3,465 research outputs found
Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling
The lattice thermal conductivity ({\kappa}{\omega}) is a key property for
many potential applications of compounds. Discovery of materials with very low
or high {\kappa}{\omega} remains an experimental challenge due to high costs
and time-consuming synthesis procedures. High-throughput computational
pre-screening is a valuable approach for significantly reducing the set of
candidate compounds. In this article, we introduce efficient methods for
reliably estimating the bulk {\kappa}{\omega} for a large number of compounds.
The algorithms are based on a combination of machine-learning algorithms,
physical insights, and automatic ab-initio calculations. We scanned
approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among
the 450 mechanically stable ordered semiconductors identified, we find that
{\kappa}{\omega} spans more than two orders of magnitude- a much larger range
than that previously thought. {\kappa}{\omega} is lowest for compounds whose
elements in equivalent positions have large atomic radii. We then perform a
thorough screening of thermodynamical stability that allows to reduce the list
to 77 systems. We can then provide a quantitative estimate of {\kappa}{\omega}
for this selected range of systems. Three semiconductors having
{\kappa}{\omega} < 5 W /(m K) are proposed for further experimental study.Comment: 9 pages, 4 figure
Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design
A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives
Structure maps for MAX phases formability revisited
The extraordinary chemical diversity of MAX phases raises the question of how
many and which novel ones are yet to be discovered. The conventional schemes
rely either on executions of well designed experiments or elaborately crafted
calculations; both of which have been key tactics within the past several
decades that have yielded many of important new materials we are studying and
using today. However, these approaches are expensive despite the emergence of
high throughput automations or evolution of high speed computers. In this work,
we have revisited the in prior proposed light duty strategy, i.e. structure
mapping, for describing the genomic conditions under which one MAX phase could
form; that allow us to make successful formability and non formability
separation of MAX phases with a fidelity of 95.5%. Our results suggest that the
proposed coordinates, and further the developed structure maps, are able to
offer a useful initial guiding principles for systematic screenings of
potential MAX phases and provide untapped opportunities for their structure
prediction and materials design
Automated computation of materials properties
Materials informatics offers a promising pathway towards rational materials
design, replacing the current trial-and-error approach and accelerating the
development of new functional materials. Through the use of sophisticated data
analysis techniques, underlying property trends can be identified, facilitating
the formulation of new design rules. Such methods require large sets of
consistently generated, programmatically accessible materials data.
Computational materials design frameworks using standardized parameter sets are
the ideal tools for producing such data. This work reviews the state-of-the-art
in computational materials design, with a focus on these automated
frameworks. Features such as structural prototyping and
automated error correction that enable rapid generation of large datasets are
discussed, and the way in which integrated workflows can simplify the
calculation of complex properties, such as thermal conductivity and mechanical
stability, is demonstrated. The organization of large datasets composed of
calculations, and the tools that render them
programmatically accessible for use in statistical learning applications, are
also described. Finally, recent advances in leveraging existing data to predict
novel functional materials, such as entropy stabilized ceramics, bulk metallic
glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo
Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the “second secret of life.” The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets
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