27 research outputs found
SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
The lack of reliable methods for identifying descriptors - the sets of
parameters capturing the underlying mechanisms of a materials property - is one
of the key factors hindering efficient materials development. Here, we propose
a systematic approach for discovering descriptors for materials properties,
within the framework of compressed-sensing based dimensionality reduction.
SISSO (sure independence screening and sparsifying operator) tackles immense
and correlated features spaces, and converges to the optimal solution from a
combination of features relevant to the materials' property of interest. In
addition, SISSO gives stable results also with small training sets. The
methodology is benchmarked with the quantitative prediction of the ground-state
enthalpies of octet binary materials (using ab initio data) and applied to the
showcase example of predicting the metal/insulator classification of binaries
(with experimental data). Accurate, predictive models are found in both cases.
For the metal-insulator classification model, the predictive capability are
tested beyond the training data: It rediscovers the available pressure-induced
insulator->metal transitions and it allows for the prediction of yet unknown
transition candidates, ripe for experimental validation. As a step forward with
respect to previous model-identification methods, SISSO can become an effective
tool for automatic materials development.Comment: 11 pages, 5 figures, in press in Phys. Rev. Material
Learning physical descriptors for materials science by compressed sensing
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
Analysis of Topological Transitions in Two-dimensional Materials by Compressed Sensing
Quantum spin-Hall insulators (QSHIs), i.e., two-dimensional topological
insulators (TIs) with a symmetry-protected band inversion, have attracted
considerable scientific interest in recent years. In this work, we have
computed the topological Z2 invariant for 220 functionalized honeycomb lattices
that are isoelectronic to functionalized graphene. Besides confirming the TI
character of well-known materials such as functionalized stanene, our study
identifies 45 yet unreported QSHIs. We applied a compressed-sensing approach to
identify a physically meaningful descriptor for the Z2 invariant that only
depends on the properties of the material's constituent atoms. This enables us
to draw a map of materials, in which metals, trivial insulators, and QSHI form
distinct regions. This analysis yields fundamental insights in the mechanisms
driving topological transitions. The transferability of the identified model is
explicitly demonstrated for an additional set of honeycomb lattices with
different functionalizations that are not part of the original set of 220
graphene-type materials used to identify the descriptor. In this class, we
predict 74 more novel QSHIs that have not been reported in literature yet
Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites
Significant advances have been made in predicting new topological materials
using high-throughput empirical descriptors or symmetry-based indicators. To
date, these approaches have been applied to materials in existing databases,
and are severely limited to systems with well-defined symmetries, leaving a
much larger materials space unexplored. Using tetradymites as a prototypical
class of examples, we uncover a novel two-dimensional descriptor by applying an
artificial intelligence (AI) based approach for fast and reliable
identification of the topological characters of a drastically expanded range of
materials, without prior determination of their specific symmetries and
detailed band structures. By leveraging this descriptor that contains only the
atomic number and electronegativity of the constituent species, we have readily
scanned a huge number of alloys in the tetradymite family. Strikingly, nearly
half of which are identified to be topological insulators, revealing a much
larger territory of the topological materials world. The present work also
attests the increasingly important role of such AI-based approaches in modern
materials discovery
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Predicting the stability of the perovskite structure remains a longstanding
challenge for the discovery of new functional materials for many applications
including photovoltaics and electrocatalysts. We developed an accurate,
physically interpretable, and one-dimensional tolerance factor, {\tau}, that
correctly predicts 92% of compounds as perovskite or nonperovskite for an
experimental dataset of 576 materials ( , ,
, , ) using a novel data analytics approach based on SISSO
(sure independence screening and sparsifying operator). {\tau} is shown to
generalize outside the training set for 1,034 experimentally realized single
and double perovskites (91% accuracy) and is applied to identify 23,314 new
double perovskites () ranked by their probability of
being stable as perovskite. This work guides experimentalists and theorists
towards which perovskites are most likely to be successfully synthesized and
demonstrates an approach to descriptor identification that can be extended to
arbitrary applications beyond perovskite stability predictions
Controlling the stereochemistry and regularity of butanethiol self-assembled monolayers on Au(111)
© 2014 American Chemical Society. The rich stereochemistry of the self-assembled monolayers (SAMs) of four butanethiols on Au(111) is described, the SAMs containing up to 12 individual C, S, or Au chiral centers per surface unit cell. This is facilitated by synthesis of enantiomerically pure 2-butanethiol (the smallest unsubstituted chiral alkanethiol), followed by in situ scanning tunneling microscopy (STM) imaging combined with density functional theory molecular dynamics STM image simulations. Even though butanethiol SAMs manifest strong headgroup interactions, steric interactions are shown to dominate SAM structure and chirality. Indeed, steric interactions are shown to dictate the nature of the headgroup itself, whether it takes on the adatom-bound motif RS•Au(0)S•R or involves direct binding of RS• to face-centered-cubic or hexagonal-close-packed sites. Binding as RS• produces large, organizationally chiral domains even when R is achiral, while adatom binding leads to rectangular plane groups that suppress long-range expression of chirality. Binding as RS• also inhibits the pitting intrinsically associated with adatom binding, desirably producing more regularly structured SAMs
Ligand-Conformation Energy Landscape of Thiolate-Protected Gold Nanoclusters
Although
several thiolate-protected Au nanoclusters have yielded
to total-structure determination, the ligand-conformation energy landscapes
and how they affect the relative stability of the whole clusters are
not well understood. In this work, we employ a force-field-based approach
to perform the ligand-conformation search for isolated thiolate-protected
Au nanoclusters using Au<sub>25</sub>(SR)<sub>18</sub> (R = C<sub>2</sub>H<sub>4</sub>Ph) as an example. We find that the ligand-conformation
energy landscape of Au<sub>25</sub>(SC<sub>2</sub>H<sub>4</sub>Ph)<sub>18</sub> comprises multiple low-energy funnels of similar stability
instead of a single global minimum. In fact, we find slightly more
stable conformations of isolated Au<sub>25</sub>(SC<sub>2</sub>H<sub>4</sub>Ph)<sub>18</sub> than those observed in the experiment from
a crystalline state, indicating that specific environments such as
crystal packing and solvents may all affect the ligand conformation.
This work reveals the role of ligand conformation in the cluster energy
landscape
Distilling Accurate Descriptors from Multi-Source Experimental Data for Discovering Highly Active Perovskite OER Catalysts
Perovskite oxides are promising catalysts for oxygen evolution reaction
(OER), yet the huge chemical space remains largely unexplored due to the lack
of effective approaches. Here, we report the distilling of accurate descriptors
from multi-source experimental data for accelerated catalysts discovery by
using the new method SCMT-SISSO that overcomes the challenge of data
inconsistency between different sources. While many previous descriptors for
the catalytic activity were proposed based on respective small datasets, we
obtained the new 2D descriptor (d_B, n_B) based on 13 experimental datasets
collected from different publications and the SCMT-SISSO. Great universality
and predictive accuracy, and the bulk-surface correspondence, of this
descriptor have been demonstrated. With this descriptor, hundreds of unreported
candidate perovskites with activity greater than the benchmark catalyst
Ba0.5Sr0.5Co0.8Fe0.2O3 were identified from a large chemical space. Our
experimental validations on five candidates confirmed the three highly active
new perovskite catalysts SrCo0.6Ni0.4O3, Rb0.1Sr0.9Co0.7Fe0.3O3, and
Cs0.1Sr0.9Co0.4Fe0.6O3