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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Tunable spin-state bistability in a spin crossover molecular complex

The spin crossover (SCO) transitions at both the surface and over the entire volume of the [Fe{H2B(pz)(2)}(2) (bipy)] polycrystalline films on Al2O3 substrates have been studied, where pz = pyrazol-1-yl and bipy = 2,2'-bipyridine. For [Fe{H2B(pz)(2)}(2) (bipy)] films of hundreds of nm thick, magnetometry and x-ray absorption spectroscopy measurements show thermal hysteresis in the SCO transition with temperature, although the transition in bulk [Fe{H2B(pz)(2)}(2) (bipy)] occurs in a non-hysteretic fashion at 157K. While the size of the crystallites in those films are similar, the hysteresis becomes more prominent in thinner films, indicating a significant effect of the [Fe{H2B(pz)(2)}(2)(bipy)]/Al2O3 interface. Bistability of spin states, which can be inferred from the thermal hysteresis, was directly observed using temperature-dependent x-ray diffraction; the crystallites behave as spin-state domains that coexist during the transition. The difference between the spin state of molecules at the surface of the [Fe{H2B(pz)(2)}(2)(bipy)] films and that of the molecules within the films, during the thermal cycle, indicates that both cooperative (intermolecular) effects and coordination are implicated in perturbations to the SCO transition.

Jiang, Xuanyuan↗
DOE OSTI2018

An inexact regularized Newton framework with a worst-case iteration complexity of $ {\mathscr O}(\varepsilon^{-3/2}) $ for nonconvex optimization

Abstract An algorithm for solving smooth nonconvex optimization problems is proposed that, in the worst-case, takes ${\mathscr O}(\varepsilon ^{-3/2})$ iterations to drive the norm of the gradient of the objective function below a prescribed positive real number $\varepsilon $ and can take ${\mathscr O}(\varepsilon ^{-3})$ iterations to drive the leftmost eigenvalue of the Hessian of the objective above $-\varepsilon $. The proposed algorithm is a general framework that covers a wide range of techniques including quadratically and cubically regularized Newton methods, such as the Adaptive Regularization using Cubics (arc) method and the recently proposed Trust-Region Algorithm with Contractions and Expansions (trace). The generality of our method is achieved through the introduction of generic conditions that each trial step is required to satisfy, which in particular allows for inexact regularized Newton steps to be used. These conditions center around a new subproblem that can be approximately solved to obtain trial steps that satisfy the conditions. A new instance of the framework, distinct from arc and trace, is described that may be viewed as a hybrid between quadratically and cubically regularized Newton methods. Numerical results demonstrate that our hybrid algorithm outperforms a cubically regularized Newton method.

Mathematics↗