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DOE OSTI2021

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Abstract Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open‐access global archive housing three‐dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB‐designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction). The RCSB PDB research‐focused web portal ( RCSB.org ) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal ( PDB101.RCSB.org ) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB‐designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org ; and (v) supports training, outreach, and education via PDB101.RCSB.org . New tools and features at RCSB.org are presented using examples drawn from high‐resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Nonradiative Recombination Dominates Voltage Losses in Cu(In,Ga)Se 2 Solar Cells Fabricated using Different Methods

Voltage losses reduce the photovoltaic conversion efficiency of thin‐film solar cells and are a primary efficiency limitation in Cu(In,Ga)Se 2 . Herein, voltage loss analysis of Cu(In,Ga)Se 2 solar cells fabricated at three institutions with variation in process, bandgap, absorber structure, postdeposition treatment (PDT), and efficiency is presented. Nonradiative voltage losses due to Shockley–Read–Hall charge carrier recombination dominate and constitute >75% of the total compared to <25% from radiative voltage losses. The radiative voltage loss results from nonideal absorption and carriers in band tails that stem from local composition‐driven potential fluctuations. It is shown that significant bulk lifetime improvements are achieved for all alkali PDT processed absorbers, chiefly associated with reductions in nonradiative recombination. Primary voltage loss contributions (radiative and nonradiative) change little across fabrication processes, but variation in submechanisms (bulk lifetime, net acceptor concentration, and interface recombination) differentiate nonradiative loss pathways in this series of solar cells.

14 SOLAR ENERGY↗

DOE OSTI2020

Protection Scheme for Multiterminal HVDC System Based on Wavelet Transform Modulus Maxima

Fault protection is a major issue in the development of multiterminal high‐voltage direct current (HVDC) system based on voltage‐source converter. Owing to the rapid increase in fault current under DC faults, protection requires a fast fault detection method to isolate the fault current. However, the selectivity and reliability of the fast protection scheme should be primarily guaranteed. Therefore, a new protection strategy based on modulus maximum under wavelet transform is proposed in this paper. Fault detection and discrimination are achieved using single‐end signals of fault current. The external and internal faults can be discriminated via local information. The reliability and robustness of the proposed scheme against potential factors are verified. Numerous simulation cases are performed in PSCAD/EMTDC under different fault scenarios, and signal data are analyzed in MATLAB to demonstrate the effectiveness of the proposed fault protection scheme. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Huai, Qing↗

DOE OSTI2022

“I Would Purposely Try to Keep them Separated”: Language Ideologies, Language Policy, and Beliefs about Emergent Bilinguals in Career and Technical Education

Abstract In the policy context of college and career readiness, career and technical education (CTE) is growing in popularity, especially for students who are underserved in academic high school programs. While CTE has shown promise for some students, very little research exists on its potential for emergent bilingual students (EBs). Thus, building on research of deficit perceptions of EBs and language ideologies (LIs), this study examines how LIs and deficit perspectives are reproduced in CTE contexts across Pennsylvania. Using a sequential mixed‐methods design, we found that, while CTE educators held generally positive attitudes toward EBs, they were influenced by monoglossic language ideology and standard language ideology. These LIs, along with other institutional factors, led educators to implement restrictive language policies within their classrooms. These implications serve to inform how, with a lack of clear language policy within CTE programs, teachers relied on their personal beliefs and reproduced deficit ideologies in the creation of classroom‐level policies.

Emerick, Mark R.↗

DOE OSTI2020

The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Magnesium Technology 2020

Magnesium and its alloys are being investigated widely for applications that require extraordinarily high strength-to-weight ratio, vibration damping, electromagnetic shielding, reduction in carbon footprint, sustainability improvements, and low toxicity. In the past decade or so, this drive has led to increased adoption of magnesium and its alloys in everyday products and it is no longer regarded as an esoteric material. The overarching goal of its widespread adoption and replacement of conventional materials such as steel and aluminum alloys has been restricted because of the cost of the material, limited sources of primary material, alloying constraints, high chemical reactivity, and challenges associated with its plasticity. Researchers, scientists, engineers, and economists from industry, government agencies and laboratories, and academic institutions alike are actively developing roadmaps for next-generation products and addressing these challenges as quickly as possible through unique and innovative methods. The TMS Magnesium Committee has been actively involved in providing a platform to these entities for disseminating the latest information, developments, and cutting-edge research and development, and showcasing the latest research and development trends related to magnesium and its alloys through the Magnesium Technology Symposium, which takes place every year at the TMS Annual Meeting. This proceedings volume retains the essence of this longstanding tradition. The twenty-first volume in the series, Magnesium Technology 2020, is the proceedings of the Magnesium Technology Symposium held during the 149th TMS Annual Meeting & Exhibition in San Diego, CA, February 23–27, 2020. The volume captures commentaries and papers from 15 different countries. The papers have been categorized systematically based on topics pertaining to magnesium production, casting and solidification, thermomechanical processing, deformation mechanisms, modeling, corrosion, and applications. The symposium began with keynote sessions that featured several distinguished invited speakers from government organizations and academia, who provided their perspectives on the state of the art, goals, and opportunities in magnesium alloy research and development. Dr. Carlos Tomé from Los Alamos National Laboratory, U.S. Department of Energy, discussed 3D characterization of mechanical twins across grain boundaries. This was followed by a talk on hierarchically structured ultrafine-grained magnesium alloys by Dr. Rajiv Mishra from the University of North Texas, USA, who presented an example of using friction stir additive manufacturing for implementing such microstructures at a component level. Dr. Mark Horstemeyer from Liberty University, USA, presented an overview of fatigue modeling to capture the corrosion-fatigue behavior of magnesium alloys. The final talk in this session was presented by Dr. Bin Jiang from Chongqing University, China, who presented a novel extrusion approach for achieving high-strength magnesium alloy plates.

Jordon, J. Brian↗

DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗

DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗

DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗

DOE OSTI2026

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets () according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ -aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by -aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a Privacy Inference Attack that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL -aware aggregation. To address this challenge, we propose IntraShuffler, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving -aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.

Riya, Farhin Farhad [ORNL]↗

DOE OSTI2025

Laser powder bed fusion parameter estimation with k-NN

Abstract Laser powder bed fusion (L-PBF) is a technique within additive manufacturing that uses a high power density laser to build parts from fused powdered metal alloy. This technology is well equipped to produce complex parts with otherwise impossible features, such as hidden voids or lattice structures. Alongside capability, reliability and quality are key characteristics considered when choosing a manufacturing method, and these are gaining attention as this method becomes more prevalent in industry. One main indicator of a stable L-PBF process is consistent melt pool geometry, and the properties of which are likely to determine the quality of the part produced. As computing power and sensing technologies become more advanced, this melt pool geometry could be studied in real time. This work addresses the challenge by leveraging a k-nearest neighbor (k-NN) model to identify key features within melt pool imagery and predict the energy density. The k-NN model was trained on data provided by the National Institute of Standards and Technology (NIST). Data preprocessing was performed on the images to extract features that were used in the k-NN model. This approach was used to accurately infer the energy density of unseen layers within the same part. The algorithm was subsequently tested with unique scan strategies and found to reasonably estimate the energy density of different parts. A fivefold cross validation found the algorithm to be consistently predicting the class of 91.4% of the in situ melt pool images.

Jung, Patrick (ORCID:0000000267890859)↗

DOE OSTI2020

A reference high-pressure CH4 adsorption isotherm for zeolite Y: results of an interlaboratory study

Abstract This paper reports the results of an international interlaboratory study led by the National Institute of Standards and Technology (NIST) on the measurement of high-pressure surface excess methane adsorption isotherms on NIST Reference Material RM 8850 (Zeolite Y), at 25 °C up to 7.5 MPa. Twenty laboratories participated in the study and contributed over one-hundred adsorption isotherms of methane on Zeolite Y. From these data, an empirical reference equation was determined, along with a 95% uncertainty interval ( U k =2 ). By requiring participants to replicate a high-pressure reference isotherm for carbon dioxide adsorption on NIST Reference Material RM 8852 (ZSM-5), this interlaboratory study also demonstrated the usefulness of reference isotherms in evaluating the performance of high-pressure adsorption experiments.

Nguyen, H. G. T. (ORCID:0000000210527565)↗

DOE OSTI2022

Reference surface excess isotherms for carbon dioxide adsorption on ammonium ZSM-5 at various temperatures

Abstract This work is part of the effort at the Facility for Adsorbent Characterization and Testing (FACT lab) at the National Institute of Standards and Technology (NIST) to develop reference materials, reference data, and measurement best practices for adsorption metrology. High-pressure surface excess isotherms for CO 2 adsorption on NIST Reference Material 8852 (ammonium ZSM-5) at 15 °C, 25 °C, and 35 °C are reported, expanding on a FACT Lab-organized international interlaboratory study on this sorbent/sorbate pair at 20 °C. The range of temperatures of the present study is of interest for many CO 2 adsorption measurements and applications. Measurements were made using five different adsorption instruments, both manometric and gravimetric. Excellent agreement in the measured isotherms among the instruments was found. An empirical reference equation of the form, $${n}_{ex,ref}({P}_{eq})=\frac{c}{\{{1+\mathrm{exp}[\left(-\mathrm{ln}\left(P\right)+a\right)/b ]\}}^{b }} ,$$ n e x , r e f ( P eq ) = c { 1 + exp [ - ln P + a / b ] } b , [ n ex,ref is surface excess uptake (mmol/g); P eq is equilibrium pressure (MPa); P is ( P eq )/(1 MPa)]; a , b , c are constants] and the 95% uncertainty interval were determined for the isotherms at each of the temperatures. Lastly, the isosteric heat of adsorption is estimated from absolute isotherms derived from the surface excess reference data.

Nguyen, Huong Giang T. (ORCID:0000000210527565)↗

DOE OSTI2022

Bioremediation of heavy oily sludge: a microcosms study

Oily sludge is a residue from the petroleum industry composed of a mixture of sand, water, metals, and high content of hydrocarbons (HCs). The heavy oily sludge used in this study originated from Colombian crude oil with high density and low American Petroleum Institute (API) gravity. The residual waste from heavy oil processing was subject to thermal and centrifugal extraction, resulting in heavy oily sludge with very high density and viscosity. Biodegradation of the total petroleum hydrocarbons (TPH) was tested in microcosms using several bioremediation approaches, including: biostimulation with bulking agents and nutrients, the surfactant Tween 80, and bioaugmentation. Select HC degrading bacteria were isolated based on their ability to grow and produce clear zones on different HCs. Degradation of TPH in the microcosms was monitored gravimetrically and with gas chromatography (GC). The TPH removal in all treatments ranged between 2 and 67%, regardless of the addition of microbial consortiums, amendments, or surfactants within the tested parameters. The results of this study demonstrated that bioremediation of heavy oily sludge presents greater challenges to achieve regulatory requirements. Additional physicochemical treatments analysis to remediate this recalcitrant material may be required to achieve a desirable degradation rate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2022

Skinny kelp (Saccharina angustissima) provides valuable genetics for the biomass improvement of farmed sugar kelp (Saccharina latissima)

Abstract Saccharina latissima (sugar kelp) is one of the most widely cultivated brown marine macroalgae species in the North Atlantic and the eastern North Pacific Oceans. To meet the expanding demands of the sugar kelp mariculture industry, selecting and breeding sugar kelp that is best suited to offshore farm environments is becoming necessary. To that end, a multi-year, multi-institutional breeding program was established by the U.S. Department of Energy's (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program. Hybrid sporophytes were generated using 203 unique gametophyte cultures derived from wild-collected Saccharina spp . for two seasons of farm trials (2019–2020 and 2020–2021). The wild sporophytes were collected from 10 different locations within the Gulf of Maine (USA) region, including both sugar kelp ( Saccharina latissima ) and the skinny kelp species ( Saccharina angustissima ). We harvested 232 common farm plots during these two seasons with available data. We found that farmed kelp plots with skinny kelp as parents had an average increased yield over the mean (wet weight 2.48 ± 0.90 kg m −1 and dry weight 0.32 ± 0.10 kg m −1 ) in both growing seasons. We also found that blade length positively correlated with biomass in skinny kelp x sugar kelp crosses or pure sugar kelp crosses. The skinny x sugar progenies had significantly longer and narrower blades than the pure sugar kelp progenies in both seasons. Overall, these findings suggest that sugar x skinny kelp crosses provide improved yield compared to pure sugar kelp crosses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Updated design of the CMB polarization experiment satellite LiteBIRD

Recent developments of transition-edge sensors (TESs), based on extensive experience in ground-based experiments, have been making the sensor techniques mature enough for their application on future satellite CMB polarization experiments. LiteBIRD is in the most advanced phase among such future satellites, targeting its launch in Japanese Fiscal Year 2027 (2027FY) with JAXA's H3 rocket. It will accommodate more than 4000 TESs in focal planes of reflective low-frequency and refractive medium-and-high-frequency telescopes in order to detect a signature imprinted on the cosmic microwave background (CMB) by the primordial gravitational waves predicted in cosmic inflation. The total wide frequency coverage between 34GHz and 448GHz enables us to extract such weak spiral polarization patterns through the precise subtraction of our Galaxy's foreground emission by using spectral differences among CMB and foreground signals. Telescopes are cooled down to 5Kelvin for suppressing thermal noise and contain polarization modulators with transmissive half-wave plates at individual apertures for separating sky polarization signals from artificial polarization and for mitigating from instrumental 1/f noise. Passive cooling by using V-grooves supports active cooling with mechanical coolers as well as adiabatic demagnetization refrigerators. Sky observations from the second Sun-Earth Lagrangian point, L2, are planned for three years. An international collaboration between Japan, USA, Canada, and Europe is sharing various roles. In May 2019, the Institute of Space and Astronautical Science (ISAS), JAXA selected LiteBIRD as the strategic large mission No. 2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗