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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗
DOE OSTI2024

An artificial intelligence accelerated virtual screening platform for drug discovery

Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na V 1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na V 1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Science & Technology - Other Topics↗
DOE OSTI2018

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Furthermore, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2023

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗
DOE OSTI2022

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (~1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗
DOE OSTI2025

Combining MicroED and native mass spectrometry for structural discovery of enzyme–small molecule complexes

With the goal of accelerating the discovery of small molecule–protein complexes, we leverage fast, low-dose, event-based electron counting microcrystal electron diffraction (MicroED) data collection and native mass spectrometry. This approach, which we term electron diffraction with native mass spectrometry (ED-MS), allows assignment of protein target structures bound to ligands with data obtained from crystal slurries soaked with mixtures of known inhibitors and crude biosynthetic reactions. This extends to libraries of printed ligands dispensed directly onto TEM grids for later soaking with microcrystal slurries, and complexes with noncovalent ligands. ED-MS resolves structures of the natural product, epoxide-based cysteine protease inhibitor E-64, and its biosynthetic analogs bound to the model cysteine protease, papain. It further identifies papain binding to its preferred natural products, by showing that two analogs of E-64 outcompete others in binding to papain crystals, and by detecting papain bound to E-64 and an analog from crude biosynthetic reactions, without purification. ED-MS also resolves binding of the CTX-M-14 β-lactamase, a target of active drug development, to the non-β-lactam inhibitor, avibactam, alone or in a cocktail of unrelated compounds. These results illustrate the utility of ED-MS for natural product ligand discovery and for structure-based screening of small molecule binders to macromolecular targets, promising utility for drug discovery.

MicroED↗
DOE OSTI2012

DISCOVERY OF FIVE NEW PULSARS IN ARCHIVAL DATA

Reprocessing of the Parkes Multibeam Pulsar Survey has resulted in the discovery of five previously unknown pulsars and several as-yet-unconfirmed candidates. PSR J0922-52 has a period of 9.68 ms and a dispersion measure (DM) of 122.4 pc cm{sup -3}. PSR J1147-66 has a period of 3.72 ms and a DM of 133.8 pc cm{sup -3}. PSR J1227-6208 has a period of 34.53 ms, a DM of 362.6 pc cm{sup -3}, is in a 6.7 day binary orbit, and was independently detected in an ongoing high-resolution Parkes survey by Thornton et al. and also in independent processing by Einstein-Home volunteers. PSR J1546-59 has a period of 7.80 ms and a DM of 168.3 pc cm{sup -3}. PSR J1725-3853 is an isolated 4.79 ms pulsar with a DM of 158.2 pc cm{sup -3}. These pulsars were likely missed in earlier processing efforts due to the fact that they have both high DMs and short periods, and also due to the large number of candidates that needed to be looked through. These discoveries suggest that further pulsars are awaiting discovery in the multibeam survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

THE 2011 ERUPTION OF THE RECURRENT NOVA T PYXIDIS: THE DISCOVERY, THE PRE-ERUPTION RISE, THE PRE-ERUPTION ORBITAL PERIOD, AND THE REASON FOR THE LONG DELAY

We report the discovery by M. Linnolt on JD 2,455,665.7931 (UT 2011 April 14.29) of the sixth eruption of the recurrent nova T Pyxidis. This discovery was made just as the initial fast rise was starting, so with fast notification and response by observers worldwide, the entire initial rise was covered (the first for any nova), and with high time resolution in three filters. The speed of the rise peaked at 9 mag day{sup -1}, while the light curve is well fit over only the first two days by a model with a uniformly expanding sphere. We also report the discovery by R. Stubbings of a pre-eruption rise starting 18 days before the eruption, peaking 1.1 mag brighter than its long-time average, and then fading back toward quiescence 4 days before the eruption. This unique and mysterious behavior is only the fourth known (with V1500 Cyg, V533 Her, and T CrB) anticipatory rise closely spaced before a nova eruption. We present 19 timings of photometric minima from 1986 to 2011 February, where the orbital period is fast increasing with P/ P-dot =+313,000 yr. From 2008 to 2011, T Pyx had a small change in this rate of increase, so that the orbital period at the time of eruption was 0.07622950 {+-} 0.00000008 days. This strong and steady increase of the orbital period can only come from mass transfer, for which we calculate a rate of (1.7-3.5) Multiplication-Sign 10{sup -7} M{sub Sun} yr{sup -1}. We report 6116 magnitudes between 1890 and 2011, for an average B = 15.59 {+-} 0.01 from 1967 to 2011, which allows for an eruption in 2011 if the blue flux is nearly proportional to the accretion rate. The ultraviolet-optical-infrared spectral energy distribution is well fit by a power law with f{sub {nu}}{proportional_to}{nu}{sup 1.0}, although the narrow ultraviolet region has a tilt with a fit of f{sub {nu}}{proportional_to}{nu}{sup 1/3}. We prove that most of the T Pyx light is not coming from a disk, or any superposition of blackbodies, but rather is coming from some nonthermal source. We confirm the extinction measure from IUE with E(B - V) = 0.25 {+-} 0.02 mag.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A MODERN SEARCH FOR WOLF–RAYET STARS IN THE MAGELLANIC CLOUDS. II. A SECOND YEAR OF DISCOVERIES

The numbers and types of evolved massive stars found in nearby galaxies provide an exacting test of stellar evolution models. Because of their proximity and rich massive star populations, the Magellanic Clouds have long served as the linchpins for such studies. Yet the continued accidental discoveries of Wolf–Rayet (WR) stars in these systems demonstrate that our knowledge is not as complete as usually assumed. Therefore, we undertook a multi-year survey for WRs in the Magellanic Clouds. Our results from our first year (reported previously) confirmed nine new LMC WRs. Of these, six were of a type never before recognized, with WN3-type emission combined with O3-type absorption features. Yet these stars are 2–3 mag too faint to be WN3+O3 V binaries. Here we report on the second year of our survey, including the discovery of four more WRs, two of which are also WN3/O3s, plus two “slash” WRs. This brings the total of known LMC WRs to 152, 13 (8.2%) of which were found by our survey, which is now ∼60% complete. We find that the spatial distribution of the WN3/O3s is similar to that of other WRs in the LMC, suggesting that they are descended from the same progenitors. We call attention to the fact that 5 of the 12 known SMC WRs may in fact be similar WN3/O3s rather than the binaries they have often assumed to be. We also discuss our other discoveries: a newly discovered Onfp-type star, and a peculiar emission-line object. Finally, we consider the completeness limits of our survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A SEARCH FOR L/T TRANSITION DWARFS WITH PAN-STARRS1 AND WISE. II. L/T TRANSITION ATMOSPHERES AND YOUNG DISCOVERIES

The evolution of brown dwarfs from L to T spectral types is one of the least understood aspects of the ultracool population, partly for lack of a large, well-defined, and well-characterized sample in the L/T transition. To improve the existing census, we have searched ≈28,000 deg{sup 2} using the Pan-STARRS1 and Wide-field Infrared Survey Explorer surveys for L/T transition dwarfs within 25 pc. We present 130 ultracool dwarf discoveries with estimated distances ≈9–130 pc, including 21 that were independently discovered by other authors and 3 that were previously identified as photometric candidates. Seventy-nine of our objects have near-IR spectral types of L6–T4.5, the most L/T transition dwarfs from any search to date, and we have increased the census of L9–T1.5 objects within 25 pc by over 50%. The color distribution of our discoveries provides further evidence for the “L/T gap,” a deficit of objects with (J − K){sub MKO} ≈ 0.0–0.5 mag in the L/T transition, and thus reinforces the idea that the transition from cloudy to clear photospheres occurs rapidly. Among our discoveries are 31 candidate binaries based on their low-resolution spectral features. Two of these candidates are common proper motion companions to nearby main sequence stars; if confirmed as binaries, these would be rare benchmark systems with the potential to stringently test ultracool evolutionary models. Our search also serendipitously identified 23 late-M and L dwarfs with spectroscopic signs of low gravity implying youth, including 10 with vl-g or int-g gravity classifications and another 13 with indications of low gravity whose spectral types or modest spectral signal-to-noise ratio do not allow us to assign formal classifications. Finally, we identify 10 candidate members of nearby young moving groups (YMG) with spectral types L7–T4.5, including three showing spectroscopic signs of low gravity. If confirmed, any of these would be among the coolest known YMG members and would help to determine the effective temperature at which young brown dwarfs cross the L/T transition.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

FIRST RESULTS FROM Z -FOURGE : DISCOVERY OF A CANDIDATE CLUSTER AT z = 2.2 IN COSMOS

We report the first results from the Z -FOURGE survey: the discovery of a candidate galaxy cluster at z = 2.2 consisting of two compact overdensities with red galaxies detected at {approx}> 20{sigma} above the mean surface density. The discovery was made possible by a new deep (K{sub s} {approx}< 24.8 AB 5{sigma}) Magellan/FOURSTAR near-IR imaging survey with five custom medium-bandwidth filters. The filters pinpoint the location of the Balmer/4000 A break in evolved stellar populations at 1.5 < z < 3.5, yielding significantly more accurate photometric redshifts than possible with broadband imaging alone. The overdensities are within 1' of each other in the COSMOS field and appear to be embedded in a larger structure that contains at least one additional overdensity ({approx}10{sigma}). Considering the global properties of the overdensities, the z = 2.2 system appears to be the most distant example of a galaxy cluster with a population of red galaxies. A comparison to a large {Lambda}CDM simulation suggests that the system may consist of merging subclusters, with properties in between those of z > 2 protoclusters with more diffuse distributions of blue galaxies and the lower-redshift galaxy clusters with prominent red sequences. The structure is completely absent in public optical catalogs in COSMOS and only weakly visible in a shallower near-IR survey. The discovery showcases the potential of deep near-IR surveys with medium-band filters to advance the understanding of environment and galaxy evolution at z > 1.5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A rich and bountiful harvest: Key discoveries in plant cell biology

Abstract The field of plant cell biology has a rich history of discovery, going back to Robert Hooke’s discovery of cells themselves. The development of microscopes and preparation techniques has allowed for the visualization of subcellular structures, and the use of protein biochemistry, genetics, and molecular biology has enabled the identification of proteins and mechanisms that regulate key cellular processes. In this review, seven senior plant cell biologists reflect on the development of this research field in the past decades, including the foundational contributions that their teams have made to our rich, current insights into cell biology. Topics covered include signaling and cell morphogenesis, membrane trafficking, cytokinesis, cytoskeletal regulation, and cell wall biology. In addition, these scientists illustrate the pathways to discovery in this exciting research field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Toward the discovery of matter creation with neutrinoless β β decay

The discovery of neutrinoless ββ decay could soon be within reach. This hypothetical ultrarare nuclear decay offers a privileged portal to physics beyond the standard model of particle physics. Its observation would constitute the discovery of a matter-creating process, corroborating leading theories of why the Universe contains more matter than antimatter, and how forces unify at high energy scales. It would also prove that neutrinos and antineutrinos are not two distinct particles but can transform into each other, with their mass described by a unique mechanism conceived by Majorana. The recognition that neutrinos are not massless necessitates an explanation and has boosted interest in neutrinoless ββ decay. The field stands now at a turning point. Further, a new round of experiments is currently being prepared for the next decade to cover an important region of parameter space. In parallel, advances in nuclear theory are laying the groundwork to connect the nuclear decay with the underlying new physics. Meanwhile, the particle theory landscape continues to find new motivations for neutrinos to be their own antiparticle. This review brings together the experimental, nuclear theory, and particle theory aspects connected to neutrinoless ββ decay to explore the path toward, and beyond, its discovery.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗