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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
DOE OSTI2014

Use of animal models for exome prioritization of rare disease genes

Over 100 disease-gene associations have been identified by whole-exome sequencing since the first reports in 2010, leading to a revolution in rare disease-gene discovery [1,2]. However, many cases remain unsolved due to the fact that ~100-1000 loss of function, candidate variants remain after removing those deemed as common, low quality or non-pathogenic. In some cases it may be possible to use multiple affected individuals, linkage data, identity-by-descent inference, identification of de novo heterozygous mutations from trio analysis, or prior knowledge of affected pathways to narrow down to the causative variant [3]. Where this is not possible or successful, one approach is to use phenotype data to evaluate whether a particular candidate variant is likely to result in the patient’s clinical manifestations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2018

Machine learning at the energy and intensity frontiers of particle physics

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1995

LDEF contributions to cosmic ray and radiation environments research

LDEF-1 carried three experiments which are producing significant advances in our knowledge of ultra heavy and anomalous cosmic rays, solar flare particles, and heavy nuclei in the trapped belts. Nine other experiments made measurements on the radiation environments or performed dosmetric monitoring. Data from those experiments, and from measurements of induced radioactivity in LDEF components have significantly improved our knowledge of LEO radiation environment. Measurements at various locations shielding depths of radiation absorbed dose, linear energy transfer spectra, proton, neutron and heavy ion fluences, and induced radioactivity have been made, and many of these results have been compared to models. This has allowed the assessment of accuracy, and the potential for improvement, of the models. Serendipitous results from the radiation measurements include the discovery of atmospheric Be-7 plated on the front surface of LDEF, which has motivated a series of new investigations. A sample of measurements and modeling results will be presented, as well as the status of archiving the measurements and models.

Parnell, Thomas A.↗
NASA NTRS2007

Serving Fisheries and Ocean Metadata to Communities Around the World

NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://gcmd.nasa.gov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches, or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage, and data resolution for selected datasets. The directory also offers data holders a means to advertise and search their data through customized portals, which are subset views of the directory. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1988. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as the International Directory Network (IDN), has been providing its services and sharing its experience and knowledge of metadata at the international, national, regional, and local level for many years. Active partners include the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and organizations (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP).

Meaux, Melanie F.↗
DOE OSTI2016

Circular RNAs and systemic lupus erythematosus

Circular RNAs (circRNAs) are a large class of noncoding RNAs that form covalently closed RNA circles. The discovery of circRNAs discloses a new layer of gene regulation occurred post-transcriptionally. Identification of endogenous circRNAs benefits from the advance in high-throughput RNA sequencing and remains challenging. Many studies probing into the mechanisms of circRNAs formation occurred cotranscriptionally or posttranscriptionally emerge and conclude that canonical splicing mechanism, sequence properties, and certain regulatory factors are at play in the process. Although our knowledge on functions of circRNAs is rather limited, a few circRNAs are shown to sponge miRNA and regulate gene transcription. The clearest case is one circRNA CDR1as that serves as sponge of miR-7. Researches on circRNAs in human diseases such as cancers highlight the function and physical relevance of circRNAs. Given the implication of miRNAs in the initiation and progression of systemic lupus erythematosus (SLE) and the roles of circRNAs in sponging miRNA and gene regulation, it is appealing to speculate that circRNAs may associate with SLE and may be potential therapeutic targets for treatment of SLE. Future studies should attach more importance to the relationship between circRNAs and SLE. This review will concern identification, biogenesis, and function of circRNAs, introduce reports exploring the association of circRNAs with human diseases, and conjecture the potential roles of circRNAs in SLE. - Highlights: • Studies have discovered thousands of circRNAs and interpreted their biogenesis. • Cytoplasmic circRNAs sponge miRNA and nuclear circRNAs modulate gene transcription. • Aberrant expression of circRNAs has been observed in various cancers. • CircRNAs may partake in the pathogenesis of systemic lupus erythematosus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Creating a Discovery Platform for Confined-Space Chemistry and Materials: Metal-Organic Frameworks

Metal organic frameworks (MOF) are a recently discovered class of nanoporous, defect-free crystalline materials that enable rational design and exploration of porous materials at the molecular level. MOFs have tunable monolithic pore sizes and cavity environments due to their crystalline nature, yielding properties exceeding those of most other porous materials. These include: the lowest known density (91% free space); highest surface area; tunable photoluminescence; selective molecular adsorption; and methane sorption rivaling gas cylinders. These properties are achieved by coupling inorganic metal complexes such as ZnO4 with tunable organic ligands that serve as struts, allowing facile manipulation of pore size and surface area through reactant selection. MOFs thus provide a discovery platform for generating both new understanding of chemistry in confined spaces and novel sensors and devices based on their unique properties. At the outset of this project in FY06, virtually nothing was known about how to couple MOFs to substrates and the science of MOF properties and how to tune them was in its infancy. An integrated approach was needed to establish the required knowledge base for nanoscale design and develop methodologies integrate MOFs with other materials. This report summarizes the key accomplishments of this project, which include creation of a new class of radiation detection materials based on MOFs, luminescent MOFs for chemical detection, use of MOFs as templates to create nanoparticles of hydrogen storage materials, MOF coatings for stress-based chemical detection using microcantilevers, and "flexible" force fields that account for structural changes in MOFs that occur upon molecular adsorption/desorption. Eight journal articles, twenty presentations at scientific conferences, and two patent applications resulted from the work. The project created a basis for continuing development of MOFs for many Sandia applications and succeeded in securing $\$$2.75 M in funding from outside agencies to continue the research.

Allendorf, Mark D.↗
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 OSTI2022

Snowmass Computational Frontier: Topical Group Report on Quantum Computing

Quantum computing will play a pivotal role in the High Energy Physics (HEP) science program over the early parts of the 21$^{st}$ Century, both as a major expansion of our capabilities across the Computational Frontier, and in synthesis with quantum sensing and quantum networks. This report outlines how Quantum Information Science (QIS) and HEP are deeply intertwined endeavors that benefit enormously from a strong engagement together. Quantum computers do not represent a detour for HEP, rather they are set to become an integral part of our discovery toolkit. Problems ranging from simulating quantum field theories, to fully leveraging the most sensitive sensor suites for new particle searches, and even data analysis will run into limiting bottlenecks if constrained to our current computing paradigms. Easy access to quantum computers is needed to build a deeper understanding of these opportunities. In turn, HEP brings crucial expertise to the national quantum ecosystem in quantum domain knowledge, superconducting technology, cryogenic and fast microelectronics, and massive-scale project management. The role of quantum technologies across the entire economy is expected to grow rapidly over the next decade, so it is important to establish the role of HEP in the efforts surrounding QIS. Fully delivering on the promise of quantum technologies in the HEP science program requires robust support. It is important to both invest in the co-design opportunities afforded by the broader quantum computing ecosystem and leverage HEP strengths with the goal of designing quantum computers tailored to HEP science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Standard and Non-Standard Neutrino Oscillations at the NOvA Experiment

Initially believed to be massless, the discovery of neutrino oscillations proved that neutrinos have masses, a feature not anticipated by the Standard Model of Elementary Particles. Investigations of the existence of the so-called physics Beyond the Standard Model is a subject of active effort in the field of particle physics. Interactions of neutrinos with matter are well described by the Standard Model, however in the presence of new physics our knowledge and ability to measure the standard neutrino oscillation parameters may be affected. Non-Standard Interactions (NSI) may arise as a subleading effect to the neutrino oscillation framework by introducing additional parameters resulting from a generalized matter potential.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Electrochemistry of PUREX: R is for Reduction and Ion Transfer

The PUREX (Plutonium, Uranium, Reduction, EXtraction) process is the foundation of all industrial activities that involve the recycling of plutonium and uranium from used nuclear fuel. With over 70 years of research, engineering, and operations, it can be argued that little opportunity is left for further discovery and new fundamental understandings regarding PUREX, in general, and tri-n-butyl phosphate (TBP), in particular. Through use of an advanced electroanalytical approach, molecular-level insights regarding the back-extraction of Pu(IV) by reductive separation is reported. Studies of model PUREX systems, with lanthanide ions extracted into 20 % TBP in n-dodecane, are used to demonstrate the approach. These studies reveal knowledge pertinent to mass transfer aspects of the biphasic reductive stripping reaction central to the separation of U and Pu in PUREX. Two lanthanide ions—Ce(IV) and Yb(III)—were extracted from their concentrated solutions in aqueous 3 M HNO 3 to expressly provide electrically conducting third phases to facilitate electrochemical data acquisition. To simplify the diabolically-complicated reductive separation aspects of PUREX, the controlled polarization of electrode surfaces was used to reduce Ce(IV) and Yb(III) in place of chemical reductants. Electroanalyses of the biphasic systems with Ce(IV) and Yb(III) using three-phase electrode differential pulse voltammetry demonstrate the effects of mass transfer across water-oil interfaces on the electrode potentials for the reduction to Ce(III) and Yb(II). These reductions trigger nitrate transfer out of the oil phase for Ce and proton transfer into the organic phase for Yb. This is in correlation with the transfer of the charge-neutral ion pairs Ce·3NO 3 and Yb·2NO 3 out of the oil phases. Here, the differences in ion transfer steps reflect significant variations in the equilibrium speciation of multinuclear Ce(IV) and mononuclear Yb(III) solvates of TBP in the oil phases and the loading of the organic phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Screening a knowledge‐based library of low molecular weight compounds against the proline biosynthetic enzyme 1‐pyrroline‐5‐carboxylate 1 ( PYCR1)

Abstract Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 1 (PYCR1) is the last enzyme of proline biosynthesis and catalyzes the NAD(P)H‐dependent reduction of Δ 1 ‐pyrroline‐5‐carboxylate toL‐proline. High PYCR1 gene expression is observed in many cancers and linked to poor patient outcomes and tumor aggressiveness. The knockdown of thePYCR1gene or the inhibition of PYCR1 enzyme has been shown to inhibit tumorigenesis in cancer cells and animal models of cancer, motivating inhibitor discovery. We screened a library of 71 low molecular weight compounds (average MW of 131 Da) against PYCR1 using an enzyme activity assay. Hit compounds were validated with X‐ray crystallography and kinetic assays to determine affinity parameters. The library was counter‐screened against human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3 and proline dehydrogenase (PRODH) to assess specificity/promiscuity. Twelve PYCR1 and one PRODH inhibitor crystal structures were determined. Three compounds inhibit PYCR1 with competitive inhibition parameter of 100 μM or lower. Among these, (S)‐tetrahydro‐2H‐pyran‐2‐carboxylic acid (70 μM) has higher affinity than the current best tool compoundN‐formyl‐l‐proline, is 30 times more specific for PYCR1 over human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3, and negligibly inhibits PRODH. Structure‐affinity relationships suggest that hydrogen bonding of the heteroatom of this compound is important for binding to PYCR1. The structures of PYCR1 and PRODH complexed with 1‐hydroxyethane‐1‐sulfonate demonstrate that the sulfonate group is a suitable replacement for the carboxylate anchor. This result suggests that the exploration of carboxylic acid isosteres may be a promising strategy for discovering new classes of PYCR1 and PRODH inhibitors. The structure of PYCR1 complexed withl‐pipecolate and NADH supports the hypothesis that PYCR1 has an alternative function in lysine metabolism.

Biochemistry & Molecular Biology↗
DOE OSTI2018

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗