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DOE OSTI2024

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Biological Electron Transfer and Catalysis EFRC

The focus of the Biological Electron Transfer and Catalysis (BETCy) EFRC research is elucidating mechanisms of conversion of electrochemical potential into chemical bond energy and is organized into two integrated Thrusts: Electron Bifurcation Catalysis and Electron Transfer Kinetics. We are developing a collective knowledge of metalloenzymes as models for redox reactions by applying physical science and computational tools to characterize biochemical reactions catalyzed by multi- subunit enzymes harboring arrays of iron-sulfur clusters and flavin cofactors. Understanding these mechanisms is central to overcoming the thermodynamic barriers that currently limit production of reduced products and fuels. Biological systems have elegant strategies for converting electrochemical potential energy into chemical bond energy (e.g., C-H, H-H, and N-H) stored in reduced compounds that can serve as advanced biofuels. One significant limitation for the production of highly reduced compounds is that their production in natural and industrial processes relies on low oxidation-reduction potential chemicals as feedstocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Machine learning strategy for accelerated design of polymer dielectrics

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces, and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. Furthermore, while this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A novel design of transitional layer structure between reduced activation ferritic martensitic steels and tungsten for plasma facing materials

Plasma-facing components (PFCs) are among the most critical gaps for fusion energy to establish technical and economic feasibility. Tungsten as a first wall/blanket material in PFCs requires to be integrating with reduced activation ferritic martensitic (RAFM) steels as a structural component. Currently, major drawbacks are the requirement of brazing, the formation of a brittle interface, and a large difference between the coefficients of thermal expansion of tungsten and steel. Here, a novel transitional multilayer structure was designed and investigated to join tungsten and RAFM steels using three interlayers. The composition of each interlayer was selected based on computational thermodynamics and diffusion kinetics to ensure a body-centered cubic (bcc) single-phase structure and prevent the formation of a brittle intermetallic phase region in the temperature range of 600–1150 °C. Although the transitional layer structure was designed for additive manufacturing, spark plasma sintering (SPS) as proof of concept was used to bond the individual layers. Interfaces were investigated using scanning and transmission electron microscopy methods but no layered intermetallic phase was observed. Nanoindentation maps across the interface suggest major hardness differences at the interface between tungsten and the vanadium interlayer, as well as the interface between RAFM steel and the FeCrAl interlayer.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Computational design of a homotrimeric metalloprotein with a trisbipyridyl core

Significance This article reports the computational design of a threefold symmetric, self-assembling protein homotrimer containing a highly stable noncanonical amino acid-mediated metal complex within the protein interface. To achieve this result, recently developed protein–protein interface design methods were extended to include a metal-chelating noncanonical amino acid containing a bipyridine functional group in the design process. Bipyridine metal complexes can give rise to photochemical properties that would be impossible to achieve with naturally occurring amino acids alone, suggesting that the methods reported here could be used to generate novel photoactive proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Significant DBSCAN+: Statistically Robust Density-based Clustering

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

Computer Science↗

DOE OSTI2016

Using the H Index to Assess Impact of DOE National Laboratories

The most readily accessible elements of the Emerald Matrix by quantitative measures are the knowledge and economy related measures. In this paper, the H Index for an institution will be used to assess STE impact, which is in the knowledge generation element. The H Index was developed by Hirsch (2005) as a measure of an individual’s scientific impact. The H Index is defined as the number of publications that have been cited h or more times for a given author. It has been generalized to organizations. Doing so leads to a complication in that H index scales with the number of publications. Although this may not be problematic when comparing individual researchers, it systematically favors larger institutions. Molinari and Molinari (2008) proposed an alternative index (hm) designed to assess organizational impact. It transforms the H Index for an organization into an impact index by removing a factor dependent on the number of publications. The hm provides another approach to compare institutions provided that differences in the citation patterns associated with fields of study are addressed. Kinney (2007) used the Molinari and Molinari (2008) approach to compare various scientific institutions in nonbiomedical research areas. Kinney (2007) used the Thomson Reuters Web of Science (WoS) as the source and used publications in nonbiomedical research areas, which is very important because the research areas of universities are much broader than say a DOE national laboratory. Also there are differences in citation rates for the various research fields that make comparisons between individuals or organizations difficult. The results from Kinney (2007) are given in Table 1 and indicate that the DOE national laboratories compare favorably with the selected universities in terms of impact (hm) in the research areas used in Kinney’s analysis. This report will compare hm for DOE national laboratories using an approach similar to Kinney (2007) providing a measure of impact of the DOE national laboratories.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Measurement of the effective leptonic weak mixing angle

Using pp collision data at $\sqrt{s}$ = 13 TeV, recorded by the LHCb experiment between 2016 and 2018 and corresponding to an integrated luminosity of 5.4 fb −1 , the forward-backward asymmetry in the pp → Z/γ* → μ + μ − process is measured. The measurement is carried out in ten intervals of the difference between the muon pseudorapidities, within a fiducial region covering dimuon masses between 66 and 116 GeV, muon pseudorapidities between 2.0 and 4.5 and muon transverse momenta above 20 GeV. These forward-backward asymmetries are compared with predictions, at next-to-leading order in the strong and electroweak couplings. The measured effective leptonic weak mixing angle is ${\sin}^2{\theta}_{\textrm{eff}}^{\ell }=0.23147\pm 0.00044\pm 0.00005\pm 0.00023,$ where the first uncertainty is statistical, the second arises from systematic uncertainties associated with the asymmetry measurement, and the third arises from uncertainties in the fit model used to extract ${\sin}^2{\theta}_{\textrm{eff}}^{\ell }$ from the asymmetry measurement. This result is based on an arithmetic average of results using the CT18, MSHT20, and NNPDF31 parameterisations of the proton internal structure, and is consistent with previous measurements and with predictions from the global electroweak fit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Dark Energy Survey Year 6 results: cell-based coadds and METADETECTION weak lensing shape catalogue

We present the metadetection weak lensing galaxy shape catalogue from the 6-yr Dark Energy Survey (DES Y6) imaging data. This data set is the final release from DES, spanning 4422 deg 2 of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition, and shear measurements with metadetection. The DES Y6 M etadetection weak lensing shape catalogue consists of 151 922 791 galaxies detected over riz bands, with an effective number density of n eff = 8.22 galaxies per arcmin 2 and shape noise of σ e = 0.29. We carry out a suite of validation tests on the catalogue, including testing for point spread function (PSF) leakage, testing for the impact of PSF modelling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-per cent level, with m = (3.4 ± 6.1) x 10 –3 , at 3σ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Real-Space Multiple-Scattering Theory and Its Applications at Exascale

In recent decades, the ab initio methods based on density functional theory (DFT) (Hohenberg and Kohn 1964, Kohn and Sham 1965) have become a widely used tool in computational materials science, which allows theoretical prediction of physical properties of materials from the first principles and theoretical interpretation of new physical phenomena found in experiments. In the framework of DFT, the original problem that requires solving a quantum mechanical equation for a many-electron system is reduced to a one-electron problem that involves an electron moving in an effective field, while the effective field potential is made up of an electrostatic potential, also known as Hartree potential, arising from the electronic and ion charge distribution in space and an exchange–correlation potential, which is a function of the electron density and encapsulates the exchange and correlation effects of the many-electron system. Even though the exact functional form of the exchange-correlation potential is formally unknown, a local density approximation (LDA) or a generalized gradient approximation (GGA) is usually applied so that the calculation of the exchange–correlation potential, as well as the exchange–correlation energy, becomes tractable while a required accuracy is retained. Based on DFT, ab initio electronic structure calculations for a material generally involve a self-consistent process that iterates between two computational tasks: (1) solving an one-electron Schrödinger equation, also known as Kohn–Sham equation, to obtain the electron density and, if needed, the magnetic moment density, and (2) solving the Poisson equation to obtain the electrostatic potential corresponding to the electron density and constructing the effective potential by adding the exchange–correlation potential to the electrostatic potential. This self-consistent process proceeds until a convergence criteria is reached.

Eisenbach, Markus↗

DOE OSTI2024

Pressureless sintering of lithium hydride

Lithium Hydride is a material of growing importance for addressing technological challenges related to nuclear fusion, long-term human space travel, and thermal energy storage. Pressureless sintering provides a straightforward, scalable approach to produce dense LiH parts of all sizes and shapes. Pressed LiH green compacts were sintered at heating rates from 2.5 to 20 °C/min to 650 °C, yielding densities up to 96 ± 1.4 %, with densification initiating at 500 °C. Here, a validated master sintering curve was constructed with a sintering apparent activation energy of 135 kJ/mol. X-ray diffraction and simultaneous thermal analysis revealed Li 2 O formation from 300 – 550 °C and decomposition of LiH into Li metal at 550 °C, each reflected as deviations in the master sintering curve. Computed tomography after thermal treatment to 550 °C showed the formation of corrosion products, and after thermal treatment to 650 °C LiH reduction to Li most significantly at exposed surfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Study of second phase in bioabsorbable magnesium alloys: Phase stability evaluation via Dmol{sup 3} calculation

Thermodynamical stabilities of four conventional second phases as well as magnesium matrix in bioabsorbable magnesium alloys were investigated theoretically via computer calculation method. Model of individual phase and systems including phase and four water molecular (phase-4H{sub 2}O) were established to simulate the in vitro and in vivo environment. Local orbital density functional theory approach was applied to calculate the total energy for the individual phase and phase-4H{sub 2}O system. The results demonstrated that all the second phases possessed higher phase stability compared with magnesium matrix, but the phase stability was quite different for different types of second phases or second phase-4H{sub 2}O systems. Furthermore, a schematic process of inflammation reaction caused by magnesium alloy implants was proposed for the further evaluation on biocompatibility of different second phases.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Full-scale computation for all the thermoelectric property parameters of half-Heusler compounds

The thermoelectric performance of materials relies substantially on the band structures that determine the electronic and phononic transports, while the transport behaviors compete and counter-act for the power factor PF and figure-of-merit ZT. These issues make a full-scale computation of the whole set of thermoelectric parameters particularly attractive, while a calculation scheme of the electronic and phononic contributions to thermal conductivity remains yet challenging. In this work, we present a full-scale computation scheme based on the first-principles calculations by choosing a set of doped half- Heusler compounds as examples for illustration. The electronic structure is computed using the WIEN2k code and the carrier relaxation times for electrons and holes are calculated using the Bardeen and Shockley’s deformation potential (DP) theory. The finite-temperature electronic transport is evaluated within the framework of Boltzmann transport theory. In sequence, the density functional perturbation combined with the quasi-harmonic approximation and the Klemens’ equation is implemented for calculating the lattice thermal conductivity of carrier-doped thermoelectric materials such as Tidoped NbFeSb compounds without losing a generality. The calculated results show good agreement with experimental data. Lastly, the present methodology represents an effective and powerful approach to calculate the whole set of thermoelectric properties for thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Emergence of local scaling relations in adsorption energies on high-entropy alloys

Alloying has been proposed to circumvent scaling relations between the adsorption energies thus allowing for the complete optimization of multistep reactions. Herein the fidelity of scaling rules on high-entropy alloy (HEA) surfaces is assessed focusing on hydrogen-containing molecules, *AH x for A = C and N (x = 0, 1, 2, 3), A = S (x = 0, 1, 2) and A = O (x = 0, 1). Using an adsorbate- and site-specific deep learning model to rapidly compute the adsorption energies on CoMoFeNiCu HEA surfaces, the energies of *AH x and *A are shown to be linearly correlated if *A and *AH x have identical adsorption site symmetry. However, a local linear dependence emerges between the configuration-averaged adsorption energies irrespective of the site symmetry. Although these correlations represent a weaker form of the scaling relationships, they are sufficient to prohibit the optimization of multistep reactions. The underpinning of this behavior is twofold (1) the nearsightedness principle and (2) the narrow distribution of the adsorption energies around the mean-field value. While the nearsightedness is general for all electronic systems, the second criterion applies in HEAs with relatively strong reactive elements. The present findings strongly suggest that alloys may not generally enable the breaking of scaling relationships.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

Few hydrogen adsorbents balance high usable volumetric and gravimetric capacities. Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing this gap, the large number of MOFs has hindered the identification of optimal materials. Here, a systematic assessment of published databases of real and hypothetical MOFs is presented. Nearly 500,000 compounds were screened computationally, and the most promising were assessed experimentally. Three MOFs with capacities surpassing that of IRMOF-20, the record-holder for balanced hydrogen capacity, are demonstrated: SNU-70, UMCM-9, and PCN-610/NU-100. Analysis of trends reveals the existence of a volumetric ceiling at ~ 40 g H 2 L -1 . Surpassing this ceiling is proposed as a new capacity target for hydrogen adsorbents. Counter to earlier studies of total hydrogen uptake in MOFs, usable capacities in the highest-capacity materials are negatively correlated with density and volumetric surface area. Instead, capacity is maximized by increasing gravimetric surface area and porosity. This suggests that property/performance trends for total capacities may not translate to usable capacities.

36 MATERIALS SCIENCE↗

DOE OSTI2018

The 2019 materials by design roadmap

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. Increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. Perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this roadmap will guide researchers and funding agencies in identifying new prospects for materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modeling antiphase boundary energies of Ni 3 Al-based alloys using automated density functional theory and machine learning

Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni 3 Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m –2 . We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.

36 MATERIALS SCIENCE↗