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DOE OSTI2023

Improv Dynamic Workflows

A workflow is a series of dependent computations which are executed to yield an experimental result, much like sheet music describes a musical performance. Dynamic workflows are like improvisational jazz, in which the musicians create unique, situation-driven, collaborative variations on themes. Dynamic workflows can potentially yield better results, save computation, and/or save human decision-making effort versus pre-specified or manual workflows, as they need not run the exhaustive set of studies which might be required in a pre-specified set, and they make their own experimental design decisions at run time. If you have an existing Maestro study where you would like to save computation and/or human time, Improv is for you. Improv provides core capabilities for creating a simple, hierarchical structure over Maestro studies, allowing you to connect ``experimental'' Maestro strudies with decision-making code to select parameters and run corresponding studies.

Goforth, John↗

DOE OSTI2024

minervachem

Minervachem is a tool for cheminformatics and machine learning in chemistry. It includes both algorithms from existing literature and algorithms which we have developed. Its features center around two main themes: molecular representations and algorithms, both for molecular machine learning. It provides a scikit-learn transformer interface for molecular featurization and an estimator interface for machine learning algorithms. It also provides visualization tools for explaining machine learning predictions. We aim to continue developing this software to improve its performance and usability and expand its capabilities within the realm of molecular machine learning, cheminformatics, and visualization.

Lubbers, Nicholas↗

DOE OSTI2025

NOVA Trame Library

The nova-frame library provides a simple themed web application based on the Trame Framework

Watson, Gregory [Oak Ridge National Laboratory (OR↗

DOE OSTI2020

Mathematics: The Tao of Data Science

The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and “Challenges and Opportunities in Statistics and Data Science: Ten Research Areas” by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data. We propose that mathematics is an important key to establishing rigor in the field of data science and as such has an essential role to play in its future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

An Atmospheric Aerosol Short Course for Early Career Scientists

The first Department of Energy (DOE) Environmental and Molecular Science Laboratory (EMSL)/Atmospheric Radiation Measurement (ARM) aerosol summer school was held July 15-19, 2019 at the EMSL user facility located at Pacific Northwest National Laboratory. The school set out to provide graduate students and early-career scientists an overview of atmospheric aerosol science and an introduction to the measurement techniques available through the EMSL and ARM user facilities to address current aerosol science issues. Instructors for the course were drawn from scientists from the EMSL (Laskin et al., 2016; China et al., 2018) and ARM facilities (McComiskey and Ferrare, 2016) as well as from the DOE Atmospheric System Research (ASR) program. Student support for the summer school was also jointly provided by EMSL, ARM, and ASR. The school spanned five days with each day covering a particular theme. 25 students from 21 institutions attended morning lectures on aerosol composition, properties, theory, measurements, and modeling. During afternoons, the students explored instruments that collect and analyze aerosol samples at EMSL and PNNL’s Atmospheric Measurements Laboratory. They also tested relevant computer modeling simulations and learned about NWChem, EMSL’s open-source computational chemistry software suite.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Water-soluble saponins accumulate in drought-stressed switchgrass and may inhibit yeast growth during bioethanol production

Abstract Background Developing economically viable pathways to produce renewable energy has become an important research theme in recent years. Lignocellulosic biomass is a promising feedstock that can be converted into second-generation biofuels and bioproducts. Global warming has adversely affected climate change causing many environmental changes that have impacted earth surface temperature and rainfall patterns. Recent research has shown that environmental growth conditions altered the composition of drought-stressed switchgrass and directly influenced the extent of biomass conversion to fuels by completely inhibiting yeast growth during fermentation. Our goal in this project was to find a way to overcome the microbial inhibition and characterize specific compounds that led to this inhibition. Additionally, we also determined if these microbial inhibitors were plant-generated compounds, by-products of the pretreatment process, or a combination of both. Results Switchgrass harvested in drought (2012) and non-drought (2010) years were pretreated using Ammonia Fiber Expansion (AFEX). Untreated and AFEX processed samples were then extracted using solvents (i.e., water, ethanol, and ethyl acetate) to selectively remove potential inhibitory compounds and determine whether pretreatment affects the inhibition. High solids loading enzymatic hydrolysis was performed on all samples, followed by fermentation using engineered Saccharomyces cerevisiae . Fermentation rate, cell growth, sugar consumption, and ethanol production were used to evaluate fermentation performance. We found that water extraction of drought-year switchgrass before AFEX pretreatment reduced the inhibition of yeast fermentation. The extracts were analyzed using liquid chromatography–mass spectrometry (LC–MS) to detect compounds enriched in the extracted fractions. Saponins, a class of plant-generated triterpene or steroidal glycosides, were found to be significantly more abundant in the water extracts from drought-year (inhibitory) switchgrass. The inhibitory nature of the saponins in switchgrass hydrolysate was validated by spiking commercially available saponin standard (protodioscin) in non-inhibitory switchgrass hydrolysate harvested in normal year. Conclusions Adding a water extraction step prior to AFEX-pretreatment of drought-stressed switchgrass effectively overcame inhibition of yeast growth during bioethanol production. Saponins appear to be generated by the plant as a response to drought as they were significantly more abundant in the drought-stressed switchgrass water extracts and may contribute toward yeast inhibition in drought-stressed switchgrass hydrolysates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The challenges of theory-software translation

Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Artificial intelligence in computational materials science

In this themed collection we aim to broadly review some of the critical, recent progress in the application of AI/ML to various aspects of computational materials science and materials science more broadly. In this collection spread across two issues, we have assembled a collection of articles from leaders in the broad domain of applying AI/ML, which we collectively refer to as ML, in computational materials science. Together these articles curate the critical, recent progress in the application of ML to various aspects of materials science. Furthermore, these include ML approaches for understanding and driving electron microscopy, designing energy materials and the discovery of principles and materials relevant to the design of materials for the future, studying crystal nucleation and growth, the use of ML to describe force fields governing material and molecular behavior, and other topics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hydrogen in energy and information sciences

Beyond its fascinating chemistry as the first element in the Periodic Table, hydrogen is of high societal importance in energy technologies and of growing importance in energy-efficient computing. In energy, hydrogen has reemerged as a potential solution to long-term energy storage and as a carbon-free input for materials manufacturing. Its utilization and production rely on the availability of proton-conducting electrolytes and mixed proton–electron conductors for the components in fuel cells and electrolyzers. In computing, proton mediation of electronic properties has garnered attention for electrochemically controlled energy-efficient neuromorphic computing. Incorporation of substitutional and interstitial hydride ions in oxides, though only recently established, enables tuning of electronic and magnetic properties, inviting a range of possible exotic applications. This article addresses common themes in the fundamental science of hydrogen incorporation and transport in oxides as relevant to pressing technological needs. The content covers (1) lattice (or bulk) mechanisms of hydrogen transport, primarily addressing proton transport, but also touching on hydride ion transport; (2) interfacial transport; (3) exploitation of extreme external drivers to achieve unusual response; and (4) advances in methods to probe the hydrogen environment and transport pathway. The snapshot of research activities in the field of hydrogen-laden materials described here underscores exciting recent breakthroughs, remaining open questions, and breathtaking experimental tools now available for unveiling the nature of hydrogen in solid-state matter.

08 HYDROGEN↗

DOE OSTI2024

Halide Perovskite Solar Photovoltaics

Technological progress in photovoltaic (PV) technologies provides hope that a comprehensive and desperately needed decarbonization of the energy sector is possible. Commercially successful PV technologies based predominantly on silicon wafer technology are reliable and cost-effective, but remain capital- and carbon-intensive. In this context, emerging PV technologies, such as metal-halide perovskites (MHPs), could further catalyze the energy transition by providing technological opportunities for even lower-cost, mass-producible, high-efficiency solar cells with a significantly reduced "carbon footprint." This themed issue of MRS Bulletin on "Halide perovskite solar photovoltaics summarizes the current state of the art, challenges, and opportunities of perovskite photovoltaics with contributions and perspectives from six expert teams worldwide. The topics covered provide a status update on perovskite PV, remaining hurdles to their deployment, and challenges to realizing the potential of this technology to impact climate goals. Articles in this collection address scalability of perovskite PV and prospects for industrial manufacturing; perovskite PV as an add-on technology on top of commercial silicon PV; environmental and sustainability considerations; and durability and reliability considerations. Further considerations include prospects of automation, coupled to artificial intelligence and machine learning, for accelerating material-based solutions to these outstanding challenges including the possibilities of discovering new absorber and device component materials to enable success and ultimately deployment of these next-generation PVs.

metal-halide perovskites↗

DOE OSTI2025

Materials laboratories of the future for alloys, amorphous, and composite materials

In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗

DOE OSTI2021

2020 ETI Annual Summer School: Data Science and Engineering

The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.

Biegalski, Steven R.↗

DOE OSTI2024

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Snowmass 2021 cross frontier report: Dark matter complementarity

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

Boveia, Antonio [The Ohio State Univ., Columbus, O↗

DOE OSTI2020

Study of shortwave spectra in fully 3D environment: synergy between scanning radars and spectral radiation measurements (final Report)

Overview of 3D atmospheric radiative transfer in cloudy atmosphere for Reports on Progress in Physics. We surveyed the 3D atmospheric radiative transfer literature over the past 50 years and identify three concurrent and intertwining themes: first, how to assess the damage (bias) caused by 3D effects in the operational 1D radiative transfer models? Second, how to mitigate this damage? Third, can we exploit 3D radiative transfer phenomena to invent new observation methods and technologies? Two equally important application areas for atmospheric radiative transfer in general, and 3D in particular, were identified: broadband radiative energy budget estimation and wavelength-specific optical remote sensing signal modeling. As illustrations, both satellite and ARM observations were highly used.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

New Predictive Capabilities for Nuclear Weapons in Composite Fires

The prevalence of flammable carbon-based composite airframe materials and their use in high-temperature nuclear weapon re-entry systems requires analysts to address the abnormal thermal environment hazards associated with composite material fires. These fires tend to burn very differently than conventional fuel fires, usually burning less intensely, but much longer. This could lead to challenges in understanding margins in classic safety themes. The technical challenges in modeling the phenomena associated with these new types of fires are considerable, but new models have been developed. Their predictions have been compared with well-documented measurements of a vertical porous burner fire, known as a "wall fire" (a "wall-fire" validation simulation is reflected in the figures below). These measurements were conducted at FMGlobal, a mutual insurance company with a strong fire risk management program, as part of an ongoing collaboration between Sandia and FMGlobal. To date, the "wall-fire" scenario has been set up and initial model assessments with grid refinement studies have been conducted focusing on mesh resolutions suitable for full weapon system simulations. This work will continue with further verification and validation tasks assessing the predictions of the new model. Future work will address specific aspects of the wall models that are lacking in their predictive ability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Space Strategy at a Crossroads: Opportunities and Challenges for 21st Century Competition

In its November 2018 report to Congress, the bipartisan National Defense Strategy Commission applauded the main themes of the Trump administration’s 2017 National Defense Strategy. But it was also deeply critical of the strategic thought so far done by the U.S. defense community about the requirements, operational and otherwise, of securing U.S. and allied interests in the types of conflicts we now face. Indeed, it assessed these shortfalls as crippling to the exercise of American power and as dangerous in crisis and war. Is the U.S. military space community exempt from such criticism? Should it be?

29 ENERGY PLANNING, POLICY, AND ECONOMY↗