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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of a Modified Embedded Atom Force Field for Zirconium Nitride Using Multi-Objective Evolutionary Optimization

Zirconium nitride (ZrN) exhibits exceptional mechanical, chemical, and electrical properties, which make it attractive for a wide range of technological applications, including wear-resistant coatings, protection from corrosion, cutting/shaping tools, and nuclear breeder reactors. Despite its broad usability, an atomic scale understanding of the superior performance of ZrN, and its response to external stimuli, for example, temperature, applied strain, and so on, is not well understood. This is mainly due to the lack of interatomic potential models that accurately describe the interactions between Zr and N atoms. To address this challenge, we develop a modified embedded atom method (MEAM) interatomic potential for the Zr–N binary system by training against formation enthalpies, lattice parameters, elastic properties, and surface energies of ZrN (and, in some cases, also Zr3N4) obtained from density functional theory (DFT) calculations. The best set of MEAM parameters are determined by employing a multiobjective global optimization scheme driven by genetic algorithms. Our newly developed MEAM potential accurately reproduces structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of Zr–N compounds, in excellent agreement with DFT calculations and experiments. As a representative application, we employed molecular dynamics simulations based on this MEAM potential to investigate the atomic scale mechanisms underlying fracture of bulk and nanopillar ZrN under applied uniaxial strains, as well as the impact of strain rate on their mechanical behavior. These simulations indicate that bulk ZrN undergoes brittle fracture irrespective of the strain rate, while ZrN nanopillars show quasi-plasticity owing to amorphization at the crack front. The MEAM potential for Zr–N developed in this work is an invaluable tool to investigate atomic-scale mechanisms underlying the response of ZrN to external stimuli (e.g, temperature, pressure etc.), as well as other interesting phenomena such as precipitation.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

We present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2–1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based genetic algorithm and density functional theory (DFT) modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly nonmonotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement, and we propose the use of an additional descriptors—the fully frozen adsorption energy—that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Ternary Bismuthide SrPtBi 2 : Computation and Experiment in Synergism to Explore Solid-State Materials

A combination of theoretical calculation and the experimental synthesis to explore the new ternary compound is demonstrated in the Sr–Pt–Bi system. Because Pt–Bi is considered as a new critical charge-transfer pair for superconductivity, it inspired us to investigate the Sr–Pt–Bi system. With a thorough calculation of all the known stable/metastable compounds in the Sr–Pt–Bi system and crystal structure predictions, the thermodynamic stability of hypothetical stoichiometry, SrPtBi2, is determined. Following the high-temperature synthesis and crystallographic analysis, the first ternary bismuthide in Sr–Pt–Bi, SrPtBi2 was prepared, and the stoichiometry was confirmed experimentally. SrPtBi 2 crystallizes in the space group Pnma (S.G. 62, Pearson Symbol oP48), which matches well with theoretical prediction using an adaptive genetic algorithm. Using first-principles calculations, we demonstrate that the orthorhombic structure has lower formation energies than other 112 structure types, such as tetragonal BaMnBi 2 (CuSmP 2 ) and LaAuBi 2 (CuHfSi 2 ) structure types. The bonding analysis indicates that the Pt–Bi interactions play a critical role in structural stability. The physical property measurements show the metallic properties at the low temperature, which agrees with the electronic structure assessment.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The Impact of Coordination Environment on the Thermodynamic Stability of Uranium Oxides

Amorphous uranium oxides are acknowledged to arise via industrial processes relevant to the nuclear fuel cycle yet evade rigorous structural characterization. An encouraging approach is to develop statistical relationships between uranium–oxygen coordination environments and thermodynamic stability from which general statements about the likelihood of observing particular U–O arrangements can be made. The number of known crystalline uranium oxides is insufficient to build statistical relationships. We have developed a database of theoretical compounds using genetic algorithms with the density functional theory as a foundation to analyze coordination geometries in the uranium–oxygen phase space. We draw fundamental insights into the nature of uranium–oxygen interactions by correlating total energy with the coordination environment. The most stable configuration of U cations with O anions is in an environment with coordination numbers 5–8 in a cubic configuration. Higher and lower coordination numbers are observed only in metastable phases. General trends are observable in the relationships between the coordination number, density, and uranium fraction in each structure. The new insight into uranium coordination enabled by these analyses is foundational for further studies into the characteristic properties of individual uranium oxide materials and for elucidation of potential oxidation pathways for uranium metal.

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

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Elucidation of Bottom-up Growth of CaCO3 Involving Prenucleation Clusters from Structure Predictions and Decomposition of Globally Optimized (CaCO3)n Nanoclusters

Low-energy minima structures for (CaCO3)n, n = 28, are predicted using bottom-up genetic algorithms in conjunction with density functional theory electronic structure calculations, in comparison with the frozen and relaxed top-down clusters generated by cuts from the calcite, vaterite, and aragonite crystal structures. Similarities in structural motifs for the bottom-up and relaxed top-down are revealed using a fragment recognition technique. Fragment energy decomposition analysis shows that the bottom-up and relaxed top-down clusters belong to two classes of amorphous clusters with distinct intracluster energy distributions, despite their structural similarity. The bottom-up clusters with >20 formula units are surface stabilized with negative surface energy densities. In contrast, the top-down clusters are interior stabilized with positive surface energy densities. We prove that the sign of the surface energy density determines whether the nucleation reaction energy as a function of nuclear size has a maximum or a minimum. The surface-stabilized bottom-up clusters are proposed to be a type of prenucleation cluster at the minimum of the nucleation reaction energy. A mechanism for mineralization of CaCO3 involving prenucleation clusters and nonclassical growth pathway is proposed on the basis of our theoretical findings, which is consistent with previous titration experiments.

calcium carbonate, prenucleation cluster, biominer↗
DOE OSTI2023

Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane

Boron-containing materials, such as hexagonal boron nitride (h-BN), recently shown to be active and selective catalysts for the oxidative dehydrogenation of propane (ODHP), have been shown to undergo significant surface oxyfunctionalization and restructuring. Although experimental ex situ studies have probed the change in chemical environment on the surface, the structural evolution of it under varying reaction conditions has not been established. Herein, we perform global optimization structure search with a grand canonical genetic algorithm to explore the chemical space of off-stoichiometric restructuring of the h-BN surface under ambient as well as ODHP-relevant conditions. A grand canonical ensemble representation of the surface is established, and the predicted 11B solid-state NMR spectra are consistent with previous experimental reports. In addition, we investigated the relative sliding of h-BN sheets and how it influences the surface chemistry with ab initio molecular dynamics simulations. Furthermore, the B–O linkages on the edges are found to be significantly strained during the sliding, causing the metastable sliding configurations to have higher reactivity toward the activation of propane and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Support and Oxidation Effects on Subnanometer Palladium Nanoparticles

The effect of cluster size, oxidation state, and the support upon the structures and energetics of subnanometer palladium nanoparticles is investigated within a density functional framework. Gas phase global minima of Pd-4 and Pd-10 along with their suboxide counterparts are determined using a genetic algorithm and deposited upon MgO (001) and a high-index alumina surface. It is observed that there is an oxidation-dependent transition in the smaller clusters from three-dimensional to two-dimensional structures both in the gas phase and when supported by a surface. MgO strongly promotes a change from tetrahedral- and icosahedral-based structures toward cubic forms, while alumina induces significant distortion of the cluster and the breaking of Pd-Pd bonds. Increased oxygenation contributes cooperatively to these effects, causing disruption of the Pd-Pd bond network, favoring the incorporation of oxygen into the cluster structure, further complicating unambiguous structure prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Structures and Stabilities of (MgO) n Nanoclusters

Global minima for (MgO) n structures were optimized using a tree growth–hybrid genetic algorithm in conjunction with MNDO/MNDO/d semiempirical molecular orbital calculations followed by density functional theory geometry optimizations with the B3LYP functional. New lowest energy isomers were found for a number of (MgO) n clusters. The most stable isomers for (MgO) n (n > 3) are 3-dimensional. For n < 20, hexagonal tubular (MgO)n structures are more favored in energy than the cubic structures. The cubic structures and their variations dominate after n = 20. For the cubic isomers, increasing the size of the cluster in any dimension improves the stability. The effectiveness of increasing the size of the cluster in a specific dimension to improve stability diminishes as the size in that dimension increases. For cubic structures of the same size, the most compact cubic structure is expected to be the more stable cubic structure. The average Mg–O bond distance and coordination number both increase as n increases. The calculated average Mg–O bond distance is 2.055 Å at n = 40, slightly smaller than the bulk value of 2.104 Å. The average coordination number is predicted to be 4.6 for the lowest energy (MgO) 40 as compared to the bulk value of 6. As n increases, the normalized clustering energy ΔE(n) for the (MgO) n increases and the slope of the ΔE(n) vs n curve decreases. The value of ΔE(40) is predicted to be 150 kcal/mol, as compared to the bulk value ΔE(∞) = 176 kcal/mol. Finally, the electronic properties of the clusters are presented and the reactive sites are predicted to be at the corners.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Interface Structure Prediction from First-Principles

Information about the atomic structures at solid–solid interfaces is crucial for understanding and predicting the performance of materials. Due to the complexity of the interfaces, it is very challenging to resolve their atomic structures using either experimental techniques or computer simulations. In this paper, we present an efficient first-principles computational method for interface structure prediction based on an adaptive genetic algorithm. This approach significantly reduces the computational cost, while retaining the accuracy of first-principles prediction. The method is applied to the investigation of both stoichiometric and nonstoichiometric SrTiO3 Σ3(112)[1¯10] grain boundaries with unit cell containing up to 200 atoms. Several novel low-energy structures are discovered, which provide fresh insights into the structure and stability of the grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Structures and Energetics of (MgCO 3 ) n Clusters ( n ≤ 16)

There is significant interest in the role of carbonate minerals for the storage of CO 2 and the role of prenucleation dusters in their formation. Global minima for (MgCO 3 ) n (n ≤ 16) structures were optimized using a tree growth-hybrid genetic algorithm in conjunction with MNDO/MNDO/d semiempirical molecular orbital calculations followed by density functional theory geometry optimizations with the B3LYP functional. The most stable isomers for (MgCO 3 ) n (n < 5) are approximately 2-dimensional. Mg can be bonded to one or two 0 atoms of a CO 3 2- , and the 1-O bonding scheme is more favored as the cluster becomes larger. The average C-Mg coordination number increases as the cluster size increases, and at n = 16, the average C-Mg coordination number was calculated to be 5.2. The normalized dissociation energy to form monomers increases as n increases. At n = 16, the normalized dissociation energy is calculated to be 116.2 kcal/mol, as compared to the bulk value of 153.9 kcal/mol. The adiabatic reaction energies for the recombination reactions of (MgO) n clusters and CO 2 to form (MgCO 3 ) n were calculated. The exothermicity of the normalized recombination energy < RE >(CO 2 ) decreases as n increases and converged to the experimental bulk limit rapidly. The normalized recombination energy < RE >(CO 2 ) was calculated to be -52.2 kcal/mol for the monomer and -30.7 kcal/mol for n = 16, as compared to the experimental value of -27.9 kcal/mol for the solid phase reaction. Infrared spectra for the lowest energy isomers were calculated, and absorption bands in the previous experimental infrared studies were assigned with our density functional theory predictions. The 13 C, 17 O, and 25 Mg NMR chemical shifts for the clusters were predicted. We found that the results provide insights into the structural and energetic transitions from nanoclusters of (MgCO 3 ) n to the bulk and the spectroscopic properties of clusters for their experimental identification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗
DOE OSTI2016

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts

Abstract Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO 2 (111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Nonlinear thermodynamic computing out of equilibrium

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

Whitelam, Stephen [Lawrence Berkeley National Labo↗
DOE OSTI2021

Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

Abstract This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe 2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe 2 , the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

Zhang, Xu↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2016

‘Crystal Genes’ in Metallic Liquids and Glasses

We analyze the underlying structural order that transcends liquid, glass and crystalline states in metallic systems. A genetic algorithm is applied to search for the most common energetically favorable packing motifs in crystalline structures. These motifs are in turn compared to the observed packing motifs in the actual liquid or glass structures using a cluster-alignment method. Using this method, we have revealed the nature of the short-range order in Cu 64 Zr 36 glasses. More importantly, we identified a novel structural order in the Al 90 Sm 10 system. In addition, our approach brings new insight into understanding the origin of vitrification and describing mesoscopic order-disorder transitions in condensed matter systems.

36 MATERIALS SCIENCE↗