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DOE OSTI2025

Coexistence of ordered and disordered vacancies in tungsten diboride with a broken hexagonal symmetry

As an important member of transition-metal borides, tungsten diboride (i.e., WB 2+𝑥 ) contains complex atomic vacancies with the originally assigned hexagonal symmetries, exhibiting many fascinating properties such as superconductivity and superior hardness. However, due to the difficulties in exploring atomic vacancies of transition-metal borides, the actual structure and composition of WB 2+𝑥 have been long-standing unsettled issues, which have impeded in-depth understanding of its structural stability and origins of such extraordinary properties. Here, we report a systematic investigation of the crystal structure of high-pressure synthesized WB 2+𝑥 samples by combination of state-of-the-art diffraction techniques and microstructural observations, leading to the discovery of an unusual coexistence of both the ordered and disordered atomic vacancies in WB 2+𝑥 with a broken hexagonal symmetry that can be well described by a dual-phase model involving hP12-W 0.70 ⁢B 1.73 and P1-W 0.75⁢ B 1.64 . Superposition of thin sample layers with disordered vacancies along the [001] or [110] direction is revealed to produce ordered vacancies, rationalizing the observed size-dependent symmetry breaking. In conclusion, these findings not only provide solid foundations for studying the phase stability and properties of this boride but also give powerful insights into how the intricate atomic vacancies can influence crystal structures of transition-metal borides.

high pressure↗

DOE OSTI2024

Prospects for exotic h → 4 τ decays in single and di-Higgs boson production at the LHC and future hadron colliders

We study the prospects for observing exotic decays of the Standard Model Higgs boson h into light beyond the Standard Model scalars a with mass m a ≲ m h / 2 in the single Higgs and Higgs pair production channels at the high luminosity run of the Large Hadron Collider (HL-LHC). Discovery prospects for single Higgs production in the gluon-gluon fusion and vector boson fusion modes with the Higgs boson decaying via the exotic mode h → a a → 4 τ are analyzed at the HL-LHC. The projected sensitivity for exotic Higgs decays in the nonresonant Higgs pair production channel p p → h h → ( h → b b ¯ ) ( h → a a → 4 τ ) → 2 b 4 τ at the HL-LHC and a future s = 100 TeV hadron collider (FCC-hh) are also estimated. Furthermore, we study HL-LHC’s potential reach for the Higgs-strahlung process in the 2 b 4 τ channel, taking into account the contamination from nonresonant Higgs pair production. Finally, the potential reach for resonant Higgs pair production in the 2 b 4 τ channel at the HL-LHC is also explored for several choices of { m H , m a } . Our studies suggest that significant improvements over existing bounds are achievable in several production channels, motivating new dedicated searches for h → a a → 4 τ at the HL-LHC and future colliders. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2022

Report of the Snowmass 2021 Topical Group on Lattice Gauge Theory

Lattice gauge theory continues to be a powerful theoretical and computational approach to simulating strongly interacting quantum field theories, whose applications permeate almost all disciplines of modern-day research in High-Energy Physics. Whether it is to enable precision quark- and lepton-flavor physics, to uncover signals of new physics in nucleons and nuclei, to elucidate hadron structure and spectrum, to serve as a numerical laboratory to reach beyond the Standard Model, or to invent and improve state-of-the-art computational paradigms, the lattice-gauge-theory program is in a prime position to impact the course of developments and enhance discovery potential of a vibrant experimental program in High-Energy Physics over the coming decade. This projection is based on abundant successful results that have emerged using lattice gauge theory over the years: on continued improvement in theoretical frameworks and algorithmic suits; on the forthcoming transition into the exascale era of high-performance computing; and on a skillful, dedicated, and organized community of lattice gauge theorists in the U.S. and worldwide. The prospects of this effort in pushing the frontiers of research in High-Energy Physics have recently been studied within the U.S. decadal Particle Physics Planning Exercise (Snowmass 2021), and the conclusions are summarized in this Topical Report.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Cortical proteins may provide motor resilience in older adults

Motor resilience proteins may be a high value therapeutic target that offset the negative effects of pathologies on motor function. This study sought to identify cortical proteins associated with motor decline unexplained by brain pathologies that provide motor resilience. We studied 1226 older decedents with annual motor testing, postmortem brain pathologies and quantified 226 proteotypic peptides in prefrontal cortex. Twenty peptides remained associated with motor decline in models controlling for ten brain pathologies (FDR < 0.05). Higher levels of nine peptides and lower levels of eleven peptides were related to slower decline. A higher motor resilience protein score based on averaging the levels of all 20 peptides was related to slower motor decline, less severe parkinsonism and lower odds of mobility disability before death. Cortical proteins may provide motor resilience. Targeting these proteins in further drug discovery may yield novel interventions to maintain motor function in old age.

Buchman, Aron↗

DOE OSTI2023

Optical potentials for the rare-isotope beam era

Abstract We review recent progress and motivate the need for further developments in nuclear optical potentials that are widely used in the theoretical analysis of nucleon elastic scattering and reaction cross sections. In regions of the nuclear chart away from stability, which represent a frontier in nuclear science over the coming decade and which will be probed at new rare-isotope beam facilities worldwide, there is a targeted need to quantify and reduce theoretical reaction model uncertainties, especially with respect to nuclear optical potentials. We first describe the primary physics motivations for an improved description of nuclear reactions involving short-lived isotopes, focusing on its benefits for fundamental science discoveries and applications to medicine, energy, and security. We then outline the various methods in use today to build optical potentials starting from phenomenological, microscopic, and ab initio methods, highlighting in particular, the strengths and weaknesses of each approach. We then discuss publicly-available tools and resources facilitating the propagation of recent progresses in the field to practitioners. Finally, we provide a set of open challenges and recommendations for the field to advance the fundamental science goals of nuclear reaction studies in the rare-isotope beam era. This paper is the outcome of the Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program ‘Optical Potentials in Nuclear Physics’ held in March 2022 at FRIB. Its content is non-exhaustive, was chosen by the participants and reflects their efforts related to optical potentials.

42 ENGINEERING↗

DOE OSTI2024

tomoCAM : fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms

X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM , is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter -based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A New Era of Discovery: The 2023 Long-Range Plan for Nuclear Science (V.1.2)

Nuclear science is the investigation of how protons and neutrons are formed from elementary particles and how the forces between those particles produce both nuclei and the vast variety of nuclear phenomena that occur in the universe. It has evolved into a broad field that addresses profound scientific questions: Where does the mass of visible matter come from? How do stars ignite, live, and die? How do nuclei illuminate the search for new laws of nature? This science points the way to using nuclei to build new technologies that benefit society. The 2015 Nobel Prize in physics was shared by nuclear physicists Art McDonald and Takaaki Kajita for the discovery of neutrino oscillations, which confirmed that neutrinos have mass. Our progress on big questions like this one since 2015 has been remarkable owing to new experimental tools, theoretical breakthroughs, powerful computational techniques, and the talented people who make these innovations possible. Focusing on these new tools, the Facility for Rare Isotope Beams (FRIB) at Michigan State University is already producing exciting results on decays of never-before-produced isotopes a year after it was completed on time and on budget. The energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) was also completed on schedule and on budget—new data from this facility are revealing the spectrum, structure, and dynamics of protons, neutrons, nuclei, and mesons. On the theory front, we can now calculate the distribution of quarks inside the proton from first principles. The implementation of artificial intelligence (AI) and machine learning (ML) techniques has led to improved data analysis and increased efficiency in running experiments and theoretical calculations. The impact of nuclear science goes beyond expanding the frontiers of knowledge about matter in the universe. We simultaneously develop a STEM work force that advances the security, technology, health, and wealth of our nation. Some connections are obvious. Expert scientists trained to work with radioactive nuclei are in demand in nuclear security arenas and are highly sought after by various government agencies and private industries. Graduate students and postdoctoral fellows (postdocs) obtain extensive computational, modeling, and data science skills that are similarly in high demand. Less obvious but equally important is the connection between these trained scientists and success in other professions, including medicine, energy, and entrepreneurial pursuits. The workforce that enables discovery in nuclear science also makes breakthroughs in technologies with tremendous impact on the nation’s economic advancement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Detection of the keto-enol tautomerization in acetaldehyde, acetone, cyclohexanone, and methyl vinyl ketone with a novel VUV light source

The discovery of enols in combustion environments and our atmosphere has garnered increasing attention to the many unanswered questions surrounding enol chemistry. The scarcity of experimental data concerning these enols renders combustion and atmospheric models with a lack of constraining parameters, leading to varying computational predictions. Experimental detection is difficult because mass spectrometry, a powerful tool for probing a wide variety of species, cannot distinguish between enols and their thermodynamically favorable ketone isomers. A solution to this ambiguity is to use tunable vacuum ultraviolet (VUV) light from a synchrotron to identify the presence of the enol by its lower ionization energy compared to the isomer. We present a tabletop-scale VUV light source that implements highly cascaded harmonic generation, a new regime of cascaded nonlinear optics, to provide a set of spectral lines spaced by 1.2 eV. We demonstrate that the variety of photon energies available allows us to detect the keto-enol tautomerization of four aldehydes and ketones. By combining this novel VUV light source with an established microreactor, we first revisit the formation of vinyl alcohol from acetaldehyde and confirm that the observed isomerization is indeed unimolecular. Secondly, we observe the thermal tautomerization of acetone to propen-2-ol for the first time. Finally, we observe the thermal tautomerization of cyclohexanone to 1-cyclohexenol and methyl vinyl ketone to 2-hydroxybutadiene, where the results are in good agreement with those reported at a synchrotron. Furthermore, our measurements can be used to constrain models, inform future experimental studies of enol reactivity, and potentially enhance current understanding of combustion and environmental chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

Conductivity spectroscopy is an extremely powerful set of methods for probing the properties of optoelectronic materials, especially photovoltaics, where photoconductivity is one of the best spectroscopic proxies for performance. Despite this power, they are substantially less commonly used than time-resolved photoluminescence (for instance) because they tend to be more expensive to implement (THz) and/or require specialized knowledge (GHz) to construct instruments, which are not widely available. The goal of this review is to illustrate the utility of these experiments in the discovery and study of photovoltaic absorber materials and simultaneously make them more accessible to the community by providing a central tutorial resource. We provide a comprehensive review of how conductivity spectroscopy has developed over the past decade and been applied in the discovery and development of photovoltaic materials, with a primary focus on emerging solution-processable technologies. Along the way we aim to demystify conductivity spectroscopy with focused tutorial sections that explain the physical models used to fit the data and illustrate how to think about “high-frequency conductivity”.

14 SOLAR ENERGY↗

DOE OSTI2024

Persistent flat band splitting and strong selective band renormalization in a kagome magnet thin film

Magnetic kagome materials provide a fascinating playground for exploring the interplay of magnetism, correlation and topology. Many magnetic kagome systems have been reported including the binary Fe m X n (X = Sn, Ge; m:n = 3:1, 3:2, 1:1) family and the rare earth RMn 6 Sn 6 (R = rare earth) family, where their kagome flat bands are calculated to be near the Fermi level in the para magnetic phase. While partially filling a kagome flat band is predicted to give rise to a Stoner-type ferromagnetism, experimental visualization of the mag netic splitting across the ordering temperature has not been reported for any of these systems due to the high ordering temperatures, hence leaving the nature of magnetism in kagome magnets an open question. Here, we probe the electronic structure with angle-resolved photoemission spectroscopy in a kagome magnet thin film FeSn synthesized using molecular beam epitaxy. We identify the exchange-split kagome flat bands, whose splitting persists above the magnetic ordering temperature, indicative of a local moment picture. Such local moments in the presence of the topological flat band are consistent with the compact molecular orbitals predicted in theory. We further observe a large spin-orbital selective band renormalization in the Fe d xy + d x 2 -y 2 spin majority channel reminiscent of the orbital selective correlation effects in the iron based superconductors. Our discovery of the coexistence of local moments with topological flat bands in a kagome system echoes similar findings in magic-angle twisted bilayer graphene, and provides a basis for theoretical effort towards modeling correlation effects in magnetic flat band systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Terahertz-field activation of polar skyrons

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discover subterahertz collective modes, dubbed “skyrons”, which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. The key to activating skyrons is the use of the THz field that couples primarily to skyrmion domain walls. Momentum-resolved time-domain measurements of diffuse scattering reveal an avoided crossing in the dispersion relation of skyrons. Atomistic simulations and dynamical phase-field modeling provide microscopic insights into the three-dimensional crystallographic and polarization dynamics. The amplitude and dispersion of skyrons are demonstrated to be controlled by sample temperature and electric-field bias. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

ferroelectrics↗

DOE OSTI2021

Effects of growth substrate on the nucleation of monolayer MoTe 2

The discovery and characterization of two-dimensional (2D) materials beyond graphene has increased dramatically over the past decade with increasingly fine control over the growth dynamics of these materials. MoTe 2 represents a model material for studying phase change properties of 2D materials due to small energy differences between its 2H semiconducting phase and 1T' semimetallic phase. Although some substrates are demonstrated to be better than others for the wafer-scale growth of high quality MoTe 2 films, substrate effects on the nucleation and growth of MoTe 2 are still not well understood. Here, we grow monolayer MoTe 2 by converting MoO x thin films deposited on three different substrates: sapphire Al 2 O 3 (0001), amorphous SiO 2 , and amorphous AlO x , and examine the early stages of the conversion reaction to elucidate the substrate effects on the nucleation of MoTe 2 . We observe that the chemical composition of the substrate is more important than the surface topography and crystallinity of the substrate, with high quality monolayer 2H MoTe 2 formed on both Al 2 O 3 (0001) and AlO x in contrast to mixed phase 2H/1T' MoTe 2 formed on SiO 2 , as determined by Raman spectroscopy, X-ray photoelectron spectroscopy, and atomic force microscopy.

36 MATERIALS SCIENCE↗

DOE OSTI2020

From core collapse to superluminous: the rates of massive stellar explosions from the Palomar Transient Factory

ABSTRACT We present measurements of the local core-collapse supernova (CCSN) rate using SN discoveries from the Palomar Transient Factory (PTF). We use a Monte Carlo simulation of hundreds of millions of SN light-curve realizations coupled with the detailed PTF survey detection efficiencies to forward model the SN rates in PTF. Using a sample of 86 CCSNe, including 26 stripped-envelope SNe (SESNe), we show that the overall CCSN volumetric rate is $r^\mathrm{CC}_v=9.10_{-1.27}^{+1.56}\times 10^{-5}\, \text{SNe yr}^{-1}\, \text{Mpc}^{-3}\, h_{70}^{3}$ at 〈z〉 = 0.028, and the SESN volumetric rate is $r^\mathrm{SE}_v=2.41_{-0.64}^{+0.81}\times 10^{-5}\, \text{SNe yr}^{-1}\, \text{Mpc}^{-3}\, h_{70}^{3}$. We further measure a volumetric rate for hydrogen-free superluminous SNe (SLSNe-I) using eight events at z ≤ 0.2 of $r^\mathrm{SLSN-I}_v=35_{-13}^{+25}\, \text{SNe yr}^{-1}\text{Gpc}^{-3}\, h_{70}^{3}$, which represents the most precise SLSN-I rate measurement to date. Using a simple cosmic star formation history to adjust these volumetric rate measurements to the same redshift, we measure a local ratio of SLSN-I to SESN of ${\sim}1/810^{+1500}_{-94}$, and of SLSN-I to all CCSN types of ${\sim}1/3500^{+2800}_{-720}$. However, using host galaxy stellar mass as a proxy for metallicity, we also show that this ratio is strongly metallicity dependent: in low-mass (logM* < 9.5 M⊙) galaxies, which are the only environments that host SLSN-I in our sample, we measure an SLSN-I to SESN fraction of $1/300^{+380}_{-170}$ and $1/1700^{+1800}_{-720}$ for all CCSN. We further investigate the SN rates a function of host galaxy stellar mass, and show that the specific rates of all CCSNe decrease with increasing stellar mass.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Bioinformatics and 3D Structural Analysis of the Coronavirus Main Protease Active Site Diversity

Coronaviruses (Coronaviridae) such as SARS‐CoV‐2 (severe acute respiratory syndrome coronavirus) and MERS‐CoV (Middle East respiratory syndrome coronavirus) have been the source of recent outbreaks and global health concerns. While vaccines have been essential for controlling the SARS‐CoV‐2 (COVID‐19) pandemic, it is uncertain whether they will be effective against future coronavirus strains. Therefore, identification or design of a broad‐spectrum drug that targets highly conserved regions of the main protease of multiple coronavirus strains is essential in the long term. As part of a virtual summer research experience with the RCSB PDB, bioinformatics tools were employed to predict and construct 3D models of the coronavirus main protease (MPro) using SARS‐CoV‐2 as the template, with a focus on mutational trends and active sites. This study focused on the active sites of MPro, a cysteine protease essential for viral assembly and replication. Sequence alignments and structure modeling of MPro structures has identified conserved regions across multiple coronavirus strains. Inhibition of MPro halts coronavirus replication, making it an ideal drug target, and studies of MPro may foster and accelerate the discovery of high affinity broad‐spectrum drugs.

Wu Wu, Amy↗

DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

FORGE: Pre-Training Open Foundation Models for Science

Large language models (LLMs) are poised to revolutionize the way we conduct scientific research. However, both model complexity and pre-training cost are impeding effective adoption for the wider science community. Identifying suitable scientific use cases, finding the optimal balance between model and data sizes, and scaling up model training are among the most pressing issues that need to be addressed. In this study, we provide practical solutions for building and using LLM-based foundation models targeting scientific research use cases. We present an end-to-end examination of the effectiveness of LLMs in scientific research, including their scaling behavior and computational requirements on Frontier, the first Exascale supercomputer. We have also developed for release to the scientific community a suite of open foundation models called FORGE with up to 26B parameters using 257B tokens from over 200M scientific articles, with performance either on par or superior to other state-of-the-art comparable models. We have demonstrated the use and effectiveness of FORGE on scientific downstream tasks. Our research establishes best practices that can be applied across various fields to take advantage of LLMs for scientific discovery.

Yin, Junqi↗