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At least 451 records · Page 25

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DOE OSTI2025

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for X-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS analysis pipeline that features several interconnected key building blocks: benchmarks, workflows, databases, and AI/ML models. Specifically, we present two case studies for XAS ML. In the first study, we demonstrate the importance of reconciling the discrepancies between simulation and experiment using spectral domain mapping (SDM). Our ML model, which is trained solely on simulated spectra, predicts an incorrect oxidation state trend for Ti atoms in a combinatorial zinc titanate film. After transforming the experimental spectra into a simulation-like representation using SDM, the same model successfully recovers the correct oxidation state trend. In the second study, we explore the development of universal XAS ML models that are trained on the entire periodic table, which enables them to leverage common trends across elements. Looking ahead, we envision that an AI-driven pipeline can unlock the potential of real-time XAS analysis to accelerate scientific discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Unconventional nonlinear Hall effects in twisted multilayer 2D materials

We present the first investigation of unusual nonlinear Hall effects in twisted multilayer 2D materials. Contrary to expectations, our study shows that these nonlinear effects are not merely extensions of their monolayer counterparts. Instead, we find that stacking order and pairwise interactions between neighboring layers, mediated by Berry curvatures, play a pivotal role in shaping their collective nonlinear optical response. By combining large-scale Real-Time Time-Dependent Density Functional Theory (RT-TDDFT) simulations with model Hamiltonian analyses, we demonstrate a remarkable second-harmonic transverse response in hexagonal boron nitride four-layers, even in cases where the total Berry curvature cancels out. Furthermore, our symmetry analysis of the layered structures provides a simplified framework for predicting nonlinear responses in multilayer materials in general. Our investigation challenges the prevailing understanding of nonlinear optical responses in layered materials and opens new avenues for the design and development of advanced materials with tailored optical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advanced spectroscopic studies of (PPh 4 ) 2 [Co(N 3 ) 4 ], a field-induced single-ion magnet

The high-spin Co II complex (PPh 4 ) 2 [Co(N 3 ) 4 ] (Co-N 3 )has been investigated using advanced spectroscopic techniques [far-IR magneto-spectroscopy (FIRMS), high-frequency and high-field EPR (HFEPR), and inelastic neutron scattering (INS)] to study its zero-field-splitting (ZFS), giving spin-Hamiltonian (SH) parameters. The analysis of multi-frequency HFEPR reveals the easy-axis anisotropy with a D value of −10.39(5) cm −1 and a rhombic ratio (E/D) of 0.21(1). The magnetic properties have also been probed by direct-current (DC) magnetometry, suggesting minor differences in anisotropy from the previously reported polymorph (Co-N 3 ′). Ligand-field theory (LFT) analysis indicates that the structures of Co-N 3 and Co-N 3 ′ are closer to D 2d symmetry than other symmetries considered. Alternate-current (AC) susceptibility reveals slow magnetic relaxation under an applied field, indicating that Co-N 3 is a field-induced single-ion magnet (SIM). Here, while both Co-N 3 and Co-N 3 ′ were studied by DC magnetometry, one unusual aspect of the current work on Co-N 3 is that advanced spectroscopies HFEPR, FIRMS, and INS were used to directly observe transitions between ZFS split states, giving accurate SH parameters.

Hand, Adam T. [Univ. of Tennessee, Knoxville, TN (↗

DOE OSTI2025

Topological perturbation to a standard dehydrogenation catalyst, Pt 3 Sn

Topological materials, which exhibit protected topological surface states (TSS) near the Fermi level, have been proposed to be good catalysts. Topological catalysis may be more prevalent than we suspect, and not limited to exotic new materials. Here we study a known dehydrogenation catalyst, Pt 3 Sn alloy, which happens to be a topological semimetal, and probe the participation of TSSs in catalytic dehydrogenation of methane catalyzed by this material. Through first principle modeling and detailed analysis of the electronic structure for topological and non-topological surfaces of Pt 3 Sn, we find that TSS get significantly altered by the binding of reaction intermediates, particularly H. However, this effect of TSS on the binding of the reagents is merely perturbative, as the majority of the adsorbate binding is achieved by not-surface-focused electronic states, located much deeper below the Fermi level. Therefore, the reaction energetics and selectivity are predominantly determined by electronic states other than TSS. The fact that TSS are available for the reagent binding does not alone guarantee that the catalysis is strongly driven by TSS. However, TSS are not to be ignored, as small changes in the energetics along the reaction profile can translate into substantial differences in the reaction rate. Hence, in our view, Pt 3 Sn – a topological material – is first and foremost a standard catalyst, with added topological features, and not purely a topological catalyst. Our results point at the need to carefully consider all the bonding effects at the topological material interface.

Chemistry↗

DOE OSTI2024

High repetition-rate 0.5 Hz broadband neutron source driven by the Advanced Laser Light Source

Neutron beams are an essential tool to investigate material structure and perform nondestructive analysis, as they give unique access to element composition, thus ideally complementing density analysis allowed by standard x-rays investigation. Laser-driven neutron sources, though compact and cost-effective, currently have lower average flux than conventional neutron sources, due to the limited repetition rate of the lasers used so far. However, advancements in laser technology allow nowadays to address this challenge. Here, we report results obtained at the Advanced Laser Light Source characterizing stable production of broadband (0.1–2 MeV) neutrons produced at a high repetition rate (0.5 Hz). The interaction of laser pulses of 22 fs duration and 3.2 J on-target energy with 2-μm-thick tantalum targets produced protons in the Target Normal Sheath Acceleration (TNSA) regime up to 7.3 MeV. These protons were subsequently converted into neutrons by (p,n) reactions in lithium fluoride (LiF). Activation measurements and bubble detectors were used to characterize neutron emissions, with a neutron fluence of up to ∼1.4×105 neutrons/shot/sr and energies mainly between a few hundred of kilo-electron volt and 2 MeV. The total neutron yield was ∼5×105 neutrons/shot. This paves the way for numerous applications, e.g., in homeland security, materials science, or cultural heritage.

Physics↗

DOE OSTI2025

On the discretization error of the discrete generalized quantum master equation

The transfer tensor method (TTM) [Cerrillo and Cao, Phys. Rev. Lett. 112 , 110401 (2014)] can be considered a discrete-time formulation of the Nakajima–Zwanzig quantum master equation (NZ-QME) for modeling non-Markovian quantum dynamics. A recent paper [Makri, J. Chem. Theory Comput. 21 , 5037 (2025)] raised concerns regarding the consistency of the TTM discretization, particularly a spurious term at the initial time t = 0. Here, this work presents a detailed analysis of the discretization structure of the TTM, clarifying the origin of the initial-time correction and establishing a consistent relationship between the TTM discrete-time memory kernel K N and the continuous-time NZ-QME kernel $\mathscr{K}$( N Δ t ). This relationship is validated numerically using the spin-boson model, demonstrating convergence of reconstructed memory kernels and accurate dynamical evolution as Δ t → 0. While the TTM provides a consistent discretization, we note that alternative schemes are also viable, such as the midpoint derivative/midpoint integral scheme proposed in Makri’s work. The relative performance of various schemes for either computing accurate $\mathscr{K}$( N Δ t ) from exact dynamics or obtaining accurate dynamics from exact $\mathscr{K}$( N Δ t ) warrants further investigation.

Density-matrix↗

DOE OSTI2025

Cholesterol-dependent enzyme activity of human TSPO1

The amino acid sequence of the tryptophan-rich sensory proteins (TSPO) is substantially conserved throughout all kingdoms of life. Human mitochondrial TSPO1 (HsTSPO1) binds to porphyrins and steroids, although its interactions with these molecules remains unknown.HsTSPO1 is associated with numerous physiological and pathological disorders, but the underlying molecular mechanisms are unknown. Here, we disclose the finding of human mitochondrial TSPO as a cholesterol-dependent protoporphyrin IX oxygenase. The results of our biochemical characterization are consistent with structural data and evolutionary analysis. The dependence ofHsTSPO1 activity on cholesterol may be the result of the coevolution of this membrane protein with the membrane system. Our study provides a molecular foundation for comprehending the various roles played by mitochondrial TSPO in normal physiological and pathological situations.

Science & Technology - Other Topics↗

DOE OSTI2024

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges

Abstract Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Biochemistry & Molecular Biology↗

DOE OSTI2025

Generalized approach for rapid entropy calculation of liquids and solids

We build a comprehensive methodology for the fast computation of entropy across both solid and liquid phases. The proposed method utilizes a single trajectory of molecular dynamics (MD) to facilitate the calculation of entropy, which is composed of three components. The electronic entropy is determined through the temporal average acquired from density functional theory MD simulations. The vibrational entropy, typically the predominant contributor to the total entropy, even within the liquid state, is evaluated by computing the phonon density of states via the velocity autocorrelation function. The most arduous component to quantify, the configurational entropy, is assessed by probability analysis of the local structural arrangement and atomic distribution. We illustrate, through a variety of examples, that this method is both a versatile and valid technique for characterizing the thermodynamic states of both solids and liquids. Furthermore, this method is employed to expedite the calculation of melting temperatures, demonstrating its practical utility in computational thermodynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗

DOE OSTI2024

Poplar

SAND2025-00683O Poplar is a software tool that generates a phylogenetic tree from input gene and genome sequences. It integrates established tools to identify genes within genomes, group sequences, construct gene trees, and infer a species tree. Poplar processes nucleotide sequences, identifies similar sequences using Nucleotide BLAST, groups them with DBSCAN, aligns sequences with MAFFT, constructs gene trees with RAxML-NG, and infers a species tree using ASTRAL-Pro3. This pipeline provides a structured approach to phylogenetic analysis, facilitating the study of evolutionary relationships among species. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Krishnakumar, Raga↗

DOE OSTI2026

Cyber-informed Engineering Dashboard Tool

The CIE Engineered Controls Database is a large collection of entries and stored in a json structure which makes manual analysis of entries challenging. The dashboard provides a user with a simple interface to look through the database contents.

Lampe, BenjaminR↗

DOE OSTI2025

Seed coat transcriptomic profiling of 5-593, a genotype important for genetic studies of seed coat color and patterning in common bean ( Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) market classes have distinct seed coat colors, which are directly related to the diverse flavonoids found in the mature seed coat. To understand and elucidate the molecular mechanisms underlying the regulation of seed coat color, RNA-Seq data was collected from the black bean 5-593 and used for a differential gene expression and enrichment analysis from four different seed coat color development stages. 5-593 carries dominant alleles for 10 of the 11 major genes that control seed coat color and expression and has historically been used to develop introgression lines used for seed coat genetic analysis. Pairwise comparison among the four stages identified 6,294 differentially expressed genes (DEGs) varying from 508 to 5,780 DEGs depending on the compared stages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction comprised the principal pathways expressed during bean seed coat pigment development. Transcriptome analysis suggested that most structural genes for flavonoid biosynthesis and some potential regulatory genes were significantly differentially expressed. Further studies detected 29 DEGs as important candidate genes governing the key enzymatic flavonoid biosynthetic pathways for common bean seed coat color development. Additionally, four gene models, Pv5-593.02G016100, 593.02G078700, Pv5-593.02G090900, and Pv5-593.06G121300, encode MYB-like transcription factor family protein were identified as strong candidate regulatory genes in anthocyanin biosynthesis which could regulate the expression levels of some important structural genes in flavonoid biosynthesis pathway. These findings provide a framework to draw new insights into the molecular networks underlying common bean seed coat pigment development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Viral Nuclease Inhibitors: Small molecule disruptors of the UL12 alkaline nuclease display broad anti-herpes virus activity

Herpes simplex virus 1 (HSV-1) UL12 encodes a highly conserved 5′ → 3′ alkaline exonuclease that is essential for the production of infectious virus. Together with the viral single-stranded DNA-binding/annealing protein ICP8, UL12 functions as a two-component recombinase that mediates recombination-dependent viral DNA replication. Here, we present the crystal structure of the catalytic domain of the HSV alkaline nuclease (UL12), which provides the first view of an α-herpesvirus alkaline nuclease. Using this structure, we optimized a series of small-molecule viral nuclease inhibitors (VNIs) that target the UL12 active site and potently inhibit UL12 exonuclease activity in vitro. We have thus established a robust platform for structure-based docking, SAR analysis and rational inhibitor design. Because UL12 orthologs are conserved across all human herpesviruses, we examined the activity of these compounds against the β- and γ-herpesvirus alkaline nucleases UL98 and SOX and found that they inhibit all three enzymes. The VNIs also exhibit antiviral activity against HSV-1 and HCMV in cell culture. EC 50 and IC 50 values were in the nanomolar to low micromolar range. Together, these findings establish herpesvirus alkaline nucleases as conserved, druggable antiviral targets and provide a foundation for the development of broad-spectrum anti-herpesvirus therapeutics, either as standalone agents or in combination with existing nucleoside analogs.

Sharma, Nidhi↗

DOE OSTI2024

Sierra/SD – User's Manual – 5.22

Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis of weapons systems. This document provides a user’s guide to the input for Sierra/SD. Details of input specifications for the different solution types, output options, element types and parameters are included. The appendices contain detailed examples, and instructions for running the software on parallel platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Sierra/SD – User's Manual (V.5.24)

42 ENGINEERING↗

DOE OSTI2026

Sierra/SD - Users Manual - 5.28

Sierra/SD provides a massively parallel implementation of structural dynamics finite element analysis, required for high-fidelity, validated models used in modal, vibration, static and shock analysis. This document provides a user's guide to the input for Sierra/SD. Details of input specifications for the different solution types, output options, element types and parameters are included.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗