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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

First-principles calculated decomposition pathways for LiBH 4 nanoclusters

Here, we analyze thermodynamic stability and decomposition pathways of LiBH 4 nanoclusters using grand-canonical free-energy minimization based on total energies and vibrational frequencies obtained from density-functional theory (DFT) calculations. We consider (LiBH 4 ) n nanoclusters with n = 2 to 12 as reactants, while the possible products include (Li) n , (B) n , (LiB) n , (LiH) n , and Li 2 B n H n ; off-stoichiometric LinBnHm (m ≤ 4n) clusters were considered for n = 2, 3, and 6. Cluster ground-state configurations have been predicted using prototype electrostatic ground-state (PEGS) and genetic algorithm (GA) based structural optimizations. Free-energy calculations show hydrogen release pathways markedly differ from those in bulk LiBH 4 . While experiments have found that the bulk material decomposes into LiH and B, with Li 2 B 12 H 12 as a kinetically inhibited intermediate phase, (LiBH 4 ) n nanoclusters with n ≤ 12 are predicted to decompose into mixed Li n B n clusters via a series of intermediate clusters of Li n B n H m (m ≤ 4n). The calculated pressure-composition isotherms and temperature-pressure isobars exhibit sloping plateaus due to finite size effects on reaction thermodynamics. Generally, decomposition temperatures of free-standing clusters are found to increase with decreasing cluster size due to thermodynamic destabilization of reaction products.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Pure and Zn-doped Pt Clusters go Flat and Upright on MgO(100)

Pure and doped sub-nanoclusters can exhibit superb catalytic activity, which, however, strongly depends on their size, shape, composition, and the nature of the support. This work is about surface-deposited sub-nano Pt-based clusters, which are promising catalysts for the reactions of dehydrogenation. Using density functional theory and ab initio calculations, and an ab initio genetic algorithm for finding the global minima of clusters, we found a peculiar effect that Pt₅ and Pt₄Zn clusters exhibit upon deposition on MgO(100). Both of them change shapes from the gas phase 3-D form to a planar form, and they stand upright on the support. Several reasons are responsible for this behaviour. In part, clusters go flat due to the electron transfer from the support. Indeed, the anionic Pt₅- and Pt₄Zn- species are flat also in the gas phase. Charging induces the second-order Jahn–Teller effect (or partial covalency) facilitated by the recruitment of the higher-energy 6p atomic orbitals on Pt into the valence manifold, and that is the reason for the planarization of the anions. Secondly, clusters maximize interactions with the surface O atoms (resulting in further favouring of 2-D structures over 3-D), and avoid contacts with surface Mg atoms (resulting in upright morphologies).

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Metastable cobalt nitride structures with high magnetic anisotropy for rare-earth free magnets

Metastable structures of cobalt nitrides and Fe-substituted cobalt nitrides are explored as possible candidates for rare-earth free permanent magnets. Through crystal structure searches using an adaptive genetic algorithm, new structures of Co n N (n = 3…8) are found to have lower energies than those previously discovered by experiments. Some structures exhibit large magnetic anisotropy energy, reaching as high as 200 μeV per Co atom (or 2.45 MJ m -3 ) based on first-principles density functional calculation. Substituting a fraction of Co with Fe helps in stabilizing new structures and at the same time further improves the magnetic properties. Our theoretical predictions provide useful insights into a promising system for the discovery of new rare-earth free magnets by experiment.

Zhao, Xin↗
DOE OSTI2017

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff–Brenner type bond order potential (BOP) for a Co–C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure–property relationships, and temperature/pressure response of Co–C systems, as well as design organic–inorganic hybrid structures with a desired set of properties.

Narayanan, Badri↗
DOE OSTI2018

Magnetism of new metastable cobalt-nitride compounds

The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co–N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic-structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co–N compounds with favorable magnetic properties including hexagonal Co 3 N nanoparticles with a high saturation magnetic polarization ($J_s$ = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy ($K_1$ = 1.01 MJ m -3 or 10.1 Mergs per cm 3 ). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Novel superconducting structures of BH 2 under high pressure

The crystal structures of boron hydrides in a pressure range of 50–400 GPa were studied using the genetic algorithm (GA) method combined with first-principles density functional theory calculations. BH 4 and BH 5 are predicted to be thermodynamically unstable. Two new BH 2 structures with Cmcm and C 2/ c space group symmetries, respectively, were predicted, in which the B atoms tend to form two-dimensional sheets. The calculated band structures showed that in the pressure range of 50–150 GPa, the Cmcm -BH 2 phase has very small gaps, while the C 2/ c -BH 2 phase at 200–400 GPa is metallic. In conclusion, the superconductivity of the C 2/ c -BH 2 structure was also investigated, and electron–phonon coupling calculations revealed that the estimated T c values of C 2/ c -BH 2 are about 28.18–37.31 K at 250 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Learning-accelerated discovery of immune-tumour interactions

We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour–immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine learning a bond order potential model to study thermal transport in WSe 2 nanostructures

Here, nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe 2 . The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe 2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe 2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon–phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe 2 structures as well as their transport properties for thermoelectric and thermal management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

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

Thermodynamic stability versus kinetic accessibility: Pareto fronts for programmable self-assembly

A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory: we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles' dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. Additionally, the tradeoff disappears when the number of particle types reaches a value m *, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a thermodynamic–kinetic tradeoff. However, we argue, by contrasting our coarse-grained model's predictions with those of Brownian dynamics simulations, that particles with short-ranged isotropic interactions should generically have a tradeoff, and avoiding it in larger systems will require orientation-dependent interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimentally informed structure optimization of amorphous TiO 2 films grown by atomic layer deposition

Amorphous titanium dioxide TiO 2 (a-TiO 2 ) has been widely studied, particularly as a protective coating layer on semiconductors to prevent corrosion and promote electron–hole conduction in photoelectrochemical reactions. The stability and longevity of a-TiO 2 is strongly affected by the thickness and structural heterogeneity, implying that understanding the structure properties of a-TiO 2 is crucial for improving the performance. This study characterized the structural and electronic properties of a-TiO 2 thin films (~17 nm) grown on Si by atomic layer deposition (ALD). Fluctuation spectra V(k) and angular correlation functions were determined with 4-dimensional scanning transmission electron microscopy (4D-STEM), which revealed the distinctive medium-range ordering in the a-TiO 2 film. A realistic atomic model of a-TiO 2 was established guided by the medium-range ordering and the previously reported short-range ordering of a-TiO 2 film, as well as the interatomic potential. The structure was optimized by the StructOpt code using a genetic algorithm that simultaneously minimizes energy and maximizes the match to experimental short- and medium-range ordering. The StructOpt a-TiO 2 model presents improved agreements with the medium-range ordering and the k-space location of the dominant 2-fold angular correlations compared with a traditional melt-quenched model. The electronic structure of the StructOpt a-TiO 2 model was studied by ab initio calculations and compared to the crystalline phases and experimental results. Finally, this work uncovered the medium-range ordering in a-TiO 2 thin films and provided a realistic a-TiO 2 structure model for further investigation of structure–property relationships and materials design. In addition, the improved multi-objective optimization package StructOpt was provided for structure determination of complex materials guided by experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Finding the Pareto front for high-entropy-alloy catalysts

Finding catalysts that have both high activity and high stability presents a long-standing challenge. Since optimizing activity and stability are conflicting objectives, the best one can do is find the Pareto front that yields optimal tradeoffs between these features. On the Pareto front, there is a trade-off where a portion of catalytic activity must be sacrificed to gain further stability and vice versa . Here, we provide a method to optimize the front by designing a multi-objective genetic algorithm that combines machine learning, graph neural network calculations, and density functional calculations. The application considered is the oxygen evolution reaction catalyzed by high-entropy alloys. We find that the Pareto front generally contains alloys with diverse elements, but that enhancing stability inevitably inflicts a toll on activity. We compare the general conclusions of our work to a survey of 545 experiments.

Zhang, Chengyi [Univ. of Auckland (New Zealand)]↗
DOE OSTI2013

Approximate photochemical dynamics of azobenzene with reactive force fields

We have fitted reactive force fields of the ReaxFF type to the ground and first excited electronic states of azobenzene, using global parameter optimization by genetic algorithms. Upon coupling with a simple energy-gap transition probability model, this setup allows for completely force-field-based simulations of photochemical cis→trans- and trans→cis-isomerizations of azobenzene, with qualitatively acceptable quantum yields. This paves the way towards large-scale dynamics simulations of molecular machines, including bond breaking and formation (via the reactive force field) as well as photochemical engines (presented in this work)

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

Optical and electronic properties of mixed Ag-Au tetramer cations

We present experimental and theoretical studies of the optical response of mixed Ag {sub n} Au {sub 4−n}{sup +} (n=1–3) clusters in the photon energy range ℏω = 1.9–3.5 eV. Absorption spectra are recorded by a newly built longitudinal molecular beam depletion spectroscopy apparatus providing lower limits to absolute photodissociation cross sections. The experimental data are compared to optical response calculations in the framework of long-range corrected time-dependent density functional theory with initial cluster geometries obtained by the unbiased Birmingham Cluster Genetic Algorithm coupled with density functional theory. Experiments and excited state calculations shed light on the structural and electronic properties of the mixed Ag-Au tetramer cations.

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

Experimental determination of the complex stiffness tensor and Euler angles in anisotropic media using ultrasonic waves

The aim of this work is to measure the complex elastic tensor and Euler angles in very complex anisotropic media like austenitic steel welds, by inverse problem resolution from experimental data. The obtained experimental characteristics of the anisotropic material will be injected in a FE code developed by EDF enabling the simulation of an actual ultrasonic NDE of welds. The present work aims to provide reliable input data to the 3D future development of the code. In particular, this complex elastic tensor will allow to predict by modeling beam skewing ant attenuation in an austenitic weld. The investigation of such anisotropic media is very complex because of the directional dependency of the elastic stiffness tensor. Then we will discuss the use of a hybrid genetic algorithm to overcome this difficulty. The identification method is based on waveforms spectra reconstruction associated to a physical model describing wave propagation in plates, during underwater measurements. The entire procedure is qualified and validated using simulated data. Moreover, a comparison of the estimated elastic coefficients with literature values and ultrasonic measurements obtained in transmission is also given, at the end of the paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Influence of spin-orbit effects on structures and dielectric properties of neutral lead clusters

Combining molecular beam electric deflection experiments and global optimization techniques has proven to be a powerful tool for resolving equilibrium structures of neutral metal and semiconductor clusters. Herein, we present electric molecular beam deflection experiments on Pb{sub N} (N = 7–18) clusters. Promising structures are generated using the unbiased Birmingham Cluster Genetic Algorithm approach based on density functional theory. The structures are further relaxed within the framework of two-component density functional theory taking scalar relativistic and spin orbit effects into account. Quantum chemical results are used to model electric molecular beam deflection profiles based on molecular dynamics calculations. Comparison of measured and simulated beam profiles allows the assignment of equilibrium structures for the most cluster sizes in the examined range for the first time. Neutral lead clusters adopt mainly spherical geometries and resemble the structures of lead cluster cations apart from Pb{sub 10}. Their growth pattern deviates strongly from the one observed for tin and germanium clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗