Materials Genome Research Articles

Toward rational and modular molecular design in soft matter engineering

This essay discusses some preliminary thoughts on the development of a rational and modular approach for molecular design in soft matter engineering and proposes ideas of structural and functional synthons for advanced functional materials. It echoes the Materials Genome Initiative by practicing a tentative retro-functional analysis (RFA) scheme. The importance of hierarchical structures in transferring and amplifying molecular functions into macroscopic properties is recognized and emphasized. According to the role of molecular segments in final materials, there are two types of building blocks: structural synthon and functional synthon. Guided by a specific structure for a desired function, these synthons can be modularly combined in various ways to construct molecular scaffolds. Detailed molecular structures are then deduced, designed and synthesized precisely and modularly. While the assembled structure and property may deviate from the original design, the study may allow further refinement of the molecular design toward the target function. The strategy has been used in the development of soft fullerene materials and other giant molecules. There are a few aspects that are not yet well addressed: (1) function and structure are not fully decoupled and (2) the assembled hierarchical structures are sensitive to secondary interactions and molecular geometries across different length scales. Nevertheless, the RFA approach provides a starting point and an alternative thinking pathway by provoking creativity with considerations from both chemistry and physics. This is particularly useful for engineering soft matters with supramolecular lattice formation, as in giant molecules, where the synthons are relatively independent of each other.

Grand Challenges in Structural Materials

INTRODUCTION The motto “citius, altius, fortius” (Latin for “faster, higher, stronger”) is known worldwide in the context of the Olympic Games and could also be viewed – with the inevitable addition of “cheaper” – as the catch-cry of the structural materials industry throughout the nineteenth and twentieth centuries. Steel, concrete, wood, structural ceramics, glass, nonferrous metals, and a wide range of composites underpin the entirety of modern society. Taller and more ambitious structures placed in more extreme service environments demand materials with higher performance: strength, durability, and scope for repair and retrofitting in challenging environments. Rapid construction not only influences costs related to performance but also demands improved materials and manufacturing. The U.S. Materials Genome Initiative Strategic Plan lists “Lightweight and Structural Materials” to be of primary importance across sectors: national security, human health and welfare, clean energy systems, and infrastructure, and consumer goods (National Science and Technology Council Committee on Technology, 2014). The goal of sustainability was added to the core ambitions of the structural materials industry around the beginning of the twenty-first century, as environmental footprints and resource depletion were better understood. As recently as 1999, the U.S. National Academy of Engineering published a report entitled “Structural Materials: Challenges and Opportunities” (Starke and Williams, 1999) without mentioning sustainability; from this point, we can observe a rapid shift in emphasis toward sustainable development objectives in less than two decades, where it would now be unthinkable to omit this aspect from a discussion of challenges in the science and engineering of structural materials. Today, we are facing scientific and technological challenges linked to provision of durable and reliable infrastructure to the billions worldwide who live without appropriate housing, energy, and/or sanitation. Solutions here will not necessarily involve advanced structural materials as such, but will be related to optimizing structural characteristics and durability at low financial and environmental costs. This requires creativity to rely on locally available resources, which means that we should not always count on one-size-fits-all solutions. Modern materials design thus goes far beyond pure technical performance. To “do more with less” has given additional impetus to research on materials recycling, reuse, and substitution, including in the improvement of material durability. The above goals can only be achieved through a thorough scientific approach to materials design that also exploits recent advances in high-throughput, combinatorial and computational methodologies in manufacturing, analytical and dataprocessing facilities. The result is that we are able to precisely specify almost all material characteristics for any given application. The present article offers a summary of the key areas of research and challenges facing structural materials researchers with particular focus on areas with major advances to be made. This discussion is by no means exhaustive as the topic merits analysis in far more depth than is possible within the context of an overview article.

Characterization of genomic variations in SNPs of PE_PGRS genes reveals deletions and insertions in extensively drug resistant (XDR) M. tuberculosis strains from Pakistan

BackgroundMycobacterium tuberculosis (MTB) PE_PGRS genes belong to the PE multigene family. Although the function of PE_PGRS genes is unknown, it is hypothesized that the PE_PGRS genes may be associated with antigenic variability in MTB. Material and methodsWhole genome sequencing analysis was performed on (n=37) extensively drug-resistant (XDR) MTB strains from Pakistan, which included Lineage 1 (East African Indian, n=2); Other lineage 1 (n=3); Lineage 3 (Central Asian, n=24); Other lineage 3 (n=4); Lineage 4 (X3, n=1) and T group (n=3) MTB strains. ResultsThere were 107 SNPs identified from the analysis of 42 PE_PGRS genes; of these, 13 were non-synonymous SNPs (nsSNPs). The nsSNPs identified in PE_PGRS genes – 6, 9 and 10 – were common in all EAI, CAS, Other lineages (1 and 3), T1 and X3. Deletions (DELs) in PE_PGRS genes – 3 and 19 – were observed in 17 (80.9%) CAS1 and 6 (85.7%) in Other lineages (1 and 3) XDR MTB strains, while DELs in the PE_PGRS49 were observed in all CAS1, CAS, CAS2 and Other lineages (1 and 3) XDR MTB strains. All CAS, EAI and Other lineages (1 and 3) strains showed insertions (INS) in PE_PGRS6 gene, while INS in the PE_PGRS genes 19 and 33 were observed in 20 (95.2%) CAS1, all CAS, CAS2, EAI and Other lineages (1 and 3) XDR MTB strains. ConclusionGenetic diversity in PE_PGRS genes contributes to antigenic variability and may result in increased immunogenicity of strains. This is the first study identifying variations in nsSNPs and INDELs in the PE_PGRS genes of XDR-TB strains from Pakistan. It highlights common genetic variations which may contribute to persistence.

The materials genome in action: identifying the performance limits for methane storage

The best ZIF structure found in screening of databases containing over half a million nanoporous materials for vehicular methane storage is shown.

Semiconductor Materials Genome Initiative: silicon-based light emission material

The purpose of the semiconductor Materials Genome Initiative is to discover, develop, and deploy new materials in such a way that the research and development period is reduced to a half of original period, and the cost to a fraction of the present cost, thereby speeding up the advance of clean energy sourse, state security, and human welfare, through the organic integration of experiment, computation and theory. Semiconductors play a key role in developing technologies and industries relating to economy, state security, and human welfare. The implement of the semiconductor materials genome initiative will promote the development of semiconductor science and technology into a new era. In this paper, we present a demo of the semiconductor material genome project through introducing our early work on designing silicon-based light emission materials. We first briefly review the status of development of silicon-compatible light emission and challenges facing it. We then demonstrate the power and value of semiconductor materials genome initiative by presenting our recent work on the inverse design of strongly dipole-allowed direct bandgap two-dimensional Si/Ge superlattices and one-dimensional Si/Ge core/multi-shell nanowires, respectively, from two indirect-gap materials (Si and Ge). We use a combination of genetic algorithms with an atomistic pseudopotential Hamiltonian to search through the astronomic number of variants of Sin/Gem//Sip/Geq stacking sequences. We finally give a short perspective of semiconductor materials genome initiative.

Opportunities in theoretical and computational polymeric materials and soft matter.

Soft materials are abundant in nature and ubiquitous in living systems. Elucidating their multi-faceted properties and underlying mechanisms is not only theoretically challenging and important in its own right, but also serves as the foundation for new materials and applications that will have wide-ranging impact on technology and the national economy. Recent initiatives in computation and data-driven materials discovery, such as the Materials Genome Initiative and the National Science Foundation Designing Materials to Revolutionize and Engineer our Future (NSF-DMREF) program, recognize and highlight the many future opportunities in the field. Building upon similar past efforts, a workshop was held at the University of California, Santa Barbara in October 2013 to specifically identify the central challenges and opportunities in theoretical and computational studies of polymeric as well as non-polymeric soft materials. This article presents a summary of the main findings of the workshop.

Characterization of genomic variations in SNPs of PE_PGRS genes reveals deletions and insertions in extensively drug resistant (XDR) M. tuberculosis strains from Pakistan

BackgroundMycobacterium tuberculosis (MTB) PE_PGRS genes belong to the PE multi-gene family. Although the function of the members of the PE_PGRS multi-gene family is not yet known, it is hypothesized that the PE_PGRS genes may be associated with genetic variability. Material and methodsWhole genome sequencing analysis was performed on (n=37) extensively drug resistant (XDR) MTB strains from Pakistan which included Central Asian (n=23), East African Indian (n=2), X3 (n=1), T group (n=3) and Orphan (n=8) MTB strains. ResultsBy analyzing 42 PE_PGRS genes, 111 SNPs were identified, of which 13 were non-synonymous SNPs (nsSNPs). The nsSNPs identified in the PE_PGRS genes were as follows: 6, 9, 10 and 55 present in each of the CAS, EAI, Orphan, T1 and X3 XDR MTB strains studied. Deletions in PE_PGRS genes: 19, 21 and 23 were observed in 7 (35.0%) CAS1 and 3 (37.5%) in Orphan XDR MTB strains, while deletions in the PE_PGRS genes: 49 and 50 were observed in 36 (95.0%) CAS1 and all CAS, CAS2 and Orphan XDR MTB strains. An insertion in PE_PGRS6 gene was observed in all CAS, EAI3 and Orphan, while insertions in the PE_PGRS genes 19 and 33 were observed in 19 (95%) CAS1 and all CAS, CAS2, EAI3 and Orphan XDR MTB strains. ConclusionGenetic diversity in PE_PGRS genes contributes to antigenic variability and may result in increased immunogenicity of strains. This is the first study identifying variations in nsSNPs, Insertions and Deletions in the PE_PGRS genes of XDR-TB strains from Pakistan. It highlights common genetic variations which may contribute to persistence.

The Materials Super Highway: Integrating High-Throughput Experimentation into Mapping the Catalysis Materials Genome

The materials genome initiative (MGI) aims to accelerate the process of materials discovery and reduce the time to commercialization of advanced materials. Thus far, the MGI has resulted in significant progress in computational simulation, modeling, and predictions of catalytic materials. However, prodigious amounts of experimental data are necessary to inform and validate these computational models. High-throughput (HT) methodologies, in which hundreds of materials are rapidly synthesized, processed, and characterized for their figure of merit, represent the key experimental enabler of the theoretical aspects of the MGI. HT methodologies have been used since the early 1980s for identifying novel catalyst formulations and optimizing existing catalysts. Many sophisticated screening and data mining techniques have been developed and since the mid-1990s, this approach has become a widely accepted industrial practice. This article will provide a short history of major developments in HT and will discuss screening approaches combining rapid, qualitative primary screens via thin-film techniques with a series of quantitative screens using plug flow reactors. An illustrative example will be provided of one such study in which novel fuel-flexible sulfur tolerant cracking catalysts were developed. We will then illustrate a path forward that leverages existing HT expertise to validate, provide empirical data to and help guide future theoretical studies.

Polymer physics and the materials genome initiative

Polymer physics and the materials genome initiative

Toward a Materials Genome Approach for ionic liquids: synthesis guided by ab initio property maps.

The Materials Genome Approach (MGA) aims to accelerate development of new materials by incorporating computational and data-driven approaches to reduce the cost of identification of optimal structures for a given application. Here, we use the MGA to guide the synthesis of triazolium-based ionic liquids (ILs). Our approach involves an IL property-mapping tool, which merges combinatorial structure enumeration, descriptor-based structure representation and sampling, and property prediction using molecular simulations. The simulated properties such as density, diffusivity, and gas solubility obtained for a selected set of representative ILs were used to build neural network models and map properties for all enumerated species. Herein, a family of ILs based on ca. 200,000 triazolium-based cations paired with the bis(trifluoromethanesulfonyl)amide anion was investigated using our MGA. Fourteen representative ILs spreading the entire range of predicted properties were subsequently synthesized and then characterized confirming the predicted density, diffusivity, and CO2 Henry's Law coefficient. Moreover, the property (CO2, CH4, and N2 solubility) trends associated with exchange of the bis(trifluoromethanesulfonyl)amide anion with one of 32 other anions were explored and quantified.

Experimental study and numerical simulation of dynamic recrystallization behavior of a micro-alloyed plastic mold steel

Abstract To research the dynamic recrystallization (DRX) characteristics of a micro-alloyed plastic mold steel (MnCrNb, SDP1®), a series of hot compression tests using Gleeble-3500 thermo-mechanical physical simulator are implemented under different temperatures and strain rates. In addition, the microstructure of specimens after hot compression tests are also observed by optical microscope. Combined Estrin and Mecking mathematic model (EM) with Avrami equation, the important material genome data of SDP1, such as DRX kinetic model, dynamic recovery (DRV) kinetic model and grain size evolution model, is established by the inverse analysis of flow stress curves to estimate the microstructure evolution during hot deformation. Furthermore, combined with the obtained material kinetic model, an elastic–plastic finite element (FE) model is built to simulate the microstructure evolution of SDP1 during the single-pass hot compression and compare with the experimental results. The results indicate that the simulation results agree well with the experimental ones, which verifies the availability of obtained material genome data. The finite element method (FEM) is an effective approach to analyze the hot compression process, which can provide a theoretical guidance and optimization scheme for making the more reasonable hot working procedure.

High dimensional data driven statistical mechanics.

In "3D4D materials science", there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modellingA three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to metric features. Such more microstructural information seems to be useful for more precise property prediction. There are two ways for microstructure-based property prediction (Fig. 1A). One is 3D image data based modelling such as micromechanics or crystal plasticity finite element method. The other one is a numerical microstructural features driven machine learning approach such as artificial neural network or Bayesian estimation method. It is the key to convert the 3D image data into numerals in order to apply the dataset to property prediction. As a numerical feature of microstructures, grain size, number of density, of particles, connectivity of particles, grain boundary connectivity, stacking degree, clustering etc. should be taken into consideration. These microstructural features are so-called "materials genome". Among those materials genome, we have to find out dominant factors to determine a focused property. The dominant factorzs are defined as "descriptor(s)" in high dimensional data driven statistical mechanics.jmicro;63/suppl_1/i4/DFU086F1F1DFU086F1Fig. 1.(a) A concept of 3D4D materials science. (b) Fully-automated serial sectioning 3D microscope "Genus_3D". (c) Materials Genome Archive (JSPS). Image acquisitionIt is important for researchers to choice a 3D microscope from various microscopes depending on a length-scale of a focused microstructure. There is a long term request to acquire a 3D microstructure image more conveniently. Therefore a fully automated serial sectioning 3D optical microscope "Genus_3D" (Fig. 1B) has been developed and nowadays it is commercially available. A user can get a good contrast image and more than one hundred sections in a day using Genus_3D, namely 3D image capturing is now a day or hour work rather than monthly work. Data sharingIt is also another important issue to archive all materials genome data including process and properties for modelling-assisted high-throughput materials design. Japan Society for the Promotion of Science (JSPS) 176 working group has announced to commence "Materials Genome Archive" service (Fig. 1C).

Exploring the possibilities and limitations of a nanomaterials genome.

What are we going to do with the cornucopia of nanomaterials appearing in the open and patent literature, every day? Imagine the benefits of an intelligent and convenient means of categorizing, organizing, sifting, sorting, connecting, and utilizing this information in scientifically and technologically innovative ways by building a Nanomaterials Genome founded upon an all-purpose Periodic Table of Nanomaterials. In this Concept article, inspired by work on the Human Genome project, which began in 1989 together with motivation from the recent emergence of the Materials Genome project initiated in 2011 and the Nanoinformatics Roadmap 2020 instigated in 2010, we envision the development of a Nanomaterials Genome (NMG) database with the most advanced data-mining tools that leverage inference engines to help connect and interpret patterns of nanomaterials information. It will be equipped with state-of-the-art visualization techniques that rapidly organize and picture, categorize and interrelate the inherited behavior of complex nanomatter from the information programmed in its constituent nanomaterials building blocks. A Nanomaterials Genome Initiative (NMGI) of the type imagined herein has the potential to serve the global nanoscience community with an opportunity to speed up the development continuum of nanomaterials through the innovation process steps of discovery, structure determination and property optimization, functionality elucidation, system design and integration, certification and manufacturing to deployment in technologies that apply these versatile nanomaterials in environmentally responsible ways. The possibilities and limitations of this concept are critically evaluated in this article.

Experimental "Materials Genomics" for Elucidation of Active Sites of M-N-C Non-PGM Electrocatalysts

Among the different Fuel Cell technologies, one is based on implementation of Polymer Electrolyte Membrane. Significant research effort has been directed towards replacement of the platinum by materials mainly consisting of metal–nitrogen–carbon (MNC) network. Heteroatomic polymeric precursors; metal macrocyclic compounds; metal salt and nitrogen/carbon organic molecules have been reported as the source for the formation of Me-N-C networks. (1)Development and optimization of non-PGM electrocatalysts consisting of a MNC framework is hindered by the complex nature of the materials, a partial understanding of the reaction mechanisms and precise chemistry of the active site or sites. The lack of this knowledge hinders the rational design of performance optimization strategies. There are two main hypotheses, one claiming that nitrogen functionalities on carbon-based support are directly responsible for their ORR activity and the second suggesting that nitrogen groups serve as the coordinating environment for metal where the ORR happens. Candidate structures participating in ORR consist of multitudes of nitrogen defect motifs in the carbon matrix of different degrees of graphitization with metal incorporated as metal particles as well as those linked to nitrogen defects in a variety of possible architectures.In all of studies used to derive the hypothesis of an active site or sites, the chemical structure of active sites is suggested through correlations between activity as measured either as half-way potential, E1/2, or current at a certain voltage, i.e. I at 0.8 V, and concentration of certain species as determined by spectroscopic method such as XPS. The cross-comparison between different reports is complicated due to the fact that different electrochemical testing protocols, as well as, different metrics of activity are used. The direct correlation between spectroscopic identifications is also complicated because different acquisition and data processing parameters are used. Besides cross-laboratory study published in 2009 (2), there is no systematic study comparing performance and structure of electrocatalysts obtained from different precursors at a variety of compositions which are tested electrochemically using the same fixed protocol and analyzed spectroscopically using the same collection and data processing procedure. In this report, we describe the data from more than 40 electrocatalysts containing metal and 8 without metal from three families of precursors, such as polymer-based, macrocycles and small organic molecules. For each of the precursors, several electrocatalysts were synthesized using the same sacrificial support method introduced by Serov (3-5), at different temperatures of pyrolysis, ratio between the precursor and iron salt and two types of leaching the support, KOH or HF. Each of the electrocatalysts was electrochemically tested using exactly the same RDE protocol and analyzed structurally by XPS for deriving structure-to-property relationships for identification of active sites.Through direct comprehensive structure-to-performance correlations, we have derived possible active sites participating in possible reaction pathway of ORR. Figure 1 shows correlations between activity and different types of nitrogen as detected by XPS. There is a correlation between amounts of pyridinic nitrogen and Fe-Nx centers and activity. Pyrrolic nitrogen catalyzes the 1st step of O2reduction to hydrogen peroxide. Ratio of pyridinic to pyrrolic nitrogens seems to be a very reliable indicator of electrocatalytic activity. We will discuss these correlations in terms of mechanism of ORR in more details.1. Z. W. Chen, D. Higgins, A. P. Yu, L. Zhang and J. J. Zhang, Energ Environ Sci, 4, 3167 (2011).2. F. Jaouen, J. Herranz, M. Lefevre, J. P. Dodelet, U. I. Kramm, I. Herrmann, P. Bogdanoff, J. Maruyama, T. Nagaoka, A. Garsuch, J. R. Dahn, T. Olson, S. Pylypenko, P. Atanassov and E. A. Ustinov, Acs Appl Mater Inter, 1, 1623 (2009).3. A. Serov, M. H. Robson, K. Artyushkova and P. Atanassov, Applied Catalysis B: Environmental, 127, 300 (2012).4. M. H. Robson, A. Serov, K. Artyushkova and P. Atanassov, Electrochim Acta, 90, 656 (2013).5. A. Serov, K. Artyushkova and P. Atanassov, Adv. Energy Materials accepted (2014).

Uncertainty Quantification in Materials Modeling

The Institute for Mathematics and its Applications (IMA) at the University of Minnesota hosted the workshop, ‘‘Uncertainty Quantification in Materials Modeling’’ on December 16–17, 2013. The workshop convened 30 scientists specializing in materials modeling and simulation and researchers in uncertainty quantification (UQ) with the goal of identifying challenges in UQ for materials applications. The following discussion is a summary of workshop findings as well as an invitation to participate in an ambitious follow-up activity tentatively scheduled for summer 2015.* The workshop objectives may be better understood in the context of the Materials Genome Initiative (MGI), which was formally announced by President Barack Obama in June 2011. This ambitious multi-agency program seeks to accelerate the introduction of novel materials into industrial products, striving to reduce by factors of two both the time and cost from discovery to deployment. The initiative proposes creation of a new Materials Innovation Infrastructure as the principal means to achieve this end. This infrastructure will consist of an integrated collection of experimental data and simulation software. These two sources of materials information—experiment and computation—will be seamlessly integrated using data informatics tools tailored for material science communities. The result will be a set of shared, community resources facilitating rapid access to data and numerical simulations on demand. While all elements of this innovation infrastructure will require investments for their research and development, the IMA workshop focused on the role of numerical simulation in material science and engineering. It is natural to expect that increased reliance on computational tools to design and assess materials in silico will enable rapid and relatively inexpensive exploration of design possibilities. That being said, critical assessment of simulation outputs is a prerequisite to promote understanding and trust in the capabilities of simulation for industrial decision making. Uncertainty quantification is an umbrella term that refers to the diverse analysis methods and tools suitable for critical assessment of models and simulations. Elements of UQ analysis include verification and validation (V&V), sensitivity analysis, and uncertainty propagation. For completeness we briefly define these terms here and refer to the recent report by the National Research Council and the numerous references therein for more detailed discussion. Verification is defined as an analysis of the quantitative correspondence between a conceptual model and its numerical implementation. Verification is exercised primarily in the computational domain and may be viewed as a modern descendant of classic numerical analysis. This term is not to be confused with validation, which is defined as an analysis of the quantitative correspondence between a numerical simulation and the desired quantities of interest in a physical experiment. Here critical attention must be paid to analysis and quantification of systemic errors introduced by selection of a specific model and the associated reductions and simplifications in the physics that the model represents in comparison with the referent physical process. Colloquially speaking, it is here that one attempts to quantify the ‘‘unknown unknowns.’’ In general, it is very difficult to quantify such errors. An important area of research in validation is to assess how confidence in the prediction of a model degrades while interpolating between or extrapolating from conditions where *This work is a contribution of the National Institute of Standards and Technology and is not subject to copyright in the United States. JOM, Vol. 66, No. 7, 2014

A genomic approach to the stability, elastic, and electronic properties of the MAX phases

In this study, we report a comprehensive assessment on the elastic and electronic properties of 792 possible MAX (Mn+1AXn) phases with n = 1–4 using ab initio methods. These crystals are then screened based on their elastic and thermodynamic stability resulting in a large database of 665 viable crystals. All the experimentally verified MAX phases passed the screening. Various correlations among and between them are fully explored. In particular, the key elements in the interdependence between the elastic properties together with mechanical parameter derived from them and the electronic structure are identified. Detailed analysis of various correlation plots shows that there is a clear correspondence between bulk modulus K and total bond order density (TBOD). Calculations show a marked difference between the carbides and nitrides. This database is also used to test the efficacy of data mining algorithms for materials genome. We further identified several thermodynamically stable new MAX phases with unusual mechanical parameters that have never been synthesized in the laboratory or theoretically investigated. The complete database on the elastic and electronic structure together with the mechanical parameters for these 665 MAX phases compounds are included in the Supplementary Materials and fully accessible.

Emerging Science and Research Opportunities for Metals and Metallic Nanostructures

During the next decade, fundamental research on metals and metallic nanostructures (MMNs) has the potential to continue transforming metals science into innovative materials, devices, and systems. A workshop to identify emerging and potentially transformative research areas in MMNs was held June 13 and 14, 2012, at the University of California Santa Barbara. There were 47 attendees at the workshop (listed in the Acknowledgements section), representing a broad range of academic institutions, industry, and government laboratories. The metals and metallic nanostructures (MMNs) workshop aimed to identify significant research trends, scientific fundamentals, and recent breakthroughs that can enable new or enhanced MMN performance, either alone or in a more complex materials system, for a wide range of applications. Additionally, the role that MMN research can play in high-priority research and development (R&D) areas such as the U.S. Materials Genome Initiative, the National Nanotechnology Initiative, the Advanced Manufacturing Initiative, and other similar initiatives that exist internationally was assessed. The workshop also addressed critical issues related to materials research instrumentation and the cyberinfrastructure for materials science research and education, as well as science, technology, engineering, and mathematics (STEM) workforce development, with emphasis on the United States but with an appreciation that similar challenges and opportunities for the materials community exist internationally. A central theme of the workshop was that research in MMNs has provided and will continue to provide societal benefits through the integration of experiment, theory, and simulation to link atomistic, nanoscale, microscale, and mesoscale phenomena across time scales for an ever-widening range of applications. Within this overarching theme, the workshop participants identified emerging research opportunities that are categorized and described in more detail in the following sections in terms of the following: three-dimensional (3-D) and four-dimensional (4-D) materials science. Structure evolution and the challenge of heterogeneous and multicomponent systems. The science base for property prediction across the length scales. Nanoscale phenomena at surfaces—experiment, theory, and simulation. Prediction and control of the morphology, microstructure, and properties of “bulk” nanostructured metals. Functionality and control of materials far from equilibrium. Hybrid and multifunctional materials assemblies. Materials discovery and design: enhancing the theory-simulation-experiment loop. Following an introduction, these emerging research opportunities are discussed in detail, along with challenges and opportunities for the materials community in the areas of instrumentation, cyberinfrastructure, education, and workforce development.

A Material Genomic Design of Advanced High Performance, Non-Corroding Steels for Ambient and High Temperature Applications

This work presents an artificial intelligence based design of a series of novel advanced high performance steels for ambient and high temperature applications, following the principle of the materials genome initiative, using an integrated thermodynamics/kinetics based model in combination with a genetic algorithm optimization routine. Novel steel compositions and associated key heat treatment parameters are designed both for applications at the room temperature (ultra-high strength maraging stainless steel) and at high temperatures (ferritic, martensitic and austenitic creep resistant steels). The strength of existing high end alloys of aforementioned four types are calculated according to the corresponding design criteria. The model validation studies suggest that the newly designed alloys have great potential in outperforming existing grades.

Modeling Static and Dynamic Kinetics of Microstructure Evolution in Type 316 Stainless Steel

The material genome of type 316 austenitic stainless steel is presented. The material genome is a set of constitutive equations which describes the microstructure evolution during hot forming. Single‐ and double‐compression tests at temperatures of 950–1150°C and with strain rates of 0.01–20 s−1 were performed to obtain the kinetics of dynamic and static events. Inverse analysis, which couples a thermomechanical finite element analysis with experimental data, is used to obtain the flow curves. It compensates the effect of inhomogeneous distributions of deformation and temperature during the compression tests. The sine‐hyperbolic law, which relates the flow stress and the Zener–Hollomon parameter, is used to validate the accuracy of the solution. Regression analysis is applied on the coefficients of the flow curves in order to obtain the parameters of the constitutive equations. Microstructure investigations are used to demonstrate the validity of the newly obtained material genome.

Computational thermodynamics, computational kinetics, and materials design

Computational thermodynamics, known as CALPHAD method when dawned in 1950s, aimed at coupling phase diagrams with thermochemistry by computational techniques. It eventually evolves toward kinetic simulations integrated with thermodynamic calculations, i.e., computational kinetics, including diffusion-controlled phase transformation, precipitation simulation, and phase-field simulation. In the meantime, thermodynamic, mobility, and physical property databases for multi-component and multi-phase materials, served as basic knowledge for materials design, are critically evaluated by CALPHAD approach combining key experiments, first-principles calculations, statistic methods, and empirical theories. The combination of these computational techniques with their conjugated databases makes it possible to simulate phase transformations and predict the microstructure evolution in real materials in a foreseeable future. Further links to micro- and macro-scale simulations lead to a multi-scale computational framework, and aid the search for the quantitative relations among chemistry, process, microstructures, and materials properties in order to accelerate materials development and deployment. This is a new route of materials and process design pursued by Integrated Computational Materials Engineering (ICME) and Materials Genome Initiative (MGI). This article presents a review on the basic theories and applications, the state of the art and perspective of computational thermodynamics and kinetics.