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All sourcesNASA NTRSDOE OSTI
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↗
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 OSTI2011

Optimizing luminescent solar concentrator design

Luminescent Solar Concentrators (LSCs) use fluorescent materials and light guides to convert direct and diffuse sunlight into concentrated wavelength-shifted light that produces electrical power in small photovoltaic (PV) cells with the goal of significantly reducing the cost of solar energy utilization. In this paper we present an optimization analysis based on the implementation of a genetic algorithm (GA) subroutine to a numerical ray-tracing Monte Carlo model of an LSC, SIMSOLAR-P. The initial use of the GA implementation in SIMSOLAR-P is to find the optimal parameters of a hypothetical ‘‘perfect luminescent material’’ that obeys the Kennard Stepanov (K-S) thermodynamic relationship between emission and absorption. The optimization balances the efficiency losses in the wavelength shift and PV conversion with the efficiency losses due to re-scattering of light out of the collector. The theoretical limits of efficiency are provided for one, two and three layer configurations; the results show that a single layer configuration is far from optimal and adding a second layer in the LSC with wavelength shifted material in the near infrared region significantly increases the power output, while the gain in power by adding a third layer is relatively small. Here, the results of this study provide a theoretical upper limit to the performance of an LSC and give guidance for the properties required for luminescent materials, such as quantum nanocrystals, to operate efficiently in planar LSC configurations

14 SOLAR ENERGY↗
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↗