribution - either in the form of a raster scanned focused beam or a defocused beam.