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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 OSTI2021

The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S 0 /S 1 and S 1 /S 2 conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.

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

Fragment optimization and elaboration strategies – the discovery of two lead series of PRMT5/MTA inhibitors from five fragment hits

Here we describe the early stages of a fragment-based lead discovery (FBLD) project for a recently elucidated synthetic lethal target, the PRMT5/MTA complex, for the treatment of MTAP-deleted cancers. Starting with five fragment/PRMT5/MTA X-ray co-crystal structures, we employed a two-phase fragment elaboration process encompassing optimization of fragment hits and subsequent fragment growth to increase potency, assess synthetic tractability, and enable structure-based drug design. Two lead series were identified, one of which led to the discovery of the clinical candidate MRTX1719.

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

High throughput discovery of ternary Cu-Fe-Ru alloy catalysts for photo-driven hydrogen production

Light driven hydrogen production from the water splitting reaction has the ability to reduce dependence on fossil fuels in a green energy future. Here, we highlight the discovery of Cu x Ru y Fe 1-x-y nanoparticle catalysts for photo-driven hydrogen production. Through a high throughput experimental setup, robust data management pipelines and intentional experimental design, this study uncovered three highly active bimetallic systems for photo-driven hydrogen and identified a new trimetallic catalyst for this system. In most cases, the multimetallic catalysts outperformed the monometallics. Furthermore, this study highlights the expansive catalytic screening capabilities of this system in contrast to traditional catalytic selection processes through the discovery of distributions of particle compositions in binary and ternary mixtures of metals with high activity for hydrogen evolution.

08 HYDROGEN↗
DOE OSTI2024

High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis

High-throughput reaction discovery is necessary to understand complex reaction spaces for inorganic nanocrystal synthesis. Here, we implemented a high-throughput continuous flow millifluidic reactor to perform reaction discovery for Cs–Pb–Br nanocrystal synthesis using a ligand assisted reprecipitation (LARP)-type approach. 3D-printed flow resistors enable the screening of up to 16 different mixing ratios within a single 90 s run, allowing for >270 different precursor concentration ratios to be quickly tested to explore the phase space that results in CsPbBr 3 , Cs 4 PbBr 6 , a biphasic mixture, or no product. To construct a full phase map from these high-throughput experiments, a neural network was trained and validated to predict the product composition (~500 000 points in precursor concentration space). The phase map predicts product composition/phase as a function of Cs–Pb–Br feed ratio. As a result, this approach demonstrates how high-throughput flow chemistry can be used in tandem with machine learning to rapidly explore nanocrystal reaction spaces in flow.

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

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

100th Anniversary of the Compton Effect: Its Discovery and Present-Day Impact on Nuclear Physics

This year is the 100th anniversary of the discovery of the Compton effect marked by A. H. Compton’s 1923 Physical Review article. On this occasion, we reflect on the effect’s impact on Nuclear Physics and celebrate the event a century ago and the eminent scientist who made the discovery. Much of the historical comments rely on the article by R. H. Stuewer. A recent Physics Today article presents a historical perspective of Compton’s work and provides more details. The various applications of the Compton effect and its “derivatives” (e.g. inverse Compton scattering) impact many areas of Physics and Chemistry. Here, this short article is restricted to examples taken from the authors' surroundings: the focus is on nuclear-structure experiments with γ-spectroscopic methods and nuclear cross-section measurements relevant for astrophysics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Kaon physics: a cornerstone for future discoveries

The kaon physics programme, long heralded as a cutting-edge frontier by the European Strategy for Particle Physics, continues to stand at the intersection of discovery and innovation in high-energy physics (HEP). With its unparalleled capacity to explore new physics at the multi-TeV scale, kaon research is poised to unveil phenomena that could reshape our understanding of the Universe. This document highlights the compelling physics case, with emphasis on exciting new opportunities for advancing kaon physics not only in Europe but also on a global stage. As an important player in the future of HEP, the kaon programme promises to drive transformative breakthroughs, inviting exploration at the forefront of scientific discovery.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Model-independent discovery prospects for primordial black holes at LIGO

ABSTRACT Primordial black holes may encode the conditions of the early Universe, and may even constitute a significant fraction of cosmological dark matter. Their existence has yet to be established. However, black holes with masses below ${\sim}{1}{\, \mathrm{M}_\odot }$ cannot form as an endpoint of stellar evolution, so the detection of even one such object would be a smoking gun for new physics, and would constitute evidence that at least a fraction of the dark matter consists of primordial black holes. Gravitational wave detectors are capable of making a definitive discovery of this kind by detecting mergers of light black holes. But since the merger rate depends strongly on the shape of the black hole mass function, it is difficult to determine the potential for discovery or constraint as a function of the overall abundance of black holes. Here, we directly maximize and minimize the merger rate to connect observational results to the actual abundance of observable objects. We show that LIGO can discover mergers of light primordial black holes within the next decade even if such black holes constitute only a very small fraction of dark matter. A single merger event involving such an object would (i) provide conclusive evidence of new physics, (ii) establish the nature of some fraction of dark matter, and (iii) probe cosmological history at scales far beyond those observable today.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Discovery and characterization of five new eclipsing AM CVn systems

ABSTRACT AM CVn systems are ultra-compact, hydrogen-depleted, and helium-rich, accreting binaries with degenerate or semidegenerate donors. We report the discovery of five new eclipsing AM CVn systems with orbital periods of 61.5, 55.5, 53.3, 37.4, and 35.4 min. These systems were discovered by searching for deep eclipses in the Zwicky Transient Facility (ZTF) light curves of white dwarfs selected using Gaia parallaxes. We obtained phase-resolved spectroscopy to confirm that all systems are AM CVn binaries, and we obtained high-speed photometry to confirm the eclipse and characterize the systems. The spectra show double-peaked H e lines but also show metals, including K and Zn, elements that have never been detected in AM CVn systems before. By modelling the high-speed photometry, we measured the mass and radius of the donor star, potentially constraining the evolutionary channel that formed these AM CVn systems. We determined that the average mass of the accreting white dwarf is ≈0.8 M⊙, and that the white dwarfs in long-period systems are hotter than predicted by recently updated theoretical models. The donors have a high entropy and are a factor of ≈2 more massive compared to zero-entropy donors at the same orbital period. The large donor radius is most consistent with H e-star progenitors, although the observed spectral features seem to contradict this. The discovery of five new eclipsing AM CVn systems is consistent with the known observed AM CVn space density and estimated ZTF recovery efficiency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Swift /UVOT discovery of Swift J221951−484240: a UV luminous ambiguous nuclear transient

We report the discovery of Swift J221951−484240 (hereafter: J221951), a luminous slow-evolving blue transient that was detected by the Neil Gehrels Swift Observatory Ultraviolet/Optical Telescope (Swift/UVOT) during the follow-up of gravitational wave alert S190930t, to which it is unrelated. Swift/UVOT photometry shows the UV spectral energy distribution of the transient to be well modelled by a slowly shrinking blackbody with an approximately constant temperature of T ∼ 2.5 × 10 4 K. At a redshift z = 0.5205, J221951 had a peak absolute magnitude of M u,AB = −23 mag, peak bolometric luminosity Lmax = 1.1 x 10 45 ergs -1 and a total radiated energy of E > 2.6 × 10 52 erg. The archival Wide-field Infrared Survey Explorer IR photometry shows a slow rise prior to a peak near the discovery date. Spectroscopic UV observations display broad absorption lines in N V and O VI, pointing towards an outflow at coronal temperatures. The lack of emission in the higher H α lines, N I and other neutral lines is consistent with a viewing angle close to the plane of the accretion or debris disc. The origin of J221951 cannot be determined with certainty but has properties consistent with a tidal disruption event and the turn-on of an active galactic nucleus.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Discovery of a z ∼ 0.8 ultra steep spectrum radio halo in the MeerKAT-South Pole Telescope Survey

Radio haloes are diffuse synchrotron sources that trace the turbulent intracluster medium (ICM) of galaxy clusters. However, their origin remains unknown. Two main formation models have been proposed: the hadronic model, in which relativistic electrons are continuously injected by cosmic-ray protons; and the leptonic turbulent re-acceleration model, where cluster mergers re-energize electrons in situ. A key discriminant between the two models would be the existence of ultra-steep spectrum radio haloes (USSRHs), which can only be produced through turbulent re-acceleration. Here, we report the discovery of an USSRH in the galaxy cluster SPT-CLJ2337–5942 at redshift $z = 0.78$ in the MeerKAT-South Pole Telescope 100 deg$^2$ UHF (0.58–1.09 GHz) survey. This discovery is noteworthy for two primary reasons: it is the highest redshift USSRH system to date; and the close correspondence of the radio emission with the thermal ICM as traced by Chandra X-ray observations, further supporting the leptonic re-acceleration model. The halo is underluminous for its mass, consistent with a minor merger origin, which produces steep-spectrum, lower luminosity haloes. This result demonstrates the power of wide-field, high-fidelity, low-frequency ($\lesssim 1$ GHz) surveys like the MeerKAT-SPT 100 deg$^2$ programme to probe the origin and evolution of radio haloes over cosmic time, ahead of the Square Kilometre Array.

X-rays: galaxies↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗