Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

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Machine learning in materials informatics: recent applications and prospects

Quantum-dot-sensitized solar cells (QDSCs) are a promising low-cost alternative to existing photovoltaic technologies such as crystalline silicon and thin inorganic films The absorption spectrum of quantum dots (QDs) can be tailored by controlling their size, and QDs can be produced by low-cost methods Nanostructures such as mesoporous films, nanorods, nanowires, nanotubes and nanosheets with high microscopic surface area, redox electrolytes and solid-state hole conductors are borrowed from standard dye-sensitized solar cells (DSCs) to fabricate electron conductor/QD monolayer/hole conductor junctions with high optical absorbance Herein we focus on recent developments in the field of mono- and polydisperse QDSCs Stability issues are adressed, coating methods are presented, performance is reviewed and special emphasis is given to the importance of energy-level alignment to increase the light to electric power conversion efficiency

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Quantum‐Dot‐Sensitized Solar Cells

Group-III-nitride semiconductors have shown enormous potential as light sources for full-colour displays, optical storage and solid-state lighting. Remarkably, InGaN blue- and green-light-emitting diodes (LEDs) emit brilliant light although the threading dislocation density generated due to lattice mismatch is six orders of magnitude higher than that in conventional LEDs. Here we explain why In-containing (Al,In,Ga)N bulk films exhibit a defect-insensitive emission probability. From the extremely short positron diffusion lengths (<4 nm) and short radiative lifetimes of excitonic emissions, we conclude that localizing valence states associated with atomic condensates of In-N preferentially capture holes, which have a positive charge similar to positrons. The holes form localized excitons to emit the light, although some of the excitons recombine at non-radiative centres. The enterprising use of atomically inhomogeneous crystals is proposed for future innovation in light emitters even when using defective crystals.

Origin of defect-insensitive emission probability in In-containing (Al,In,Ga)N alloy semiconductors.

Through the use of photoelectron spectroscopy in air (PESA), we investigate the size-dependent valence and conduction band-edge energies of CdSe, CdTe, PbS, and PbSe semiconductor quantum dots (QDs). The results are compared to those of previous studies, based on differing experimental methods, and to theoretical calculations based on k·p theory and state-of-the-art atomistic semiempirical pseudopotential modeling. To accurately map out the energy level landscapes of QDs as a function of size, the QDs must be passivated by comparable surface chemistries. This is highlighted by studying the effect of surface chemistry on the valence band-edge energy in an ensemble of 4.7 nm CdSe QDs. An energy level shift as large as 0.35 eV is observed for this system through modification of surface chemistry alone. This shift is significantly larger than the size-dependent valence band-edge shift that is observed when comparable surface chemistries are used.

Size-dependent valence and conduction band-edge energies of semiconductor nanocrystals.

PbSe is a pseudo-II-VI material distinguished from ordinary II-VI's (e.g., CdSe, ZnSe) by having both its valence band maximum (VBM) and its conduction band minimum (CBM) located at the fourfold-degenerate L-point in the Brillouin zone. It turns out that this feature dramatically affects the properties of the nanosystem. We have calculated the electronic and optical properties of PbSe quantum dots using an atomistic pseudopotential method, finding that the electronic structure is different from that of ordinary II-VI's and, at the same time, is more subtle than what k·p or tight-binding calculations have suggested previously for PbSe. We find the following in PbSe dots:  (i) The intraband (valence-to-valence and conduction-to-conduction) as well as interband (valence-to-conduction) excitations involve the massively split L-manifold states. (ii) In contrast to previous suggestions that the spacings between valence band levels will equal those between conduction band levels (because the corresponding effect...

The peculiar electronic structure of PbSe quantum dots.

The Peculiar electronic structure of PbSe quantum dots

The ability to artificially grow different configurations of semiconductor alloys---random structures, spontaneously ordered and layered superlattices---raises the issue of how different alloy configurations may lead to new and different alloy physical properties. We address this question in the context of nitrogen impurities in GaP, which form deep levels in the gap whose energy and optical absorption sensitively depend on configuration. We use the ``inverse band structure'' approach in which we first specify a desired target physical property (such as the deepest nitrogen level, or lowest strain configuration), and then we search, via genetic algorithm, for the alloy atomic configurations that have this property. We discover the essential structural motifs leading to such target properties. This strategy opens the way to efficient alloy design.

Searching for alloy configurations with target physical properties: impurity design via a genetic algorithm inverse band structure approach.

The transition temperature ${T}_{C}$ of multicomponent systems---ferromagnetic, superconducting, or ferroelectric---depends strongly on the atomic arrangement, but an exhaustive search of all configurations for those that optimize ${T}_{C}$ is difficult, due to the astronomically large number of possibilities. Here we address this problem by parametrizing the ${T}_{C}$ of a set of $\ensuremath{\sim}50$ input configurations, calculated from first principles, in terms of configuration variables (``cluster expansion''). Once established, this expansion allows us to search almost effortlessly the transition temperature of arbitrary configurations. We apply this approach to search for the configuration of Mn dopants in GaAs having the highest ferromagnetic Curie temperature. Our general approach of cluster expanding physical properties opens the way to design based on exploring a large space of configurations.

First-principles combinatorial design of transition temperatures in multicomponent systems: the case of Mn in GaAs.

The effect of 1:1 long range order on optical properties of ${\mathrm{Al}}_{x}{\mathrm{Ga}}_{1\ensuremath{-}x}\mathrm{N}$ alloys is investigated by means of first-principles calculations combined with large-scale atomistic empirical-pseudopotential simulations. We propose an intra-band mechanism of ordering-induced band gap reduction for different optical polarization. The scaling of band gap reductions with order parameter is analyzed. Our simulations of inhomogeneous ordering suggest that coexistence of ordered and random domains may explain the large magnitude of the observed redshifts upon ordering.

S. V. Dudiy

Papers

The peculiar electronic structure of PbSe quantum dots.

The Peculiar electronic structure of PbSe quantum dots

Searching for alloy configurations with target physical properties: impurity design via a genetic algorithm inverse band structure approach.

First-principles combinatorial design of transition temperatures in multicomponent systems: the case of Mn in GaAs.

Optical consequences of long-range order in wurtzite Al x Ga 1 − x N alloys