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At least 19 records

Predicting Band-Gap of Inorganic Materials Using Neuromorphic Graph Learning

Predicting properties of inorganic materials is a heavily researched topic, with several new prediction approaches emerging as competitors. One such competitor is graph neural networks, which leverage the structure of the graph to aid in the prediction process. In this work, we propose integration of neuromorphic computation into the graph neural network pipeline. We call this approach Neuromorphic Graph Learning (NGL). We utilize the NGL approach to leverage evolutionary algorithms and a novel Spike Pipeline for Raster Analysis (SPIRE) for the prediction of band gap in inorganic materials.

Mulet, Ian [University of Tennessee (UT)]↗

Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’

The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open data and open-source tools to propel the field. Despite the increasing usefulness and capabilities of these tools, developers neglecting to follow reproducible practices presents a significant barrier for other researchers looking to use or build upon their work. In this study, we investigate the challenges encountered while attempting to reproduce a section of the results presented in “A general-purpose machine learning framework for predicting properties of inorganic materials.” Our analysis identifies four major categories of challenges: (1) reporting software dependencies, (2) recording and sharing version logs, (3) sequential code organization, and (4) clarifying code references within the manuscript. The result is a proposed set of tangible action items for those aiming to make material informatics tools accessible to, and useful for the community.

36 MATERIALS SCIENCE↗

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

36 MATERIALS SCIENCE↗

Plasma chemistry for inorganic materials

Practical application of plasma chemistry to the development of inorganic materials using both low temperature and warm plasmas are summarized. Topics cover: the surface nitrification and oxidation of metals; chemical vapor deposition; formation of minute oxide particles; the composition of oxides from chloride vapor; the composition of carbides and nitrides; freezing high temperature phases by plasma arc welding and plasma jet; use of plasma in the development of a substitute for petroleum; the production of silicon for use in solar cell batteries; and insulating the inner surface of nuclear fusion reactor walls.

Matsumoto, O.↗

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Chen, Jiadong (ORCID:0009000476038838)↗

The Features of Self-Assembling Organic Bilayers Important to the Formation of Anisotropic Inorganic Materials in Microgravity Conditions

There is a growing need for inorganic anisotropic particles in a variety of materials science applications. Structural, optical, and electrical properties can be greatly augmented by the fabrication of composite materials with anisotropic microstructures or with anisotropic particles uniformly dispersed in an isotropic matrix. Examples include structural composites, magnetic and optical recording media, photographic film, certain metal and ceramic alloys, and display technologies including flat panel displays. While considerable progress has been made toward developing an understanding of the synthesis of powders composed of monodispersed, spherical particles, these efforts have not been transferred to the synthesis of anisotropic nanoparticles. The major objective of the program is to develop a fundamental understanding of the growth of anisotropic particles at organic templates, with emphasis on the chemical and structural aspects of layered organic assemblies that contribute to the formation of anisotropic inorganic particles.

Talham, Daniel R.↗

Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials

Following publication of this article, concerns were raised about the unambiguous identification of the compound structures using diffraction as well as the original claims of material novelty. We acknowledge that the original claims of material novelty were subject to misinterpretation—their intention was to indicate that the materials were new to the prediction platform, not necessarily new to science. The article text has been updated to reflect this in the HTML and PDF versions of the article.

Szymanski, Nathan J. [University of California, Be↗

The features of self-assembling organic bilayers important to the formation of anisotropic inorganic materials in microgravity conditions

Materials with directional properties are opening new horizons in a variety of applications including chemistry, electronics, and optics. Structural, optical, and electrical properties can be greatly augmented by the fabrication of composite materials with anisotropic microstructures or with anisotropic particles uniformly dispersed in an isotropic matrix. Examples include structural composites, magnetic and optical recording media, photographic film, certain metal and ceramic alloys, and display technologies including flat panel displays. The new applications and the need for model particles in scientific investigations are rapidly out-distancing the ability to synthesize anisotropic particles with specific chemistries and narrowly distributed physical characteristics (e.g. size distribution, shape, and aspect ratio).

Talham, Daniel R.↗

Oil skimmer with oleophilic coating

A method of fabricating an coating includes providing a coating comprising a base material. The base material is coated with an inorganic material using at least one of an atomic layer deposition (ALD), a molecular layer deposition (MLD), or sequential infiltration synthesis (SIS) process. The SIS process includes at least one cycle of exposing the coating to a first metal precursor for a first predetermined time and a first partial pressure. The first metal precursor infiltrates at least a portion of the base material and binds with the base material. The coating is exposed to a second co-reactant precursor for a second predetermined time and a second partial pressure. The second co-reactant precursor reacts with the first metal precursor, thereby forming the inorganic material on the base material. The inorganic material infiltrating at least the portion of the base material. The inorganic material is functionalized with a material.

Darling, Seth B.↗

Inorganic Photovoltaics Materials and Devices: Past, Present, and Future

This report describes recent aspects of advanced inorganic materials for photovoltaics or solar cell applications. Specific materials examined will be high-efficiency silicon, gallium arsenide and related materials, and thin-film materials, particularly amorphous silicon and (polycrystalline) copper indium selenide. Some of the advanced concepts discussed include multi-junction III-V (and thin-film) devices, utilization of nanotechnology, specifically quantum dots, low-temperature chemical processing, polymer substrates for lightweight and low-cost solar arrays, concentrator cells, and integrated power devices. While many of these technologies will eventually be used for utility and consumer applications, their genesis can be traced back to challenging problems related to power generation for aerospace and defense. Because this overview of inorganic materials is included in a monogram focused on organic photovoltaics, fundamental issues and metrics common to all solar cell devices (and arrays) will be addressed.

Hepp, Aloysius F.↗