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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Polymorphism in the Ruddlesden–Popper Nickelate La 3 Ni 2 O 7 : Discovery of a Hidden Phase with Distinctive Layer Stacking

We report the discovery of a novel form of Ruddlesden–Popper (RP) nickelate that stands as the first example of long-range, coherent polymorphism in this class of inorganic solids. Rather than the well-known, uniform stacking of perovskite blocks ubiquitously found in RP phases, this newly discovered polymorph of the bilayer RP phase La 3 Ni 2 O 7 adopts a novel stacking sequence in which single-layer and trilayer blocks of NiO 6 octahedra alternate in a “1313” sequence. Crystals of this new polymorph are described in space group Cmmm, although we note evidence for a competing Imam variant. Transport measurements at ambient pressure reveal metallic character with evidence of a charge density wave transition with an onset at T ≈ 134 K. In conclusion, the discovery of such polymorphism could reverberate to the expansive range of science and applications that rely on RP materials, particularly the recently reported signatures of superconductivity in bilayer La 3 Ni 2 O 7 with T c as high as 80 K above 14 GPa.

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

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2026

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗
DOE OSTI2020

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A ligand discovery toolbox for the WWE domain family of human E3 ligases

The WWE domain is a relatively under-researched domain found in twelve human proteins and characterized by a conserved tryptophan-tryptophan-glutamate (WWE) sequence motif. Six of these WWE domain-containing proteins also contain domains with E3 ubiquitin ligase activity. The general recognition of poly-ADP-ribosylated substrates by WWE domains suggests a potential avenue for development of Proteolysis-Targeting Chimeras (PROTACs). Here, we present novel crystal structures of the HUWE1, TRIP12, and DTX1 WWE domains in complex with PAR building blocks and their analogs, thus enabling a comprehensive analysis of the PAR binding site structural diversity. Furthermore, we introduce a versatile toolbox of biophysical and biochemical assays for the discovery and characterization of novel WWE domain binders, including fluorescence polarization-based PAR binding and displacement assays, 15 N-NMR-based binding affinity assays and 19 F-NMR-based competition assays. Through these assays, we have characterized the binding of monomeric iso -ADP-ribose ( iso -ADPr) and its nucleotide analogs with the aforementioned WWE proteins. Finally, we have utilized the assay toolbox to screen a small molecule fragment library leading to the successful discovery of novel ligands targeting the HUWE1 WWE domain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep Koopman operators for causal discovery

Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relationships in nonlinear dynamics due to the presence of complex feedback mechanisms, timescale mixing, and nonstationarity. Thus, applying these methods to study causal dynamics in real-world systems, such as the Earth, is a major challenge. Addressing this shortcoming, we leverage deep learning and a Koopman operator-theoretic formalism to present a class of causal discovery algorithms. Kausal uses deep Koopman operator methods to approximate nonlinear dynamics in a linearized vector space in which traditional causal inference methods such as Granger causality can be more easily applied. Our idealized experiments demonstrate Kausal’s superior ability in discovering and characterizing causal signals compared to existing deep learning and non-deep learning state-of-the-art approaches. Finally, the successful identification of major El Niño and La Niña events in observations showcases Kausal’s skill to handle real-world applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2021

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

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

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

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

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

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

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

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

08 HYDROGEN↗
DOE OSTI2024

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

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

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

Active anomaly detection for time-domain discoveries

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

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2024

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

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

97 MATHEMATICS AND COMPUTING↗