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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Dose–volume histogram evaluation scheme for beam orientations in intensity-modulated radiation therapy

Beam orientations in intensity-modulated radiation therapy (IMRT) is an important, but difficult NP-hard optimization problem. A considerable part of this difficulty is due to the essential prerequisite of defining, appropriately, counter-intuitive criteria for a mathematical plan evaluation that coincides with the clinical judgment of the considered plan. Moreover, the quality of beam directions depends heavily on their intensities. Usually a stochastic selector is utilized for optimizing beam orientations, and the intensities are thus calculated many thousands of times, each time for a different selection of beam directions, resulting in excessive time complexity. Therefore, selecting an appropriate set of beam directions in IMRT is still a time-consuming manual trial-and-error search procedure that depends on intuition and empirical knowledge. To overcome these difficulties, this work utilizes the concept of dose–volume histogram (DVH), which is one of the main recognized quantitative measurement tools used for plan judgment, and presents a DVH evaluation scheme that parallelizes plan evaluation with clinical plan judgment. This scheme is then utilized to optimize the intensities of each individual beam, enabling it to be represented by its DVHs, produced for the different irradiated volumes. Finally, an evolutionary algorithm is employed to search for the best combination of those DVHs reflecting the clinical optimal. The presented methods have been applied to three real clinical examples. In comparison to the standard equally spaced beam plans, improvements were reported in all three cases. In comparison with the greedy algorithm, improvements were reported in those examples requiring more than five beam directions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt

As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.

09 BIOMASS FUELS↗
DOE OSTI2018

Toward First-Principles Design of Organic Nonlinear Optical Materials: Crystal Structure Prediction and Halogen Bonding Impact on Hyperpolarizabilities of 2-Iodo-3-hydroxypyridine

Computational methods can potentially accelerate development of more efficient organic materials for second harmonic generation. Here, we test the method that includes the evolutionary algorithm for predicting crystal structure and prognosis of nonlinear optical properties based on the predicted structure. For this test, we selected 2-iodo-3-hydroxypyridine, which exhibits second harmonic generation intensity comparable to that of urea. We performed global minimization of the lattice energy and found the experimental structure when many-body dispersion correction is added to the density functional theory values. We analyzed geometric preferences of the halogen bonding in predicted virtual polymorphs. We also found linear correlation between the lengths of the iodine–iodine halogen bonds and calculated second order susceptibilities.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties—are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. Here, the burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g . While the present contribution is focused specifically on T g , the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Unveiling a Novel, Cation-Rich Compound in a High-Pressure Pb–Te Binary System

Because of the common oxidation states of group IV elements (+2 or +4) and group VI elements (–2), 1:1 and 1:2 are two typical stoichiometries found in the IV–VI compounds. Particularly, in the Pb–Te binary system, the 1:1 stoichiometric PbTe is believed to be the only stable compound. Herein, using evolutionary algorithms, density functional theory, a laser-heated diamond anvil cell, and synchrotron X-ray diffraction experiments, we discovered a novel Pb–Te compound with an unexpected stoichiometry of 3:2 above 20 GPa. This tetragonal Pb 3 Te 2 is the one of the very few cation-rich compounds that has ever been discovered in the entire IV–VI binary system. Further analyses based on electron density distribution, electron localization function, and Bader charge have shown that this newly discovered compound has a mixed character of chemical bonding with a decreased ionicity. By further calculating the electron–phonon interaction, Pb 3 Te 2 is predicted to exhibit a superconducting transition at low temperatures. Lastly, the discovery of Pb 3 Te 2 paves the way for further explorations of other novel cation-rich IV–VI group compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Pressure-Induced Valence and Structural Changes in YbMn 2 Ge 2 -Inelastic X-ray Spectroscopy and Theoretical Investigations

The pressure-induced valence change of Yb in YbMn 2 Ge 2 has been studied by high pressure inelastic X-ray emission and absorption spectroscopy in the partial fluorescence yield mode up to 30 GPa. The crystal structure of YbMn 2 Ge 2 has been investigated by high pressure powder X-ray diffraction experiments up to 40 GPa. The experimental investigations have been complemented by first principles density functional theoretical calculations using the generalized gradient approximation with an evolutionary algorithm for structural determination. The Yb valence and magnetic structures have been calculated using the self-interaction corrected local spin density approximation. The X-ray emission results indicate a sharp increase of Yb valence from v = 2.42(2) to v = 2.75(3) around 1.35 GPa, and Yb reaches a near trivalent state (v = 2.95(3)) around 30 GPa. Further, a new monoclinic P1 type high pressure phase is found above 35 GPa; this structure is characterized by the Mn layer of the ambient (I4/mmm) structure transforming into a double layer. The theoretical calculations yield an effective valence of v = 2.48 at ambient pressure in agreement with experiment, although the pure trivalent state is attained theoretically at significantly higher pressures (above 40 GPa).

Kumar, Ravhi S↗
DOE OSTI2018

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Artificial intelligence-driven approaches for materials design and discovery

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial and error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence, have reshaped the landscape of designing new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. Here, in this Review, we present key computational advances in materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning methods and evolutionary algorithms, to advanced artificial intelligence strategies such as reinforcement learning and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This Review may serve as a brief guide to the approaches, progress and outlook of designing future functional materials with technological relevance.

computational methods↗
DOE OSTI2015

In silico prediction of MOFs with high deliverable capacity or internal surface area

Metal–organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal–organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. Furthermore, the correlation between deliverable capacities and surface area is network dependent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Super-oxidation of silicon nanoclusters: magnetism and reactive oxygen species at the surface

Oxidation of silicon nanoclusters depending on the temperature and oxygen pressure is explored from first principles using the evolutionary algorithm, and structural and thermodynamic analysis. From our calculations of 90 SinOm clusters we found that under normal conditions oxidation does not stop at the stoichiometric SiO2 composition, as it does in bulk silicon, but goes further placing extra oxygen atoms on the cluster surface. These extra atoms are responsible for light emission, relevant to reactive oxygen species and many of them are magnetic. We argue that the super-oxidation effect is size-independent and discuss its relevance to nanotechnology and miscellaneous applications, including biomedical ones.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Ferroelectric 2D antimony oxides with wide bandgaps

The first two-dimensional (2D) polymorphs of antimony dioxide, namely, γ-Sb 2 O 4 and δ-Sb 2 O 4 , are predicted using the evolutionary algorithm combined with first-principles density functional theory (DFT) calculations. Out-of-plane ferroelectricity is found in γ-Sb 2 O 4 , while in-plane ferroelectricity is found in δ-Sb 2 O 4 . The predicted dipole moments of γ-Sb 2 O 4 and δ-Sb 2 O 4 phases are 36.63 and 14.96 eÅ, respectively, implying that they can be good candidates for making ferroelectric devices. Further, the calculations show that doping with other group V elements or applying strain can lower the switching energy barriers and thus facilitate switching. Results from GW calculations show indirect band gaps of 5.51 and 3.39 eV for γ-Sb 2 O 4 and δ-Sb 2 O 4 in their monolayers, respectively. Raman spectra are calculated to facilitate the experimental investigation of the predicted structures. The existence of both in-plane and out-of-plane 2D ferroelectricity and the large band gaps make this material system particularly interesting for potential applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Realization of a reversible switching in TaO{sub 2} polymorphs via Peierls distortion for resistance random access memory

Transition-metal-oxide based resistance random access memory (RRAM) is a promising candidate for next-generation universal non-volatile memories. Searching and designing appropriate materials used in the memories becomes an urgent task. Here, a structure with the TaO{sub 2} formula was predicted using evolutionary algorithms in combination with first-principles calculations. This triclinic structure (T-TaO{sub 2}) is both energetically and dynamically more favorable than the commonly believed rutile structure (R-TaO{sub 2}). The metal-insulator transition (MIT) between metallic R-TaO{sub 2} and T-TaO{sub 2} (band gap: 1.0 eV) is via a Peierls distortion, which makes TaO{sub 2} a potential candidate for RRAM. The energy barrier for the reversible phase transition is 0.19 eV/atom and 0.23 eV/atom, respectively, suggesting low power consumption for the resistance switch. The present findings about the MIT as the resistance-switch mechanism in Ta-O system will stimulate experimental work to fabricate tantalum oxides based RRAM.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Coherent control of plasma dynamics by feedback-optimized wavefront manipulation

Plasmas generated by an intense laser pulse can support coherent structures such as large amplitude wakefield that can affect the outcome of an experiment. We investigate the coherent control of plasma dynamics by feedback-optimized wavefront manipulation using a deformable mirror. The experimental outcome is directly used as feedback in an evolutionary algorithm for optimization of the phase front of the driving laser pulse. In this paper, we applied this method to two different experiments: (i) acceleration of electrons in laser driven plasma waves and (ii) self-compression of optical pulses induced by ionization nonlinearity. The manipulation of the laser wavefront leads to orders of magnitude improvement to electron beam properties such as the peak charge, beam divergence, and transverse emittance. The demonstration of coherent control for plasmas opens new possibilities for future laser-based accelerators and their applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Determination of ground and excited state dipole moments via electronic Stark spectroscopy: 5-methoxyindole

The dipole moments of the ground and lowest electronically excited singlet state of 5-methoxyindole have been determined by means of optical Stark spectroscopy in a molecular beam. The resulting spectra arise from a superposition of different field configurations, one with the static electric field almost parallel to the polarization of the exciting laser radiation, the other nearly perpendicular. Each field configuration leads to different intensities in the rovibronic spectrum. With an automated evolutionary algorithm approach, the spectra can be fit and the ratio of both field configurations can be determined. A simultaneous fit of two spectra with both field configurations improved the precision of the dipole moment determination by a factor of two. We find a reduction of the absolute dipole moment from 1.59(3) D to 1.14(6) D upon electronic excitation to the lowest electronically excited singlet state. At the same time, the dipole moment orientation rotates by 54{sup ∘} showing the importance of the determination of the dipole moment components. The dipole moment in the electronic ground state can approximately be obtained from a vector addition of the indole and the methoxy group dipole moments. However, in the electronically excited state, vector addition completely fails to describe the observed dipole moment. Several reasons for this behavior are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

On the adaptivity and complexity embedded into differential evolution

This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

First principles crystal engineering of nonlinear optical materials. I. Prototypical case of urea

The crystalline materials with nonlinear optical (NLO) properties are critically important for several technological applications, including nanophotonic and second harmonic generation devices. Urea is often considered to be a standard NLO material, due to the combination of non-centrosymmetric crystal packing and capacity for intramolecular charge transfer. Various approaches to crystal engineering of non-centrosymmetric molecular materials were reported in the literature. Here we propose using global lattice energy minimization to predict the crystal packing from the first principles. We developed a methodology that includes the following: (1) parameter derivation for polarizable force field AMOEBA; (2) local minimizations of crystal structures with these parameters, combined with the evolutionary algorithm for a global minimum search, implemented in program USPEX; (3) filtering out duplicate polymorphs produced; (4) reoptimization and final ranking based on density functional theory (DFT) with many-body dispersion (MBD) correction; and (5) prediction of the second-order susceptibility tensor by finite field approach. This methodology was applied to predict virtual urea polymorphs. After filtering based on packing similarity, only two distinct packing modes were predicted: one experimental and one hypothetical. DFT + MBD ranking established non-centrosymmetric crystal packing as the global minimum, in agreement with the experiment. Finite field approach was used to predict nonlinear susceptibility, and H-bonding was found to account for a 2.5-fold increase in molecular hyperpolarizability to the bulk value.

36 MATERIALS SCIENCE↗