Materials Genome Research Articles

Rise of the Liquid Metal Science, Technology and Industry: Advancements and Opportunities

The room temperature liquid metal and its allied materials are a class of emerging functional matters with diverse species. Recently, with breakthrough discoveries made on liquid metals, tremendous exciting applications were raised and many new materials that had never been anticipated before were invented. As a result, the latest achievements on liquid metals were ascribed as the second revolution of human beings over the process of utilizing metals. This article briefly summarized typical advancements, fundamental sciences, and key technological and industrial areas thus initiated which include but are not limited to: chip cooling and energy utilization, printed electronics and 3D printing, biomedical materials, as well as smart soft machines. In addition, historic background to propose and establish “The China Liquid Metal Valley” and the basic strategy to mold a brand new industry of liquid metal were outlined. The core values to strengthen future research on liquid metal material genome engineering and thus build up corresponding databases were summarized. Overall, liquid metals are important frontiers for science, technology, and industry integrating both fundamental and practical issues together. Further continuous endeavors would lead to pivotal progress of human civilization and thus reshape social production and lifestyle. Its impact for both China and the world to explore next generation revolutionary science, technology, and industry will be huge.

Searching for stable perovskite solar cell materials using materials genome techniques and high-throughput calculations

A summary of four classes of perovskites and their representatives, considering the stability and PV properties for each type of perovskite.

An efficient materials genome method to predict heterostructure interfaces

An efficient materials genome method was demonstrated to predict the interfaces of Er2O3(110)/Si(001), GaN(0001)/Sc2O3(111), and GaN(0001)/Ga2O3(100) heterostructures by using bond enthalpies of interfacial bonding based on sequential bulk bonding configurations. In this study, the structural properties of these heterojunctions were reviewed, some new results on the interfacial bonding of the heterojunctions were presented, and the method that efficiently predicts heterojunction interfaces was discussed. We successfully provide a general computational methodology for analyzing interfacial structures without building an interfacial supercell.

A Machine Learning Tool for Materials Informatics

AbstractIn response to the increasing demand for the highly efficient design of materials, materials informatics has been proposed for using data and computational sciences to extract data features that provide insight into how properties track with microstructure variables. However, the general metrics of microstructural features often ignore the complexities of the microstructure geometry for many properties of interest. An independently developed machine learning tool called shiny materials genome integration system for phase and property analysis (ShinyMIPHA), which is designed with either standalone software or cloud system based on an R programing package of “Shiny”, is introduced. ShinyMIPHA provides topological microstructure analysis methods based on image processing technology by employing a two‐point correlation function, persistent homology, and mean (H)–Gauss (K) curvature approaches, as well as sparse study and regression analysis methods that enable a data‐driven properties‐to‐microstructure‐to‐processing inverse materials‐design approach. The demo version is available at https://adachi-lab.shinyapps.io/demo/.

Mesoscale simulation research on the homogenized elasto-plastic behavior of FeCrAl alloys

Iron chromium aluminum (FeCrAl) alloys have been considered as a promising candidate in nuclear power applications. In order to accelerate the advanced fabrication of these alloys, it is necessary to perform numerical simulations to predict the macroscale mechanical properties for FeCrAl alloys. In this study, modified crystal plasticity models and stress update algorithm are proposed to describe the deformation behavior of single crystals. Computational homogenization methods for polycrystalline aggregates are provided to obtain the macroscopic properties from the mesoscale stress and strain fields in the representative volume element (RVE). The involved models and algorithms are implemented in ABAQUS with a self-written VUMAT subroutine, and validated with experimental data. The homogenized elasto-plastic curves for FeCrAl alloys are numerically obtained, which match well with the experimental results for different alloy compositions, grain sizes and processing conditions. The relations of the shear-rate model parameters with the compositions, microstructures and processing conditions of FeCrAl alloys are discussed. The research results indicate that the phenomenological shear-rate model and the modified crystal plasticity models can be adopted in Materials Genome Initiative (MGI) to achieve the multi-scale simulation of polycrystalline mechanical performances. The simulation results could also provide a reference for the optimization of FeCrAl alloys.

First Principle Material Genome Approach for All Solid‐State Batteries

Due to ever‐increasing concern about safety issues in using alkali metal ionic batteries, all solid‐state batteries (ASSBs) have attracted tremendous attention. The foundation to enable high‐performance ASSBs lies in delivering ultra‐fast ionic conductors that are compatible with both alkali anodes and high‐voltage cathodes. Such a challenging task cannot be fulfilled, without solid understanding covering materials stability and properties, interfacial reactions, structural integrity, and electrochemical windows. Here in this work, we will review recent advances on fundamental modeling in the framework of material genome initiative based on the density functional theory (DFT), focusing on solid alkali batteries. Efforts are made in offering a dependable road chart to formulate competitive materials and construct “better” batteries.

A MGI-oriented investigation of the Young's modulus and its application to the development of a novel Ti-Nb-Zr-Cr bio-alloy.

Young's modulus is essential for the design and production of the alloys. Thus, we proposed a MGI (Materials Genome Initiative)-oriented strategy for the high-throughput development of Young's modulus databank in the single-phase alloys. In this study, 17 diffusion couples of the bcc Ti-rich Ti-Nb-Zr, Ti-Nb-Cr and Ti-Nb-Zr-Cr systems annealed at 1273 K for 25 h were experimentally prepared. Subsequently, the composition-dependent Young's moduli and hardness in the bcc Ti-rich Ti-Nb-Zr-Cr system were determined by combining the nanoindentation technique and the electron probe micro analysis (EPMA). Moreover, on the basis of the presently obtained experimental data, the Young's modulus databank in the bcc Ti-Nb-Zr-Cr system were established by means of the CALPHAD (CALculation of PHAse Diagrams) approach. Finally, the Ti-22.6 at.% Nb-30 at.% Zr-3.8 at.% Cr alloy was designed from the Young's modulus databank and verified by using the nanoindentation and cytotoxicity tests. The results reveal that the present MGI-oriented strategy with the combination of the high-throughput measurements and the CALPHAD approach is a very effective method to accelerate the design/development of novel bio-Ti alloys.

Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN

Materials data management, analytics, and e-collaborations have been identified as three of the main technological gaps currently hindering the realization of the accelerated development and deployment of advanced materials targeted by the federal materials genome initiative. In this paper, we present our ongoing efforts aimed at addressing these critical gaps through the customized design and build of suitable data infrastructure elements. Specifically, our solutions include: (1) ELA—an experimental and laboratory automation software platform that systematically tracks interrelationships between the heterogeneous experimental datasets (i.e., provenance) acquired from diverse sample preparation and materials characterization equipment in a single consistent metadata database, (2) PyMKS—the first Python-based open-source materials data analytics framework that can be used to create high-fidelity, reduced-order (i.e., low computational cost), process–structure–property linkages for a broad range of material systems with a rich hierarchy of internal structures spanning multiple length scales, and (3) MATIN—a HUBzero-based software platform aimed at nucleating an emergent e-science community at the intersection of materials science, manufacturing, and computer science, and facilitating highly productive digital collaborations among geographically and organizationally distributed materials innovation stakeholders. This paper provides a timely report of lessons learned from these interrelated efforts.

Computational prediction of nanostructured alloys with enhanced thermoelectric properties

The Materials Genome Initiative calls for a dramatic increase in the rate of materials discovery and development. High-throughput (HT) calculations can advance this goal by efficiently screening a large search space for candidate materials to study in more depth. Thermoelectric materials (TEs) are prime candidates for such HT calculations: The properties required to achieve good performance are known, but systematic ways of improving these properties are scarce. Furthermore, known HT methods for TEs only address bulk crystals---screening realistic multicomponent alloys for their TE properties has yet to be accomplished. In this paper, we use a density functional theory driven HT screening-and-sorting procedure to search for new multicomponent bulk-nanostructured thermoelectric materials. We make maximum use of minimal calculations to obtain eight descriptors of the thermodynamics and TE performance of five-element semiconductor alloy systems from combinations of ternary additions in binary compounds. We use these descriptors to reduce a search space of 29 700 five-element systems to a set of 130 candidates. We screen these candidates using TE descriptors to identify several existing high-performance thermoelectrics as well as promising new material systems awaiting further experimental verification.

Using Deep Neural Networks to Predict the Tensile Property of Ceramic Matrix Composites Based on Incomplete Small Dataset

It has been a hot spot in the area of material science that how to design an experiment to produce a kind of ceramic matrix composites (CMCs) which possesses ideal performances. An approach is to build models with data from previous experiments recorded in published papers and predict the experiment parameters needed by the ideal CMCs. According to the database of CMCs funded by the National Material Genome Engineering, 8 factors were considered to affect the tensile property of CMCs, which were the basis, the reinforcement fiber type, the reinforcement fiber volume content, the perform type, the porosity, the interface type, the interface thickness and the density. Among the data we collected from papers, however, only few of pieces contained all the 8 factors, most were incomplete, some of them even lacked multiple factors. This paper’s work mainly researched how to take advantage of the incomplete data to build an effective model to predict the tensile property of CMCs. We proposed a model to predict the tensile property of CMCs based on a 1-D convolution neural network (CNN), the training data of which were all from papers. To decrease the influences of the incompleteness of data, we tried several methods to process the missing data, such as the mean imputation, the K-Means clustering imputation, the Hot-Deck Imputation and the regression imputation. The results showed that the regression imputation with a dual-hidden-layer feedforward network performed better and improved the performance of the CNN tensile property prediction model.

Combinatorial Tuning of Structural and Optoelectronic Properties in CuxZn1−xS

Summary P-type transparent conductors (TCs) are important enabling materials for optoelectronics and photovoltaics, but their performance still lags behind n-type counterparts. Recently, semiconductor CuxZn1−xS has demonstrated potential as a p-type TC, but it remains unclear how properties vary with composition. Here, we investigate CuxZn1−xS across the entire alloy space (0 ≤ x ≤ 1) using combinatorial sputtering and high-throughput characterization. First, we find a metastable wurtzite alloy at an intermediate composition between cubic endpoint compounds, contrasting with solid solutions or cubic composites (ZnS:CuyS) from the literature. Second, structural transformations correlate with shifts in hole conductivity and absorption; specifically, conductivity increases at the wurtzite phase transformation (x ≈ 0.19). Third, conductivity and optical transparency are optimized within a "TC regime" of 0.10

(Invited) The Materials Genome and Electrochemistry

In this presentation I will review the US Materials Genome Initiative and explore the progress made to create the infrastructure required to accelerate the discovery, design, development, and deployment of new materials at a fraction of the cost. The connections to Chemistry and Electrochemistry are in some sense both trivial and profound.

(Invited) Designing Materials to Revolutionize and Engineer the Future of Clean Energy Technologies through Computationally-Led and Data-Driven Approaches

The Materials Genome Initiative (MGI) is a multi-agency partnership that seeks to accelerate the pace of materials development across the Materials Development Continuum. Through use of a computationally-led and data-driven approach, MGI promotes the rapid discovery and progression to deployment of advanced materials that will ensure sustained American leadership in sectors including clean energy. The goals of the MGI include 1) leading a culture shift in materials research through use of an iterative feedback-loop approach, 2) integrating experiment, computation, and theory throughout the materials research community, 3) making digital data accessible and useful to the larger community, and 4) creating a world-class materials science and engineering workforce. The Designing Materials to Revolutionize and Engineer our Future (DMREF) program at the National Science Foundation (NSF) partners with a number of other federal agencies to promote these objectives. DMREF currently funds about one hundred projects that cover the full spectrum of materials research, including a number that focus on energy conversion technologies. This talk will provide an overview of the DMREF program and provide specific examples of ways that DMREF teams are benefitting from collaboration with MGI-related efforts at other federal agencies. Interactions with the U.S. Department of Energy's (DOE) Energy Materials Network (EMN) will be highlighted. Future priorities and funding opportunities for the DMREF program will be discussed.

On the binary Sb–Sn system: ab initio calculation and thermodynamic remodeling

The thermodynamic descriptions of phase diagrams play an important role in modern materials engineering, especially as a part of materials genome used for development of new alloys. Therefore, it is crucial to have a thermodynamic database that is in agreement with recent experimental findings. The binary Sb–Sn system is an important part of step soldering and a promising Li-ion battery electrode; therefore, a knowledge of its phase equilibria is essential for modern engineering. The newest experimental results enhanced the knowledge about phase equilibria and crystal structures in this system, and hence it is possible to propose a new, more accurate thermodynamic model of this important binary system. In this work, the CALPHAD method was used for determination of Gibbs energies of all phases; moreover, the new knowledge about a crystal structure of intermetallic compound Sb3Sn4 enabled the application of the first-principles calculations, which made CALPHAD description more precise. The proposed thermodynamic description shows a good agreement with available experimental data and can be used for future development of higher-ordered alloys.

Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel

With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including high-end steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (Vf) and driving force (Df) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.

Additive manufacturing alumina components with lattice structures by digital light processing technique

Digital light processing technique was applied to manufacture alumina ceramic parts with two types of lattice structure units, i.e. vertex interconnect structure and edge structure. The internal porosity of the unit is 40%. The printed parts were sintered and the grain size is about 1.1 μm. The bending strength of the vertex interconnect structure is much larger than that of the edge structure. Materials genome initiative (MGI) aims to digital design and intelligent manufacture for advanced components. This research shows us an example to achieve this goal.

1. EVALUATION AND IMPLEMENTATION OF ONEPGT FOR MONOGENIC CONDITIONS

Introduction Preimplantation genetic testing for monogenic disorders (PGT-M) offers couples who carry a known inherited genetic condition the chance of having children free of the disorder. Currently, a bespoke genetic test is developed for each new condition which can take up to 6 months to implement; over 300 gene specific tests have been produced at our centre. OnePGT (Agilent) utilises genome-wide Next Generation Sequencing (NGS), allowing most genetic conditions to be tracked within families and embryos. Availability of a commercially developed genetic test eliminates the time involved with new test development. Evaluation of OnePGT for the testing of embryos for familial monogenic conditions has been undertaken. Material & methods Whole genome amplified (WGA) biopsy DNA was available for 33 embryos from 13 couples who previously underwent PGT-M; all embryo biopsies consisted of ∼5 trophectoderm cells. The genetic status of the embryos had been determined using an ISO15189 accredited STR haplotyping approach. 13 single gene disorders were tested including 9 autosomal dominant, 2 autosomal recessive and 2 X-linked. The embryo samples and respective reference family gDNA samples were processed using the OnePGT workflow, sequenced on a NextSeq550 and the data analysed using Alissa software for SNP haplotyping of the monogenic disorder, in conjunction with targeted assessment of copy number of the chromosome on which the gene was located, to aid interpretation of the monogenic results in embryos. Results OnePGT results were all in concordance with the outcomes from the STR results (33/33). The automated PGT-M call rate was 28/33 whilst 5 required manual inspection. Of these, 3 were because there was less than 1Mb between the genes to the end of the chromosome, 1 was due to the proximity of the gene to a chromosome crossover breakpoint and 1 was due to a reduced number of concordant SNPs which was caused by the low-level presence of both maternal haplotypes in the embryo. In one case, an affected embryo was successfully used as the reference for phasing for the couple and in another, the availability of chromosome copy number identified an embryo as having uniparental Inheritance. Conclusions The NGS approach of OnePGT has been successfully evaluated for determining the genetic status of embryos for monogenic disease. Due to the sequencing costs involved with NGS, it may not be merited when there is an established ‘in-house’ test. On balance, taking all laboratory aspects into consideration, OnePGT is a contender to evaluate new disorders. It is an anxious time for couples as they wait to know whether they have embryos suitable for replacement. Implementing OnePGT will eliminate test development and expedite genetic testing for couples requesting PGT-M for rare monogenic diseases. Declaration of Interest The OnePGT kits were provided by Agilent as part of an early access programme

Machine learning for glass science and engineering: A review

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties.

Machine learning for glass science and engineering: A review

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties.

Quantitative prediction of the structure and luminescence properties of Nd3+ doped borate laser glasses

AbstractAlthough great advance has been made in glass science, predicting luminescence properties of laser glass poses a significant challenge for scientists due to the complex relationship between the composition, structure, and properties of the rare earth ions doped laser glasses. The development of high‐performance laser glass usually relies on intuition and trial‐and‐error. Recently, with the proposal of the materials genome engineering, the “glass genome” has also attracted much attention. Here, the structure of the Nd3+ doped B2O3‐Li2O laser glasses was analyzed using Fourier transform infrared spectra and nuclear magnetic resonance, revealing that the glass contains similar glass‐forming ion‐centered coordination polyhedron structure groups to the neighbor congruent glassy compounds. The structure and properties of glass largely depend on the neighbor congruent glassy compounds. Therefore, the structure and luminescence properties of Nd3+ doped B2O3‐Li2O and B2O3‐MgO‐Li2O laser glasses can be quantitatively predicted via the neighbor congruent glassy compounds. The predictive values are in good agreement with the experimental data, which indicates that our approach is an effective way to predict the structure and luminescence properties of Nd3+ doped borate laser glasses.