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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance of sweeping gas membrane distillation for treating produced water: Modeling and experiments

Membrane distillation (MD) has shown promise for desalination of produced water (PW), but there are very few studies reported on the performance of MD with real PW and the characteristics of the fouling and scaling in MD. Among different MD configurations, sweeping gas MD (SGMD) is the least studied configuration. Here we report the application of SGMD for treating high-salinity PW (123 g L -1 ). The SGMD operating conditions were first carefully optimized through pure water MD experiments by varying operating conditions. The flux evaluation was accompanied by a detailed Aspen Custom Modeler simulation based on the electrolyte-NRTL thermodynamic model to better understand the heat and mass transfer in the feed and permeate channel. Supported by experiments, modeling results confirmed the partial condensation of the vapor in the permeate channel. Furthermore, it was concluded that the saturation of the sweeping gas is controlling the overall mass transfer through the membrane. Selected membranes and optimized conditions were then used to conduct short-term and long-term performance tests with PW. In long-term MD test, the concentration of the PW increased by a factor of two (50% water recovery), while the membrane exhibited small flux decline. Elemental analysis showed that strontium and sodium were the major scalant compounds.

42 ENGINEERING↗
DOE OSTI2024

Model associated with: "Thermodynamic control on the decomposition of organic matter across different electron acceptors"

This model data package is associated with the publication “Thermodynamic control on the decomposition of organic matter across different electron acceptors” submitted to Soil Biology and Biochemistry (Zheng et al., 2023; https://doi.org/10.1016/j.soilbio.2024.109364).In this research, a thermodynamic modeling framework is built to flexibly incorporate both organic matter (OM) molecules and electron acceptors for estimating potential free energy release from various redox reactions and to further predict reaction rates based on Microbial Transition State Theory. The model package includes scripts for thermodynamic modeling and postprocessing. Input Fourier-transform ion cyclotron resonance (FTICR) data are from a previous experimental study (Boye et al., 2018), and model outputs are free energy predictions and stoichiometric coefficients associated with all possible redox reactions.This data package is associated with the project GitHub repository found at MM_bioenergetic_modeling.This data package contains four folders (Input_FTICR, Model, Output, and Output_processing), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Zheng_bioenergetic_modeling_flmd.csv for a list of all files contained in this data package and descriptions for each. The Zheng_bioenergetic_modeling_dd.csv file describes the csv column headers. The “Model” folder contains scripts to run energy balance calculations for each electron acceptor. The “Output” folder contains csv files with stoichiometric information from model simulations. And the "Output_processing" folder contains scripts for reaction rate calculations and to generate plots.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Estimation of abatement potentials and costs of air pollution emissions in China

Understanding the air pollution emission abatement potential and associated control cost is a prerequisite to design cost ef?cient control policies. In this study, a linear programming algorithm model, International Control Cost Estimate Tool, was updated with cost data for applications of 56 types of end-of-pipe technologies and ?ve types of renewable energy in 10 major sectors namely power generation, industry combustion, cement pro-duction, iron and steel production, other industry processes, domestic combustion, transportation, solvent use, livestock rearing, and fertilizer use. The updated model was implemented to estimate the abatement potential and marginal cost of multiple pollutants in China. The total maximum abatement potentials of sulfur dioxide (SO2), nitrogen oxides (NOx), primary particulate matter (PM2.5), non-volatile organic compounds (NMVOCs), and ammonia (NH3) in China were estimated to be 19.2, 20.8, 9.1, 17.2 and 8.6 Mt, respectively, which accounted for 89.7%, 89.9%, 94.6%, 74.0%, and 80.2% of their total emissions in 2014, respectively. The associated control cost of such reductions was estimated as 92.5, 469.7, 75.7, 449.0, and 361.8 billion CNY in SO2, NOx, primary PM2.5, NMVOCs and NH3, respectively. Shandong, Jiangsu, Henan, Zhejiang, and Guangdong provinces exhibited large abatement potentials for all pollutants. Provincial disparity analysis shows that high GDP regions tend to have higher reduction potential and total abatement costs. End-of-pipe technologies tended be a cost-ef?cient way to control pollution in industries processes (i.e., cement plants, iron and steel plants, lime production, building ceramic production, glass and brick production), whereas such technologies were less cost- effective in fossil fuel-related sectors (i.e., power plants, industry combustion, domestic combustion, and transportation) compared with renewable energy. The abatement potentials and marginal abatement cost curves developed in this study can further be used as a crucial component in an integrated model to design optimized cost-ef?cient control policies.

Zhang, Fenfen↗
DOE OSTI2020

Linear in temperature resistivity in the limit of zero temperature from the time reparameterization soft mode

The most puzzling aspect of the ‘strange metal’ behavior of correlated electron compounds is that the linear in temperature resistivity often extends down to low temperatures, lower than natural microscopic energy scales. We consider recently proposed deconfined critical points (or phases) in models of electrons in large dimension lattices with random nearest-neighbor exchange interactions. The criticality is in the class of Sachdev–Ye–Kitaev models, and exhibits a time reparameterization soft mode representing gravity in dual holographic theories. We compute the low temperature resistivity in a large M limit of models with SU(M) spin symmetry, and find that the dominant temperature dependence arises from this soft mode. The resistivity is linear in temperature down to zero temperature at the critical point, with a co-efficient universally proportional to the product of the residual resistivity and the co-efficient of the linear in temperature specific heat. We argue that the time reparameterization soft mode offers a promising and generic mechanism for resolving the strange metal puzzle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Plant myo -inositol transport influences bacterial colonization phenotypes

Plant microbiomes are assembled and modified through a complex milieu of biotic and abiotic factors. Despite dynamic and fluctuating contributing variables, specific host metabolites are consistently identified as important mediators of microbial interactions. We combine information from a large-scale metatranscriptomic dataset from natural poplar trees and experimental genetic manipulation assays in seedlings of the model plant Arabidopsis thaliana to converge on a conserved role for transport of the plant metabolite myo-inositol in mediating host-microbe interactions. While microbial catabolism of this compound has been linked to increased host colonization, we identify bacterial phenotypes that occur in both catabolism-dependent and -independent manners, suggesting that myo-inositol may additionally serve as a eukaryotic-derived signaling molecule to modulate microbial activities. Our data suggest host control of this compound and resulting microbial behavior are important mechanisms at play surrounding the host metabolite myo-inositol.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Protein–Ligand Binding Free-Energy Calculations with ARROW–A Purely First-Principles Parameterized Polarizable Force Field

Protein–ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)–a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein–ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ~0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

NaRuO2: Kitaev-Heisenberg exchange in triangular-lattice setting

Abstract Kitaev exchange, a new paradigm in quantum magnetism research, occurs for 90° metal-ligand-metal links, $${t}_{2g}^{5}$$ t 2 g 5 transition ions, and sizable spin-orbit coupling. It is being studied in honeycomb compounds but also on triangular lattices. While for the former it is known by now that the Kitaev intersite couplings are ferromagnetic, for the latter the situation is unclear. Here we pin down the exchange mechanisms and determine the effective coupling constants in the $${t}_{2g}^{5}$$ t 2 g 5 triangular-lattice material NaRuO 2 , recently found to host a quantum spin liquid ground state. We show that, compared to honeycomb compounds, the characteristic triangular-lattice cation surroundings dramatically affect exchange paths and effective coupling parameters, changing the Kitaev interactions to antiferromagnetic. Quantum chemical analysis combined with subsequent effective spin model simulations provide perspective onto the nature of the experimentally observed quantum spin liquid—it seemingly implies fairly large antiferromagnetic second-neighbor isotropic exchange, and the atypical proximity to ferromagnetic order is related to ferromagnetic nearest-neighbor Heisenberg coupling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Magnetic states of the quasi-one-dimensional iron chalcogenide Ba 2 FeS 3

Quasi-one-dimensional iron-based ladders and chains, with the 3d iron electronic density n=6, are attracting considerable attention. Recently, a new iron chain system Ba 2 FeS 3 , also with n=6, was prepared under high-pressure and high-temperature conditions. Here the magnetic and electronic phase diagrams are theoretically studied for this quasi-one-dimensional compound. Based on first-principles calculations, a strongly anisotropic one-dimensional electronic band behavior near the Fermi level was observed. In addition, a three-orbital electronic Hubbard model for this chain was constructed. Introducing the Hubbard and Hund couplings and studying the model via the density matrix renormalization group (DMRG) method, we studied the ground-state phase diagram. A robust staggered ↑-↓-↑-↓ AFM region was unveiled in the chain direction, consistent with our density functional theory (DFT) calculations. Furthermore, at intermediate Hubbard U coupling strengths, this system was found to display an orbital selective Mott phase (OSMP) with one localized orbital and two itinerant metallic orbitals. At very large U/W (W=bandwidth), the system displays Mott insulator characteristics, with two orbitals half-filled and one doubly occupied. Our results for high pressure Ba 2 FeS 3 provide guidance to experimentalists and theorists working on this one-dimensional iron chalcogenide chain material.

1-dimensional systems↗
DOE OSTI2021

3,3’-Diindolylmethane Exhibits Significant Metabolism After Oral Dosing in Humans

3,3’-Diindolylmethane (DIM), a major phytochemical derived from ingestion of cruciferous vegetables such as Brussels sprouts, is also a dietary supplement. In preclinical models, DIM is an effective cancer chemopreventive agent and has been studied in a number of clinical trials. Previous pharmacokinetic studies in preclinical and clinical models have not reported DIM metabolites in plasma or urine following oral dosing and the pharmacological actions of DIM on target tissues is assumed to be solely via the parent compound. Seven subjects (6 males and 1 female) ranging from 26-65 years of age, on a cruciferous vegetable-restricted diet prior to and during the study, took 2 BioResponse-DIM® 150 mg capsules (45.3 mg DIM/capsule) in the evening for one week with a final dose the morning of the first blood draw. A complete time course was performed with plasma and urine collected over 48 hours and analyzed by UPLC-MS/MS. In addition to parent DIM, two mono-hydroxylated (2-ox- and 3-methylenehydroxy-DIM) and 1 dihydroxylated (3-methylenehydroxy-2-ox-DIM) metabolites, along with their sulfate and glucuronide conjugates, were present in both plasma and urine. The results reported here are indicative of significant phase 1 and phase 2 metabolism and differ from previous pharmacokinetic studies in rodents and humans which reported only parent DIM present following oral administration.

Vermillion-Maier, Monica↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
DOE OSTI2021

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R 2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Frequency-domain hot-wire sensor and 3D model for thermal conductivity measurements of reactive and corrosive materials at high temperatures

High temperature solids and liquids are becoming increasingly important in next-generation energy and manufacturing systems that seek higher efficiencies and lower emissions. Accurate measurements of thermal conductivity at high temperatures are required for the modeling and design of these systems, but commonly employed time-domain measurements can have errors from convection, corrosion, and ambient temperature fluctuations. Here, we describe the development of a frequency-domain hot-wire technique capable of accurately measuring the thermal conductivity of solid and molten compounds from room temperature up to 800 °C. Therefore, by operating in the frequency-domain, we can lock into the harmonic thermal response of the material and reject the influence of ambient temperature fluctuations, and we can keep the probed volume below 1 µl to minimize convection. The design of the microfabricated hot-wire sensor, electrical systems, and insulating wire coating to protect against corrosion is covered in detail. Furthermore, we discuss the development of a full three-dimensional multilayer thermal model that accounts for both radial conduction into the sample and axial conduction along the wire and the effect of wire coatings. The 3D, multilayer model facilitates the measurement of small sample volumes important for material development. A sensitivity analysis and an error propagation calculation of the frequency-domain thermal model are performed to demonstrate what factors are most important for thermal conductivity measurements. Finally, we show thermal conductivity measurements including model data fitting on gas (argon), solid (sulfur), and molten substances over a range of temperatures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

National Energy Water Treatment & Speciation (NEWTS): A Water & Critical Mineral Database and Dashboard

The scarcity of water resources, the need for beneficial water reuse, and the challenges of wastewater treatment are becoming increasingly pressing in economic, social, and environmental domains. Addressing these concerns requires effective treatment strategies to manage wastewater streams and tackle environmental and economic issues. Furthermore, the recovery of critical minerals from the waste streams associated with energy production holds the promise of offsetting treatment costs and securing local sources of valuable minerals. However, relevant data on these waste streams are dispersed and challenging to locate. The process of ingesting such data into modeling software often involves multiple steps, requiring data restructuring to meet software-input requirements. The non-standardized reporting of water data makes data aggregation and reformatting a time-consuming process. Additionally, essential attributes necessary for modeling water treatment and mineral scale formation are frequently missing. Moreover, data gaps vary depending on the region of interest. Consequently, there is a pressing need for high-quality energy-water composition data that can be easily imported into water chemistry modeling software. To address this need, the National Energy Technology Laboratory has created the National Energy Water Treatment and Speciation (NEWTS) Database and Dashboard—a free online tool catering to community leaders and water researchers. NEWTS facilitates a comprehensive understanding of the composition of energy-related wastewater streams in the United States. The datasets provide detailed concentrations and speciation of major and minor aqueous compounds in energy-related wastewater streams, including power plant leachate, acid mine drainage, brackish water, and oil and gas produced water across the United States. Many of the aqueous species are critical minerals (Li, REEs) in high demand to modernize the world’s energy infrastructure. Many of the datasets also contain volumetric flow-rates needed to model the treatment and reuse scenarios in advanced aqueous chemistry software programs. The NEWTS Database and Dashboard offer public access to hitherto challenging-to-access datasets, presented in a standardized format that is tailored for easy input into aqueous chemistry modeling software. By performing the work needed to transform dispersed, disparate data sources into unified, model-ready datasets, NEWTS serves as an essential resource in advancing water treatment research and sustainable water resource management.

produced water management↗
DOE OSTI2024

Chemical Differences between Phenolic Secondary Organic Aerosol Formed through Gas-Phase and Aqueous-Phase Reactions

Phenolic compounds, which are significant emissions from biomass burning (BB), undergo rapid photochemical reactions in both gas and aqueous phases to form secondary organic aerosol, namely, gasSOA and aqSOA, respectively. The formation of gasSOA and aqSOA involves different reaction mechanisms, leading to different product distributions. In this study, we investigate the gaseous and aqueous reactions of guaiacol a representative BB phenol to elucidate the compositional differences between phenolic aqSOA and gasSOA. Aqueous-phase reactions of guaiacol produce higher SOA yields than gas-phase reactions (e.g., roughly 60 vs 30% at one half-life of guaiacol). These aqueous reactions involve more complex reaction mechanisms and exhibit a more gradual SOA evolution than their gaseous counterparts. Initially, gasSOA forms with high oxidation levels (O/C > 0.82), while aqSOA starts with lower O/C (0.55–0.75). However, prolonged aqueous-phase reactions substantially increase the oxidation state of aqSOA, making its bulk chemical composition closer to that of gasSOA. Additionally, aqueous reactions form a greater abundance of oligomers and high-molecular-weight compounds, alongside a more sustained production of carboxylic acids. AMS spectral signatures representative of phenolic gasSOA have been identified, which, together with tracer ions of aqSOA, can aid in the interpretation of field observation data on aerosol aging within BB smoke. The notable chemical differences between phenolic gasSOA and aqSOA highlighted in this study also underscore the importance of accurately representing both pathways in atmospheric models to better predict the aerosol properties and their environmental impacts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

HT Model Dataset

This website contains the dataset that was used for writing the manuscript "HT Model: Using the Molecular Transformer for predicting hydrotreating reactions" (PNNL-SA-186589) The dataset includes a collection of hydrotreating reactions compiled from 41 peer-reviewed literature sources. These sources contain experimental data related to hydrotreating reactions. These reactions involve the reaction of chemical compounds with hydrogen gas in the presence of a catalyst to remove heteroatoms or to convert specific functional groups. The dataset contains reactions both with and without reaction conditions. Reaction conditions refer to the specific parameters under which the reaction takes place, such as temperature and pressure. For each reaction, the dataset includes both SMILES and SELFIES representations. SMILES (Simplified Molecular Input Line Entry System) and SELFIES (SELF-referencIng Embedded Strings) are two popular notations used to represent chemical structures in a compact and standardized format. The dataset was created with the aim of training a predictive model, specifically using the Molecular Transformer architecture.

bioprocessing, hydrotreating, deep learning algori↗
DOE OSTI2023

Oxamniquine derivatives overcome Praziquantel treatment limitations for Schistosomiasis

Human schistosomiasis is a neglected tropical disease caused by Schistosoma mansoni, S. haematobium, and S. japonicum. Praziquantel (PZQ) is the method of choice for treatment. Due to constant selection pressure, there is an urgent need for new therapies for schistosomiasis. Previous treatment of S. mansoni included the use of oxamniquine (OXA), a drug that is activated by a schistosome sulfotransferase (SULT). Guided by data from X-ray crystallography and Schistosoma killing assays more than 350 OXA derivatives were designed, synthesized, and tested. We were able to identify CIDD-0150610 and CIDD-0150303 as potent derivatives in vitro that kill (100%) of all three Schistosoma species at a final concentration of 71.5 μM. We evaluated the efficacy of the best OXA derivates in an in vivo model after treatment with a single dose of 100 mg/kg by oral gavage. The highest rate of worm burden reduction was achieved by CIDD -150303 (81.8%) against S. mansoni, CIDD-0149830 (80.2%) against S. haematobium and CIDD-066790 (86.7%) against S. japonicum. We have also evaluated the ability of the derivatives to kill immature stages since PZQ does not kill immature schistosomes. CIDD-0150303 demonstrated (100%) killing for all life stages at a final concentration of 143 μM in vitro and effective reduction in worm burden in vivo against S. mansoni. To understand how OXA derivatives fit in the SULT binding pocket, X-ray crystal structures of CIDD-0150303 and CIDD-0150610 demonstrate that the SULT active site will accommodate further modifications to our most active compounds as we fine tune them to increase favorable pharmacokinetic properties. Treatment with a single dose of 100 mg/kg by oral gavage with co-dose of PZQ + CIDD-0150303 reduced the worm burden of PZQ resistant parasites in an animal model by 90.8%. Therefore, we conclude that CIDD-0150303, CIDD-0149830 and CIDD-066790 are novel drugs that overcome some of PZQ limitations, and CIDD-0150303 can be used with PZQ in combination therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides

The computational design of suitable multiferroic double perovskite oxides requires finding materials that exhibit sizable polarization, magnetization, and coupling between them. Oxides with the chemical formula of AA'BB'O 6 with building blocks of ABO 3 single perovskite oxides in centrosymmetric Pnma symmetry are strong candidates that have been reported to satisfy such criteria. The system lowers to noncentrosymmetric, polar P2 1 symmetry if A/A' layered and B/B' rocksalt cation orderings are imposed. A detailed compositional search over a variety of chemical spaces followed by evaluating their polarization may lead to the identification of more of these compounds with ferroelectric ordering. The standard density functional theory practices to estimate polarization within the Berry phase formalism require the systems to be perfectly insulating. The number of compounds that can be evaluated using this method is therefore limited. In this work, we introduce a predictive learning strategy based on importance sampling to build a series of machine learning models using results from first-principles simulations to predict polarization and the corresponding switching barrier. The geometry-driven features related to charge states and cationic radii play key roles in predicting the switching barrier with complementary contributions from the key structural mode-based order parameters. These modes become important to draw reasonable predictions of polarization components from machine learning models. In conclusion, our predictive models identify candidates with high polarizations and low switching barriers from a pool of double perovskite oxides, suitable for future investigation for their potential applications in spintronic devices.

36 MATERIALS SCIENCE↗