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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Ta-Nb-Mo-W refractory high-entropy alloys: Anomalous ordering behavior and its intriguing electronic origin

From electronic-structure-based thermodynamic linear response, we establish chemical ordering behavior in complex solid solutions versus how Gibbs' space is traversed—applying it on prototype refractory A2 Ta-Nb-Mo-W high-entropy alloys. Near ideal stoichiometry, this alloy has anomalous, intricate chemical ordering tendencies, with long-ranged chemical interactions that produce competing short-range order (SRO) with a crossover to spinodal segregation. This atypical SRO arises from canonical band behavior that, with alloying, creates features near the Fermi surface (well defined even with disorder) that change to simple commensurate SRO with (un)filling of these states. In conclusion, our results reveal how complexity and competing electronic effects control ordering in these alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2018

First-principles study of vibrational entropy effects on the PbTe-SrTe phase diagram

We present first-principles calculations of the solid-state phase diagram of PbTe-SrTe that include both configurational and vibrational contributions to the free energy. These configurational and vibrational free energies were calculated using the cluster expansion (CE) approach and quasiharmonic phonon calculations, respectively. The coupled configurational and vibrational degrees of freedom were taken into account by including temperature-dependent CE interactions with the T = 0K values and temperature dependence of the interactions determined from total energies and phonon free energies, both calculated using density functional theory. This Hamiltonian was then employed in Monte Carlo simulations to obtain a quantitative prediction of the PbTe-SrTe phase diagram. The phase diagram consists of a miscibility gap with a maximum temperature of ~500 K, with the inclusion of the vibrational entropy resulting in a more than fourfold increase in the calculated solubility limits of SrTe in PbTe. Our results suggest that the equilibrium solubility of this system is likely much larger than previously thought and deserves further experimental investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Anisotropic magnetic entropy change in Cr 2 X 2 Te 6 ( X = Si and Ge )

Intrinsic, two-dimensional (2D) ferromagnetic semiconductors are an important class of materials for spintronics applications. Cr 2 X 2 Te 6 ( X = Si and Ge ) semiconductors show 2D Ising-like ferromagnetism, which is preserved in few-layer devices. The maximum magnetic entropy change associated with the critical properties around the ferromagnetic transition for Cr 2 Si 2 Te 6 - ΔS$max\atop{M}$ ~ 5.05 J kg -1 K -1 is much larger than - ΔS$max\atop{M}$ ~ 2.64 J kg -1 K -1 for Cr 2 Ge 2 Te 6 with an out-of-plane field change of 5 T. The rescaled - ΔS M (T,H) curves collapse onto a universal curve independent of temperature and field for both materials. Here, this indicates similar critical behavior and 2D Ising magnetism, confirming the magnetocrystalline anisotropy that could preserve the long-range ferromagnetism in a few layers of Cr 2 X 2 Te 6 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Hidden Mn magnetic-moment disorder and its influence on the physical properties of medium-entropy NiCoMn solid solution alloys

In this work, the ab initio Korringa-Kohn-Rostoker method combined with the coherent potential approximation (CPA) was employed to investigate the electronic, magnetic, and transport properties of medium-entropy face-centered-cubic (fcc) NiCoMn solid solution alloys. By comparing the CPA electronic structure with that from supercell calculations, we uncovered an unconventional CPA ground state, which correctly distinguishes two equally populated Mn CPA components—with large spin moments but opposite orientations. Using the spin spiral calculations, we further demonstrated that this ground state is most energetically favorable in the presence of spin noncollinearity, and no significant longitudinal spin fluctuation is observed, justifying the applicability of the Heisenberg model. The finite-temperature magnetism was further studied using different approximations based on the Heisenberg model, and we found the Mn moments to be fully disordered at low temperature due to a small net effective Weiss field on Mn. In addition, the magnetic effect on the electron scattering at finite temperatures was evaluated and compared with other scattering mechanisms. Since the magnetization-induced electron scattering is almost saturated in the ground state, (full) spin disorder only yields a small addition to the resistivity, whereas the thermal displacements increase it modestly. Finally, we elucidate the role of hydrostatic pressure on the magnetic and transport properties. In conclusion, these findings reflect the importance of the magnetic signatures on the physical properties of alloys, and they provide a window into magnetism-controlled electronic structure and energy dissipation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Impact of anharmonicity on the vibrational entropy and specific heat of UO 2

The impact of anharmonicity on the vibrational entropy and heat capacity of UO 2 has been discovered from 10 to 1200 K using inelastic neutron-scattering measurements of the phonon density of states (PDOS). Small changes in the PDOS are observed from 10 to 295 K, with more noticeable changes appearing in the 750- and 1200-K data. The specific heat determined from the PDOS measurements is in agreement with macroscopic specific heat measurements, and the overall impact of nondilation anharmonicity on the specific heat has been shown to be less than 2%. An analysis of the phonon measurements shows that the softening of acoustic phonons with temperature is consistent with the quasiharmonic approximation. The optical phonons deviate from the quasiharmonic prediction, with the low-energy optical phonons between approximately 20 and 50 meV softening more than expected, while the higher-energy optical phonons between approximately 50 and 80 meV have no appreciable softening over the temperature range measured. The observation of a small anharmonic specific heat contribution has been demonstrated to be the result of relatively large energy-dependent anharmonic effects which have opposite sign, leading to a total contribution near zero.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spin dynamics and a nearly continuous magnetic phase transition in an entropy-stabilized oxide antiferromagnet

In this work, the magnetic order and the spin dynamics in the antiferromagnetic entropy-stabilized oxide (Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 )O (MgO-ESO) have been studied using muon spin relaxation (μSR) and inelastic neutron scattering. We find that antiferromagnetic order develops gradually in the sample volume as it is cooled below 140 K, becoming fully ordered around 100 K. The spin dynamics show a critical slowing down in the vicinity of the transition, and the magnetic order parameter grows continuously in the ordered state. These results indicate that the antiferromagnetic transition is continuous but proceeds with a Gaussian distribution of ordering temperatures. Additionally, the magnetic contribution to the specific heat determined from inelastic neutron scattering likewise shows a broad feature centered around 120 K. High-resolution inelastic neutron scattering further reveals an initially gapped spectrum at low temperature which sees an increase in a quasielastic contribution upon heating until the ordering temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Theory of thermal properties of magnetic materials with unknown entropy

Theoretical approaches to study thermal properties of magnetic materials typically require accurate models of magnetic interactions in order to define the entropy. Here we introduce a complementary approach for examining thermal properties in magnetic systems where an accepted model for such interactions does not exist. In place of a specific model for magnetic interactions, the approach integrates measurements of temperature dependent magnetization of the studied material into a first principles computational scheme. The approach calculates system pressure from thermally disordered microstates that properly incorporate vibrational and spin subsystems at each temperature as well as the coupling between these subsystems. We apply the approach to calculate phonon modes and to investigate the anomalously low thermal expansion of the classical Invar alloy, Fe 0.65 Ni 0.35 . Here, the calculated phonon dispersions for Invar are in excellent agreement with measured data. The Invar thermal expansion is shown to remain small between 50 K and room temperature, consistent with the experimentally observed low thermal expansion value in this same temperature range. This anomalously small thermal expansion is directly connected to a small positive contribution from lattice thermal disorder that is nearly canceled by a large negative magnetic disorder contribution. By contrast, calculations for bcc Fe show a much larger thermal expansion, consistent with experiment, which is dominated by a large contribution from lattice thermal disorder that is reduced only slightly by a small negative contribution from that of magnetism. These findings give insights into the unusual nature of magnetism and spin-lattice coupling in Invar and Fe. In addition, they give promising preliminary support to the presented new methodology as a complementary way to investigate thermal properties of magnetic materials. The success achieved on Invar and Fe motivates future testing of the approach on other magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Maximum Entropy Principle in Deep Thermalization and in Hilbert-Space Ergodicity

We report universal statistical properties displayed by ensembles of pure states that naturally emerge in quantum many-body systems. Specifically, two classes of state ensembles are considered: those formed by (i) the temporal trajectory of a quantum state under unitary evolution or (ii) the quantum states of small subsystems obtained by partial, local projective measurements performed on their complements. These cases, respectively, exemplify the phenomena of “Hilbert-space ergodicity” and “deep thermalization.” In both cases, the resultant ensembles are defined by a simple principle: The distributions of pure states have maximum entropy, subject to constraints such as energy conservation, and effective constraints imposed by thermalization. We present and numerically verify quantifiable signatures of this principle by deriving explicit formulas for all statistical moments of the ensembles, proving the necessary and sufficient conditions for such universality under widely accepted assumptions, and describing their measurable consequences in experiments. We further discuss information-theoretic implications of the universality: Our ensembles have maximal information content while being maximally difficult to interrogate, establishing that generic quantum state ensembles that occur in nature hide (scramble) information as strongly as possible. Our results generalize the notions of Hilbert-space ergodicity to time-independent Hamiltonian dynamics and deep thermalization from infinite to finite effective temperature. Our work presents new perspectives to characterize and understand universal behaviors of quantum dynamics using statistical and information-theoretic tools.

Eigenstate thermalization↗
DOE OSTI2025

Source function from two-particle correlation function through entropy-regularized Richardson-Lucy deblurring

Source functions are obtained from p – p and d – α correlation functions by applying the Richardson-Lucy (RL) deblurring to the Koonin-Pratt (KP) equation. To prevent fitting of noise in the correlation function, total-variation (TV) regularization is employed that has been effective in ordinary image restoration. TV alone cannot ensure normalization of the source functions. To ensure the latter, we propose a maximum-entropy regularized RL algorithm (MEM-RL). We outline the MEM-RL formalism and optimization strategy for the KP equation, demonstrating its effectiveness on both simulated and experimental data, including the p – p and d – α correlation functions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

𝑁-dimensional maximum-entropy tomography via particle sampling

We propose a modified maximum-entropy (MENT) algorithm for six-dimensional phase space tomography. The algorithm uses particle sampling and low-dimensional density estimation to approximate large sets of high-dimensional integrals in the original MENT formulation. We implement this approach using Markov Chain Monte Carlo (MCMC) sampling techniques and demonstrate convergence of six-dimensional MENT on both synthetic and measured data.

Hoover, Austin [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2023

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation—DeepThermo. By using deep learning–based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e 10,000 for a real material. We also demonstrate DeepThermo’s performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

Yin, Junqi↗
DOE OSTI2023

A Modified Maximum Entropy Inverse Reinforcement Learning Approach for Microgrid Energy Scheduling

Increasing popularity of integrating distributed energy resources (DERs) into the power system brings a challenge to optimize the microgrid dispatch policy. The reinforcement learning methods suffer from a long-time problem with the theoretical assumption of the objective/reward function for the microgrid system. Although the traditional inverse reinforcement learning (IRL) approaches can solve this problem to some extent, they encounter a limitation of complex computations for state visitation frequency in the large and continuous state space. To alleviate this limitation, we propose a modified maximum entropy IRL (MMIRL) method to extract the reward function from the expert demonstrations for solving the microgrid energy scheduling problem. The proposed MMIRL algorithm is promising in recovering the reward function and learning the dispatch policy compared to conventional approaches. Case studies are performed in an energy arbitrage problem and a microgrid system with DERs. Results substantiate that the proposed MMIRL approach can learn the dispatch policy with more than 99% efficiency and outperforms other comparative methods.

artificial intelligence, reinforcement learning, m↗
DOE OSTI2023

An over-approximation of entropy for elemental multiconfigurational ground state electronic structures

The ability to predict the chemical and physical properties of a material is directly related to the structure and interactions of its electrons. For materials comprised of f-block elements (the lanthanides and actinides found in the last two rows of the periodic table), the complexity of electronic structure has presented great difficulty in understanding, modeling, and predicting material properties. The complexity of multiconfigurational ground state electronic structures is illustrated herein by the combinatorics of electron permutations within individual and cumulative occupancy configurations. A non-integer orbital occupancy representation of multiconfigurational ground states is described for superposition mixing between multiple near-energy degenerate occupancy configurations and generalized in such a way that established ground states are returned by approximation for elements with less-complex electronic structures. By considering the occupancy configurations as statistical mechanics macrostates, and the permutations of electrons as statistical mechanics microstates within those macrostates, an over-approximation of entropy for multiconfigurational elemental ground state electronic structures has been calculated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Amorphization in extreme deformation of the CrMnFeCoNi high-entropy alloy

Ever-harsher service conditions in the future will call for materials with increasing ability to undergo deformation without sustaining damage while retaining high strength. Prime candidates for these conditions are certain high-entropy alloys (HEAs), which have extraordinary work-hardening ability and toughness. By subjecting the equiatomic CrMnFeCoNi HEA to severe plastic deformation through swaging followed by either quasi-static compression or dynamic deformation in shear, we observe a dense structure comprising stacking faults, twins, transformation from the face-centered cubic to the hexagonal close-packed structure, and, of particular note, amorphization. The coordinated propagation of stacking faults and twins along {111} planes generates high-deformation regions, which can reorganize into hexagonal packets; when the defect density in these regions reaches a critical level, they generate islands of amorphous material. These regions can have outstanding mechanical properties, which provide additional strengthening and/or toughening mechanisms to enhance the capability of these alloys to withstand extreme loading conditions.

36 MATERIALS SCIENCE↗