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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2025

Odd-Parity Magnetism Driven by Antiferromagnetic Exchange

Realizing odd-parity, time-reversal-preserving, nonrelativistic spin splitting is a central goal for spintronics applications. We propose a group-theory-based microscopic framework to induce odd-parity spin splitting from coplanar antiferromagnetic (AFM) states without spin-orbit coupling (SOC). We develop phenomenological models for 421 conventional period-doubling AFM systems in nonsymmorphic space groups and construct minimal microscopic models for 119 of these. We find that these AFM states can attain three possible competing ground states. These ground states all break symmetries in addition to those broken by the usual AFM order. Specifically, they give rise to either odd-parity spin-splitting, nematic order, or scalar odd-parity order related to multiferroicity. Our microscopic theories reveal that the odd-parity spin-splitting energy scale is generically large and further reveal that the scalar odd-parity order gives a nonzero Berry curvature dipole without SOC. We identify 67 materials in the Magndata database for which our theory applies. We provide density-functional theory (DFT) calculations on Fe-based materials that reveal an ℎ-wave spin splitting consistent with our symmetry arguments and apply our microscopic model to determine the nonrelativistic Edelstein response for CeNiAsO.

Antiferromagnets↗
DOE OSTI2023

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis

Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Accurate interpretation of experimental PDF data is critically reliant on the development of structural models that can account for local variations in site occupancies and bond lengths. To this end, we outline an approach to model SRO using first-principles calculations based on the cluster-expansion formalism. These methods are validated on neutron scattering data from two disordered rocksalt oxyfluorides, Li 1.3 Mn 0.4 Ti 0.3 O 1.7 F 0.3 and Li 1.3 Mn 0.4 Nb 0.2 Ti 0.1 O 1.7 F 0.3 . For each composition, we demonstrate that an average structure without any SRO fails to reproduce several key features in the experimental PDF. To pinpoint the origin of the suspected SRO in these materials, configurational and displacive effects were separately investigated using two disparate models. Special quasi-random structures were relaxed using density functional theory to account for local changes in bond lengths while maintaining a near-random ionic configuration. This leads to slightly improved accuracy but still misrepresents asymmetry in the first few peaks of the PDF. Monte Carlo simulations were performed to model configurational SRO on a fixed lattice, which by itself is shown to have a minimal influence on the PDF. Instead, we find that it is the bond length relaxations within environments created by SRO which controls the details of the PDF, thereby highlighting the subtle but important coupling between configurational and displacive SRO in disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enhanced and stabilized hydrogen production from methanol by ultrasmall Ni nanoclusters immobilized on defect-rich h-BN nanosheets

Employing liquid organic hydrogen carriers (LOHCs) to transport hydrogen to where it can be utilized relies on methods of efficient chemical dehydrogenation to access this fuel. Therefore, developing effective strategies to optimize the catalytic performance of cheap transition metal-based catalysts in terms of activity and stability for dehydrogenation of LOHCs is a critical challenge. Here, we report the design and synthesis of ultrasmall nickel nanoclusters (∼1.5 nm) deposited on defect-rich boron nitride (BN) nanosheet (Ni/BN) catalysts with higher methanol dehydrogenation activity and selectivity, and greater stability than that of some other transition-metal based catalysts. The interface of the two-dimensional (2D) BN with the metal nanoparticles plays a strong role both in guiding the nucleation and growth of the catalytically active ultrasmall Ni nanoclusters, and further in stabilizing these nanoscale Ni catalysts against poisoning by interactions with the BN substrate. We provide detailed spectroscopy characterizations and density functional theory (DFT) calculations to reveal the origin of the high productivity, high selectivity, and high durability exhibited with the Ni/BN nanocatalyst and elucidate its correlation with nanocluster size and support–nanocluster interactions. This study provides insight into the role that the support material can have both regarding the size control of nanoclusters through immobilization during the nanocluster formation and also during the active catalytic process; this twofold set of insights is significant in advancing the understanding the bottom-up design of high-performance, durable catalytic systems for various catalysis needs.

08 HYDROGEN↗
DOE OSTI2024

Impact of intermolecular interactions on the spectroscopic signals, energetics, and redox behavior of high valent 237 Np (Final Report)

Our studies have led to definitive spectral assignments for neptunyl-neptunyl interactions in aqueous solutions that will be valuable for predicting Np(V) speciation in solution. We have definitively demonstrated how actinyl-hydrogen and actinyl-cation interactions can influence spectral features for uranyl and neptunyl system. In addition, we have developed DFT methodologies to predict enthalpies of formation for uranyl and neptunyl systems and linked stability to chemical descriptors (hydrogen interaction energies or the electrostatic attraction energies). The counterion influenced the rate of Np(V) oxidation to Np(VI) neptunates and [Np(VII)O 4 OH] 3- complexes. Oxidation to Np(VII) during ozonolysis takes place through the presence of hydroxyl radicals.

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

Stability and mobility of tungsten clusters on tungsten (110) surface: Ab initio and atomistic simulations

Quantifying the surface transport properties of tungsten (W) is of prime importance to understand the formation of nano-fuzz in fusion plasma-facing conditions. The stability and mobility of W adatom clusters (W n , n = 2-9) on the W(110) surface has been investigated by computer simulations, including ab initio calculations using density functional theory (DFT) and molecular statics (MS) simulations with multiple W interatomic potentials. The DFT results demonstrate that the sequential binding energy generally increases with number of W adatoms, except for the 5 th and 7 th W adatoms. The most common elemental migration steps of W n (n>2) clusters are observed to consist of monomer and dimer hops, while larger W n clusters can also diffuse by dissociation and recombination of smaller clusters. The threshold migration energy of W 9 is the highest, then followed by W 8 , W 4 , and W 6 , while W 3 , W 5 , and W 7 have similar migration energies. Compared to DFT, each interatomic potential evaluated overestimates the binding energies of W n clusters. Additionally, the embedded-atom potential developed by Juslin and Wirth adequately predicts the threshold migration energy of W n (n>2) clusters, although it predicts different underlying migration mechanisms. The results show that interaction mechanism between W adatoms controls the stability and mobility of W n clusters on the W(110) surface.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DFT Plus DMFT Study of Spin-Charge-Lattice Coupling in Covalnt LaCoO{siub 3}.

We study energetics and the nature of both homogeneous and mixed spin (MS) states in LaCoO3 incorporating structural changes of the crystal volume expansion and the Co-O bond disproportionation (BD) during the spin-state transition using the density functional theory plus dynamical mean field theory (DFT + DMFT) method. DFT + DMFT predicts that energetics of both excited spin states are almost the same while DFT + U calculations of the same structures energetically favor the MS states and produce various metastable solutions whose energetics depend sensitively on final spin states. Within DFT + DMFT, the homogeneous spin state in the expanded crystal volume shows the multiconfigurational nature with non-negligible occupancy probabilities of both high spin (HS) and low spin (LS) states along with d(6) and d(7) charge configurations indicating the dynamically fluctuating nature of spin and charge states due to the Co-O covalency. The nature of the MS state under the BD structure reveals that Co sites with the long Co-O bonds develop a Mott insulating state and favor HS with a d(6) configuration, while more covalent Co sites with the short Co-O bonds occupy more LS states with a d(7) configuration and behave as a band insulator, as a result, charge ordering is induced in the BD structure from the spin-state ordering. We also find that both energetics and electronic structure sensitively depend on the Co-O covalency effect, which can be tuned by changing the double counting potential and the resulting d occupancy (N-d), and N-d close to 6.7 is consistent with the nature of the spin-state transition. Our results show that structural changes during the spin-state transition can play an important role in understanding energetics and electronic structure of LaCoO3.

Park, Hyowon↗
DOE OSTI2026

Competing magnetic phases in Cr 3+𝛿 ⁢Te 4 are spatially segregated

Cr 1+𝑥⁢ Te 2 is a self-intercalated van der Waals system that is of current interest for its room-temperature room-temperature ferromagnetic (FM) phases and tunable topological properties. In bulk samples, the strain from the interstitial Cr ions leads to distinct structural phases for different ranges of 𝑥. Early neutron powder diffraction (NPD) measurements on the monoclinic phase Cr 3 ⁢Te 4 (𝑥=0.5) presented evidence for competing FM and antiferromagnetic (AFM) phases. Here we apply neutron diffraction to a single crystal of Cr 3+𝛿 ⁢Te 4 with 𝛿=−0.10 and discover that it consists of two distinct monoclinic phases, one with FM order below 𝑇 C ≈321 K and another that develops AFM order below 𝑇 N ≈86 K. In contrast, we find that a crystal with 𝛿=−0.26 exhibits only FM order below 𝑇 C ≈285 K. The single-crystal analysis is complemented by results obtained with NPD, x-ray powder diffraction, and transmission electron microscopy (TEM) measurements on the 𝛿=−0.10 composition. From observations of spontaneous magnetostriction of opposite sign at 𝑇 C and 𝑇 N , along with the TEM evidence for both monoclinic phases in a single thin ( ≈100 nm) grain, we conclude that the two phases must have a fine-grained ( ≲100 nm) intergrowth character, as might occur from high-temperature spinodal decomposition during the growth process. Calculations of the relaxed lattice structures for the FM and AFM phases with density functional theory provide a rationalization of the observed spontaneous magnetostrictions. Correlations between the magnitude and orientation of the magnetic moments with lattice parameter variation demonstrate that the magnetic orders are sensitive to strain, thus explaining why magnetic ordering temperatures and anisotropies can be different between bulk and thin-film samples, when the latter are subject to epitaxial strain. Our results point to the need to investigate the supposed coexistence FM and AFM phases reported elsewhere in the Cr 1+𝑥 ⁢Te 2 system, such as in the Cr 5 ⁢Te 8 phase (𝑥=0.25).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

09 BIOMASS FUELS↗
DOE OSTI2021

Increasing Ionic Conductivity of Poly(ethylene oxide) by Reaction with Metallic Li

Poly(ethylene oxide) (PEO) was the first lithium-ion conducting polymer developed 50 years ago and is still the most popular electrolyte matrix for solid-state lithium metal batteries. While many studies focus on increasing PEO ionic conductivity through doping with Li salts, little work has addressed using PEO and Li directly to generate Li + -conducting species in situ. Reaction between PEO and Li leads to ionic conductivity largely from Li + , in contrast to the case of added salts where the anion contribution dominates. Herein, electrochemical impedance spectroscopy shows the ionic conductivity of PEO thin films increases up to three orders of magnitude (from 10 –7 to 10 –4 S cm –1 ) when contacted with Li at elevated temperature. This is due to the reduction of ether bonds, which produces lithium alkoxides that are responsible for Li + transport. Density functional theory analysis confirms this mechanism as thermodynamically favorable. X-ray photoelectron spectroscopy also shows the presence of organolithium species and Li 2 O, which are responsible for propagating reactions with PEO and forming an electronically insulating layer at the PEO–Li interface that halts further reaction, respectively. The underlying mechanisms of Li–polymer electrolyte reactions is clarified and new pathways for in situ Li + doping of polymer electrolytes is presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

First Principles Nonadiabatic Excited-State Molecular Dynamics in NWChem

Computational simulation of non-adiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons, to name a few. Here, we report an implementation of the fewest-switches surface hopping algorithm in the NWChem computational chemistry program. Here the surface hopping method is combined with linear-response time-dependent density functional theory calculations of adiabatic excited state potential energy surfaces. To treat quantum transitions between arbitrary electronic Born{Oppenheimer states, we have implemented both numerical and analytical differentiation schemes for derivative non-adiabatic couplings. A numerical approach for the time-derivative non-adiabatic couplings together with an analytical method for calculating non-adiabatic coupling vectors is an efficient combination for surface hopping approaches. Additionally, electronic decoherence schemes and a state reassigned unavoided crossings algorithm are also implemented to improve the accuracy of the simulated dynamics and to handle trivial unavoided crossings. We apply our code to study the ultrafast decay of photoexcited benzene, including a detailed analysis of the potential energy surface, population decay time scales, and vibrational coordinates coupled to the excitation dynamics. The development presented in this work is a baseline for future implementations of more sophisticated frameworks for simulating of non-adiabatic molecular dynamics in NWChem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Stability Analysis of Substituted Cobaltocenium [Bis(cyclopentadienyl)cobalt(III)] Employing Chemistry-Informed Neural Networks

Cobaltocenium derivatives are promising components of the anion exchange membranes due to their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here we present an efficient modeling approach of assessing the chemical stability of substituted cobaltocenium CoCp 2 + based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCp 2 + OH¯ complexes, whose dissociation is studied within the implicit solvent employing density functional theory. The data set of about 118 species based on 42 substituent groups characterized by a range of electron- donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron withdrawing-donating character of the substituents. Based on this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, are developed. Both models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 5.40% and average absolute errors less than 0.94 kcal/mol. The results show the potential of QNN to efficiently capture more complex relationships. Here, the concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the conventional regression analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Real-Time Time-Dependent Density Functional Theory for Simulating Nonequilibrium Electron Dynamics

The explicit real-time propagation approach for time-dependent density functional theory (RT-TDDFT) has increasingly become a popular first-principles computational method for modeling various time-dependent electronic properties of complex chemical systems. Here, in this Perspective, we provide a nontechnical discussion of how this first-principles simulation approach has been used to gain novel physical insights into nonequilibrium electron dynamics phenomena in recent years. Following a concise overview of the RT-TDDFT methodology from a practical standpoint, we discuss our recent studies on the electronic stopping of DNA in water and the Floquet topological phase as examples. Our discussion focuses on how RT-TDDFT simulations played a unique role in deriving new scientific understandings. We then discuss existing challenges and some new advances at the frontier of RT-TDDFT method development for studying increasingly complex dynamic phenomena and systems.

Hamiltonians↗
DOE OSTI2022

Training and projecting: A reduced basis method emulator for many-body physics

Here, we present the reduced basis method as a tool for developing emulators for equations with tun able parameters within the context of the nuclear many-body problem. The method uses a basis expansion informed by a set of solutions for a few values of the model parameters and then projects the equations over a well-chosen low-dimensional subspace. We connect some of the results in the eigenvector continuation literature to the formalism of reduced basis methods and show how these methods can be applied to a broad set of problems. As we illustrate, the possible success of the formalism on such problems can be diagnosed beforehand by a principal component analysis. We apply the reduced basis method to the one-dimensional Gross-Pitaevskii equation with a harmonic trap ping potential and to nuclear density functional theory for 48 Ca, achieving speed-ups of more than x150 and x250, respectively, when compared to traditional solvers. The outstanding performance of the approach, together with its straightforward implementation, show promise for its application to the emulation of computationally demanding calculations, including uncertainty quantification.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Sparse expansions of multicomponent oxide configuration energy using coherency and redundancy

We report that compressed sensing has become a widely accepted paradigm to construct high dimensional cluster expansion models used for statistical mechanical studies of atomic configuration in complex multicomponent crystalline materials. However, strict sampling requirements necessary to obtain minimal coherence measurements for compressed sensing to guarantee accurate estimation of model parameters are difficult and in some cases impossible to satisfy due to the inability of physical systems to access certain configurations. Nevertheless, the dependence of energy on atomic configuration can still be adequately learned without these strict requirements by using compressed sensing by way of coherent measurements using redundant function sets known as frames. We develop a particular frame constructed from the union of all occupancy-based cluster expansion basis sets. We illustrate how using this highly redundant frame yields sparse expansions of the configuration energy of complex oxide materials that are competitive and often surpass the prediction accuracy and sparsity of models obtained from standard cluster expansions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. Here, in this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chelating Rare-Earth Metals (Ln 3+ ) and 225 Ac 3+ with the Dual-Size-Selective Macrocyclic Ligand Py 2 -Macrodipa

Radioisotopes of metallic elements, or radiometals, are widely employed in both therapeutic and diagnostic nuclear medicine. For this application, chelators that efficiently bind the radiometal of interest and form a stable metal–ligand complex with it are required. Toward the development of new chelators for nuclear medicine, we recently reported a novel class of 18-membered macrocyclic chelators that is characterized by their ability to form stable complexes with both large and small rare-earth metals (Ln 3+ ), a property referred to as dual size selectivity. A specific chelator in this class called py-macrodipa, which contains one pyridyl group within its macrocyclic core, was established as a promising candidate for 135 La 3+ , 213 Bi 3+ , and 44 Sc 3+ chelation. Building upon this prior work, here we report the synthesis and characterization of a new chelator called py 2 -macrodipa with two pyridyl units fused into the macrocyclic backbone. Its coordination chemistry with the Ln 3+ series was investigated by NMR spectroscopy, X-ray crystallography, density functional theory (DFT) calculations, analytical titrations, and transchelation assays. These studies reveal that py 2 -macrodipa retains the expected dual size selectivity and possesses an enhanced thermodynamic affinity for all Ln 3+ compared to py-macrodipa. By contrast, the kinetic stability of Ln 3+ complexes with py 2 -macrodipa is only improved for the light, large Ln 3+ ions. Based upon these observations, we further assessed the suitability of py 2 -macrodipa for use with 225 Ac 3+ , a large radiometal with valuable properties for targeted α therapy. Radiolabeling and stability studies revealed py 2 -macrodipa to efficiently incorporate 225 Ac 3+ and to form a complex that is inert in human serum over 3 weeks. Although py 2 -macrodipa does not surpass the state-of-the-art chelator macropa for 225 Ac 3+ chelation, it does provide another effective 225 Ac 3+ chelator. Furthermore, these studies shed light on the fundamental coordination chemistry of the Ln 3+ series and may inspire future chelator design efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Atomic Three-Dimensional Investigations of Pd Nanocatalysts for Acetylene Semi-hydrogenation

Deciphering the three-dimensional (3D) insight into nanocatalyst surfaces at the atomic level is crucial to understanding catalytic reaction mechanisms and developing high-performance catalysts. Nevertheless, better understanding the inherent insufficiency of a long-range ordered lattice in nanocatalysts is a big challenge. Here, in this work, we report the local structure of Pd nanocatalysts, which is beneficial for demonstrating the shape-structure-adsorption relationship in acetylene hydrogenation. The 5.27 nm spherical Pd catalyst (Pd sph ) shows an ethylene selectivity of 88% at complete acetylene conversion, which is much higher than those of the Pd octahedron and Pd cube and superior to other reported monometallic Pd nanocatalysts so far. By virtue of the local structure revelation combined with the atomic pair distribution function (PDF) and reverse Monte Carlo (RMC) simulation, the atomic surface distribution of the unique compressed strain of Pd-Pd pairs in Pd sph was revealed. Density functional theory calculations verified the obvious weakening of the ethylene adsorption energy on account of the surface strain of Pd sph . It is the main factor to avoid the over-hydrogenation of acetylene. The present work, entailing shape-induced surface strain manipulation and atomic 3D insight, opens a new path to understand and optimize chemical activity and selectivity in the heterogeneous catalysis process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗