The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial ...raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.
Abstract
Sub-nano metal clusters often exhibit unique and unexpected properties, which make them particularly attractive as catalysts. Herein, we report a “precursor-preselected” wet-chemistry ...strategy to synthesize highly dispersed Fe
2
clusters that are supported on mesoporous carbon nitride (mpg-C
3
N
4
). The obtained Fe
2
/mpg-C
3
N
4
sample exhibits superior catalytic performance for the epoxidation of
trans
-stilbene to
trans
-stilbene oxide, showing outstanding selectivity of 93% at high conversion of 91%. Molecular oxygen is the only oxidant and no aldehyde is used as co-reagent. Under the same condition, by contrast, iron porphyrin, single-atom Fe, and small Fe nanoparticles (ca. 3 nm) are nearly reactively inert. First-principles calculations reveal that the unique reactivity of the Fe
2
clusters originates from the formation of active oxygen species. The general applicability of the synthesis approach is further demonstrated by producing other diatomic clusters like Pd
2
and Ir
2
, which lays the foundation for discovering diatomic cluster catalysts.
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is ...not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.
Abstract
A main goal of data-driven materials research is to find optimal low-dimensional descriptors, allowing us to predict a physical property, and to interpret them in a human-understandable way. ...In this work, we advance methods to identify descriptors out of a large pool of candidate features by means of compressed sensing. To this extent, we develop schemes for engineering appropriate candidate features that are based on simple basic properties of building blocks that constitute the materials and that are able to represent a multi-component system by scalar numbers. Cross-validation based feature-selection methods are developed for identifying the most relevant features, thereby focusing on high generalizability. We apply our approaches to an
ab initio
dataset of ternary group-IV compounds to obtain a set of descriptors for predicting lattice constants and energies of mixing. In particular, we introduce simple complexity measures in terms of involved algebraic operations as well as the amount of utilized basic properties.
Abstract
Two-dimensional electron gases (2DEG), arising due to quantum confinement at interfaces between transparent conducting oxides, have received tremendous attention in view of electronic ...applications. Here, we explore the potential of interfaces formed by two lattice-matched wide-gap oxides of emerging interest, i.e., the polar, orthorhombic perovskite LaInO
3
and the nonpolar, cubic perovskite BaSnO
3
, employing first-principles approaches. We find that the polar discontinuity at the interface is mainly compensated by electronic relaxation through charge transfer from the LaInO
3
to the BaSnO
3
side. This leads to the formation of a 2DEG hosted by the highly dispersive Sn-
s
-derived conduction band and a 2D hole gas of O-
p
character, strongly localized inside LaInO
3
. We rationalize how polar distortions, termination, thickness, and dimensionality of the system (periodic or non-periodic) can be exploited in view of tailoring the 2DEG characteristics, and why this material is superior to the most studied prototype LaAlO
3
/SrTiO
3
.
Identifying similar materials (i.e., those sharing a certain property or feature) requires interoperable data of high quality. It also requires means to measure similarity. We demonstrate how a ...spectral fingerprint as a descriptor, combined with a similarity metric, can be used for establishing quantitative relationships between materials data, thereby serving multiple purposes. This concerns, for instance, the identification of materials exhibiting electronic properties similar to a chosen one. The same approach can be used for assessing uncertainty in data that potentially come from different sources. Selected examples show how to quantify differences between measured optical spectra or the impact of methodology and computational parameters on calculated properties, like the density of states or excitonic spectra. Moreover, combining the same fingerprint with a clustering approach allows us to explore materials spaces in view of finding (un)expected trends or patterns. In all cases, we provide physical reasoning behind the findings of the automatized assessment of data.
Impact statement
To predict novel materials with desired properties, data-centric approaches are in the process of becoming an additional fundament of materials research. Prerequisite for their success are well-curated data. Ideally, one can make use of multiple data collections. Bringing data from different sources together, poses challenges on their interoperability which are routed in two out of the 4V of Big Data. These are the uncertainty of data quality (veracity) and the heterogeneity in form and meaning of the data (variety). To overcome this barrier, universal and interpretable measures must be established, which quantify differences between data that are supposed to have the same meaning. Here, we show how a spectral fingerprint in combination with a similarity metric can be used for assessing spectral properties of materials. Our approach allows for tracing back in computed as well as measured data, differences stemming from various aspects. It thus paves the way for automatized data-quality assessment toward interoperability. Based on this, in turn, materials exhibiting similar features can be identified.
Graphical abstract
Abstract
We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate the similarity of materials based on it. As an application example, we study the ...Computational 2D Materials Database (C2DB) that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We introduce additional descriptors to characterize these clusters in terms of crystal structures, atomic compositions, and electronic configurations of their members. This allows us to rationalize the found (dis)similarities and to perform an automated exploratory and confirmatory analysis of the C2DB data. From this analysis, we find that the majority of clusters consist of isoelectronic materials sharing crystal symmetry, but we also identify outliers,
i.e
., materials whose similarity cannot be explained in this way.
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