Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Materials Modeling, Density Functional Theory, Computational Materials Science”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Giant linear magnetoelectric effect at the morphotropic phase boundary of epitaxial Sr 0.5 Ba 0.5 MnO 3 films

First-principles calculations are conducted to compute linear magnetoelectric coupling coefficients in epitaxial (001) Sr 0.5 Ba 0.5 MnO 3 films. A large enhancement of different linear magnetoelectric elements is found in a strained-induced morphotropic phase boundary region. Such enhancement is demonstrated to originate from the behavior of the dielectric susceptibility, thanks to a simple phenomenological model that is presently shown to be relevant and accurate. Finally, this work can thus provide a promising approach towards designing highly desired single-phase multiferroic with a colossal magnetoelectric conversion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Parameter-free prediction of phase transition in PbTiO 3 through combination of quantum mechanics and statistical mechanics

Thermodynamics of ferroelectric materials and their ferroelectric to paraelectric (FE-PE) transitions is commonly described by the phenomenological Landau theory and more recently by effective Hamiltonian and various potentials, all with model parameters fitted to experimental or theoretical data. Here, in this work, we show that the zentropy theory, which considers the total entropy of a system as a weighted sum of entropies of configurations that the system may experience and the statistical entropy among the configurations, can predict the FE-PE transition without fitting parameters. For PbTiO 3 , the configurations are identified as the FE configurations with 90- or 180° domain walls in addition to the ground state FE configuration without domain wall. With the domain wall energies predicted from first-principles based on density functional theory in the literature as the only inputs, the FE-PE transition for PbTiO 3 is predicted showing remarkable agreement with experiments, unveiling the microscopic fundamentals of the transition.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulated Adhesive Response to Aging (CRADA 453 Final Report)

The goal of this project was to develop a suite of molecular models to investigate adhesive performance as a result of the presence of different levels of moisture. These different levels of moisture are accepted in the adhesives community to represent different states of aging. First, our team modeled the adhesive’s crosslinked structure. Next, we modeled adding water to the crosslinked structure and used high performance computing resources to simulate water diffusion through the bulk adhesive and possible sites for chain scission. Finally, we modeled the response of the simulated adhesive to tensile loads at various levels of moisture content. These simulations were validated by experimental characterization by the industry partner. We found that the Young’s modulus of the adhesive decreased by about 30% when the water content increased to 3 wt.%. Our simulations also identified the likely site where the polymer chain could break. Our integration of molecular level simulations and mechanical property predictions using high performance computing with experimental studies of a hydrated epoxy system has yielded fundamental insights that will help industry advance lightweight joining technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Majorana modes with side features in magnet-superconductor hybrid systems

Magnet-superconductor hybrid (MSH) systems represent promising platforms to host Majorana zero modes (MZMs), the elemental building blocks for fault-tolerant quantum computers. Theoretical description of such MSH structures is mostly based on simplified models, not accounting for the complexity of real materials. Here, based on density functional theory, we derive a superconducting 80-band model to study an MSH system consisting of a magnetic manganese chain on the s wave superconductor niobium. For a wide range of values of the superconducting order parameter, the system is a topological superconductor, with MZMs exhibiting non-universal spatial patterns and a drastic accumulation of spectral weight on both sides along the magnetic chain. These side feature states can be explained by an effective model which is guided by the ab initio results. Performing scanning tunneling spectroscopy experiments on the same system, we observe a spatial structure in the low-energy local density of states that is consistent with the theoretical findings. Our results open a first-principle approach to the discovery of topological superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additively manufactured Ni-20Cr to V functionally graded material: Computational predictions and experimental verification of phase formations

Throughout this work, a database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and elemental V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing the amount of V across the gradient resulted in the formation of sigma phase, followed by the bcc phase. The experimentally measured phase data was compared with computational predictions made using a Cr-Ni-V thermodynamic database from the literature as well as the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing Oxidation-Driven Amorphized Surfaces in a Ta(110) Film for Superconducting Qubit

Recent advances in superconducting qubit technology have led to significant progress in quantum computing, but the challenge of achieving a long coherence time remains. Despite the excellent lifetime performance that tantalum (Ta) based qubits have demonstrated to date, the majority of superconducting qubit systems, including Ta-based qubits, are generally believed to have uncontrolled surface oxidation as the primary source of the two-level system loss in two-dimensional transmon qubits. Therefore, atomic-scale insight into the surface oxidation process is needed to make progress toward a practical quantum processor. Here, in this study, the surface oxidation mechanism of native Ta films and its potential impact on the lifetime of superconducting qubits were investigated using advanced scanning transmission electron microscopy (STEM) techniques combined with density functional theory calculations. The results suggest an atomistic model of the oxidized Ta(110) surface, showing that oxygen atoms tend to penetrate the Ta surface and accumulate between the two outermost Ta atomic planes; oxygen accumulation at the level exceeding a 1:1 O/Ta ratio drives disordering and, eventually, the formation of an amorphous Ta 2 O 5 phase. In addition, we discuss how the formation of a noninsulating ordered TaO 1–δ (δ < 0.1) suboxide layer could further contribute to the losses of superconducting qubits. Subsurface oxidation leads to charge redistribution and electric polarization, potentially causing quasiparticle loss and decreased current-carrying capacity, thus affecting superconducting qubit coherence. The findings enhance the comprehension of the realistic factors that might influence the performance of superconducting qubits, thus providing valuable guidance for the development of future quantum computing hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GradDFT. A software library for machine learning enhanced density functional theory

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy.

Chemistry↗
DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Characterization of the atomic-level structure of γ-alumina and (111) Pt/γ-alumina interfaces

The atomic-level structure of platinum/γ-alumina interfaces is characterized in a model system of dense γ-alumina embedded with faceted Pt NPs produced by implantation of platinum ions into sapphire followed by thermal annealing in air at 800 °C. Aberration-corrected scanning transmission electron microscopy (STEM) was used to collect atomic-resolution images, which are compared to STEM image simulations of two experimentally-based bulk models of γ-alumina by Smrčok et al. and Zhou and Snyder. A density functional theory (DFT) based model of (111) interfaces with different chemical terminations (O, Al 1 , Al 2 ) of the γ-alumina developed by Oware Sarfo et al. is also compared to experimental STEM data from Pt/γ-alumina interfaces. Further, the Smrčok γ-alumina model provides a better fit than the Zhou structure to the bulk of the γ-alumina. The oxygen-terminated Oware Sarfo model best fits the experimental data and is a very good model close to the interface. However, the fit of the interface model to the experimental data is poorer beyond the third atomic layer in the γ-alumina. This is attributed to compromises required in the design of the model to limit the cell size and computational time for DFT calculations. Understanding the accuracy and limits of the structural models of γ-alumina and Pt/γ-alumina interfaces is important to further the understanding of the structure/property relationships in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CrRhAs: a member of a large family of metallic kagome antiferromagnets

Abstract Kagome lattice materials are an important platform for highly frustrated magnetism as well as for a plethora of phenomena resulting from flat bands, Dirac cones and van Hove singularities in their electronic structures. We study the little known metallic magnet CrRhAs, which belongs to a vast family of materials that includes 3 d , 4 f , and 5 f magnetic elements, as well as numerous nonmagnetic metals and insulators. Using noncollinear spin density functional calculations (mostly spin spirals), we extract a model magnetic Hamiltonian for CrRhAs. While it is dominated by an antiferromagnetic second nearest neighbor coupling in the kagome plane, the metallic nature of the compound leads to numerous nonzero longer range couplings and to important ring exchange terms. We analyze this Hamiltonian and find unusual ground states which are dominated by nearly isolated antiferromagnetic triangles that adopt 120 ∘ order either with positive or with negative vector chirality. We discuss the connection to the few known experimental facts about CrRhAs. Finally, we give a brief survey of other interesting magnetic members of this family of kagome compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An open database of computed bulk ternary transition metal dichalcogenides

Abstract We present a dataset of structural relaxations of bulk ternary transition metal dichalcogenides (TMDs) computed via plane-wave density functional theory (DFT). We examined combinations of up to two chalcogenides with seven transition metals from groups 4–6 in octahedral (1T) or trigonal prismatic (2H) coordination. The full dataset consists of 672 unique stoichiometries, with a total of 50,337 individual configurations generated during structural relaxation. Our motivations for building this dataset are (1) to develop a training set for the generation of machine and deep learning models and (2) to obtain structural minima over a range of stoichiometries to support future electronic analyses. We provide the dataset as individual VASP xml files as well as all configurations encountered during relaxations collated into an ASE database with the corresponding total energy and atomic forces. In this report, we discuss the dataset in more detail and highlight interesting structural and electronic features of the relaxed structures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A physics-based model of cladding wastage layer formation rate

In this report, lower length scale simulations to inform an engineering-scale model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY22 are described. An overall strategy for the implementation of the BISON model is described, and the past and current lower length scale work on FCCI formation is put into the context of this overall strategy. Density functional theory and kinetic Monte Carlo simulations are used to determine the diffusion coefficient of neodymium in iron. Density functional theory calculations are also used to parameterize a parabolic approximation for the dependence of free energy on composition for the intermetallic compound Fe 17 Nd 2 . A phase-field model of the Fe-Nd system was developed that includes the random solid solution body-centered cubic phase and the intermetallic Fe 17 Nd 2 . The model was used to simulate growth of the intermetallic layer in a diffusion couple and the results were compared to experiment. The model’s prediction of the parabolic growth constant is within 40 % of the experimental value. The model is also used to simulate the formation of a grain boundary attack layer that is observed in experiment. The simulations show enhanced Nd concentration along the grain boundaries, but do not clearly show formation of a Fe 17 Nd 2 layer

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Theoretical framework for new magnetic materials for quantum computing and information storage. Final report for the Award No. DE-SC0018910

The focus of this grant was on molecular magnetic materials for information storage and quantum computing. We have been developing robust, first-principle methods for computing relevant electronic and magnetic properties of molecular building blocks (SMMs) of novel magnetic materials and quantum computers. These tools enable theoretical modeling of SMMs’ behavior, facilitating the interpretation of experimental studies and aiding the design of novel magnetic materials. Our strategy is based on the spin-flip (SF) approach, which extends the hierarchy of black-box single-reference methods to strongly correlated systems. Specifically, we developed general scalable algorithms and computer codes for calculating molecular properties, with an emphasis on spin-related properties, such as zero-field splittings, hyperfine couplings, and g-tensors. While our primary focus was on SF wave functions and SF-TDDFT, the underlying theory and computer codes were formulated using reduced density matrices, such that these tools are applicable to a broader class of methods. To extend the scope of applicability of wave-function-based SF methods to larger systems, we developed reduced-scaling approaches for the equation-of-motion coupled-cluster (EOM-CC) methods and continue developing libtensor (our open-source general tensor contraction library for many-body methods). We carried out extensive benchmarks and also carried out several applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Energy Frontier Research Centers: Center for the Computational Design of Functional Layered Materials (CCDM) August 1, 2014 - July 31, 2018; Center for Complex Materials from First Principles (CCM) August 1, 2018 - July 31, 2021 (Final Report)

The mission of the DOE Energy Frontier Research Centers CCDM (2014-2018) and CCM (2018-2021) was to theoretically develop, computationally apply, and experimentally validate electronic structure methods for all materials, with a focus on the complex materials, especially layered and two-dimensional materials, strongly-correlated materials, and liquid water. This was achieved by over 200 published journal articles authored by about 17 senior investigators from physics and chemistry and from theory, computation, and experiment, plus their collaborators. In particular, the Centers confirmed the predictive power of the SCAN (strongly constrained and appropriately normed) density functional, which was constructed to satisfy 17 known exact constraints and several appropriate norms. Without being fitted to real bonded systems, and at a modest computational cost, SCAN correctly predicted covalent, ionic, metallic, hydrogen, and van der Waals bonds in many challenging materials. SCAN gave an improved description of defects in semiconductors, surface properties of metals, seven phases of ice, liquid water, liquid and supercooled silicon, subtle structural distortions in ferroelectrics, formation energies and structural predictions for solids, and critical pressures for structural phase transitions. Perhaps most remarkably, SCAN correctly described some strongly-correlated materials that were previously believed to be beyond the reach of density-functional approximations. SCAN is the only density functional that correctly predicts the band gap closing under chemical doping of the cuprate high-temperature superconducting materials. SCAN also predicts a landscape of competing stripe and magnetic phases in the cuprates. For some materials with some codes, SCAN has convergence problems that are greatly reduced by the CCM-developed r 2 SCAN, without loss of accuracy or rigor. SCAN and r 2 SCAN still make some self-interaction error, which is greatly reduced by the CCDM/ CCM-developed local orbital scaling correction (LOSC). These Centers further proved that the fundamental energy gaps of a solid from an orbital energy difference and from total energy differences are the same for a large class of generalized Kohn-Sham (GKS) functionals, including SCAN and standard hybrid functionals, and that symmetry breaking arises when a dynamic density fluctuation drops to zero frequency. The Centers identified new mechanisms for catalysis in layered materials with ions intercalated between the layers, investigated charge density waves both in model systems and in real layered materials, studied changes of band gap with the number of layers, and explored topological ultrathin films, bent nanoribbons, and defects.

08 HYDROGEN↗