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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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Non-linear threshold analysis enabled discovery of a highly soluble (1.3 M) bipyrimidine anolyte. Stable cycling in a full flow battery cell for 75 cycles.
Exploratory synthesis has been the main generator of new inorganic materials for decades. AI-assisted discovery is possible, but human-AI collaboration should be refined according to their respective strengths.
The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.
Our work introduces an innovative deep machine learning framework to significantly accelerate novel materials discovery, as demonstrated by its application to the La–Si–P system where new ternary and quaternary compounds were successfully identified.
Active learning based on literature and experimental data enabled the discovery of highly active novel catalysts for ammonia synthesis. Pathway analysis implies that these activities have been achieved by both structural and electronic promotion.
Correction for ‘Accelerating materials discovery using integrated deep machine learning approaches’ by Weiyi Xia et al. , J. Mater. Chem. A , 2023, 11 , 25973–25982, https://doi.org/10.1039/d3ta03771a.
Natural products have applications as biopharmaceuticals, agrochemicals, and other high-value chemicals. However, there are challenges in isolating natural products from their native producers (e.g. bacteria, fungi, plants). In many cases, synthetic chemistry or heterologous expression must be used to access these important molecules. The biosynthetic machinery to generate these compounds is found within biosynthetic gene clusters, primarily consisting of the enzymes that biosynthesise a range of natural product classes (including, but not limited to ribosomal and nonribosomal peptides, polyketides, and terpenoids). Cell-free synthetic biology has emerged in recent years as a bottom-up technology applied towards both prototyping pathways and producing molecules. Recently, it has been applied to natural products, both to characterise biosynthetic pathways and produce new metabolites. This review discusses the core biochemistry of cell-free synthetic biology applied to metabolite production and critiques its advantages and disadvantages compared to whole cell and/or chemical production routes. Specifically, we review the advances in cell-free biosynthesis of ribosomal peptides, analyse the rapid prototyping of natural product biosynthetic enzymes and pathways, highlight advances in novel antimicrobial discovery, and discuss the rising use of cell-free technologies in industrial biotechnology and synthetic biology.
Cyanobacteria are prolific producers of biologically active compounds that are important in influencing ecology, behavior of interacting organisms, and as leads in drug discovery efforts. Here we discuss the challenges faced by all natural product researchers, especially those that focus on cyanobacteria, and then describe progress that has been made in these areas. We also propose some solutions, paths forward, and thoughts for consideration on these challenges.
We report the discovery and spectroscopic identification of the bright doubly lensed quasar eRASS1 J050129.5–073309 at redshift z = 2.47, selected from the first all-sky survey of the Spectrum Roentgen Gamma (SRG) eROSITA telescope and the Gaia EDR3 catalog. We systematically searched for extragalactic sources with eROSITA X-ray positions that have multiple Gaia counterparts, and we have started spectroscopic follow-up of the most promising candidates using long-slit spectroscopy with NTT/EFOSC2 to confirm the lens nature. The two images are separated by 2.7", and their average Gaia g-band magnitudes are 16.95 and 17.33. Legacy Survey DR10 imaging and image modeling reveal both the lensing galaxy and tentatively the lensed image of the quasar host galaxy. Archival optical light curves show evidence of a variability time delay, with the fainter component lagging the brighter by about 100 days. The brightness of the fainter image has also decreased by about one magnitude since 2019. This dimming was still obvious at the time of the spectroscopic observations and is probably caused by microlensing. The optical spectroscopic follow-up obtained from NTT/EFOSC2 and the evidence provided by the imaging and timing analysis allow us to confirm the lensed nature of eRASS1 J050129.5–073309.
Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token based authentication and authorisation throughout its entire middleware stack. Following the publication of the WLCG Common JSON Web Token (JWT) Schema v1.0 [1] in 2019, middleware developers have been able to enhance their services to consume and validate the JWT-based [2] OAuth2.0 [3] tokens and process the authorization information they convey. Complex scenarios, involving multiple delegation steps and command line flows, are a key challenge to be addressed in order for the system to be fully operational. This paper expands on the anticipated token based workflows, with a particular focus on local storage of tokens and their discovery by services. The authors include a walk-through of this token flow in the RUCIO managed data-transfer scenario, including delegation to FTS and authorised access to storage elements. Next steps are presented, including the current target of submitting production jobs authorised by Tokens within 2021.
Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.
Computational materials discovery has been successful in predicting novel, technologically relevant materials. However, it has remained focused almost exclusively on finding ground-state structures. Now that the lower-hanging fruit has been found in many fields of application, materials exploration is moving toward metastable materials: higher energy phases that are stable at practical time scales. Because of the challenges associated with predicting which phases are realistic, this class of materials has remained relatively unexplored, despite numerous examples of metastable structures with unmatched properties (e.g., diamond). This article highlights recent advances in developing computational and theoretical methods for predicting useful and realizable metastable materials. Topics discussed cover (1) the latest strategies for identifying potential metastable phases, (2) methodologies for assessing which phases can be realized experimentally, and (3) current approaches to estimate the lifetime of metastable materials.
Ultrawide-band-gap (UWBG) semiconductors are promising for fast, compact, and energy-efficient power-electronics devices. Their wider band gaps result in higher breakdown electric fields that enable high-power switching with a lower energy loss. Yet, the leading UWBG semiconductors suffer from intrinsic materials' limitations with regard to their doping asymmetry that impedes their adoption in CMOS technology. Improvements in the ambipolar doping of UWBG materials will enable a wider range of applications in power electronics as well as deep-UV optoelectronics. These advances can be accomplished through theoretical insights on the limitations of current UWBG materials coupled with the computational prediction and experimental demonstration of alternative UWBG semiconductor materials with improved doping and transport properties. As an example, we discuss the case of rutile GeO2 (r-GeO 2 ), a water-insoluble GeO 2 polytype, which is theoretically predicted to combine an ultra-wide gap with ambipolar dopability, high carrier mobilities, and a higher thermal conductivity than β-Ga 2 O 3 . The subsequent realization of single-crystalline r-GeO 2 thin films by molecular beam epitaxy provides the opportunity to realize r-GeO 2 for electronic applications. Future efforts toward the predictive discovery and design of new UWBG semiconductors include advances in first-principles theory and high-performance computing software, as well as the demonstration of controlled doping in high-quality thin films with lower dislocation densities and optimized film properties.
In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.
The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.
Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computational studies have shed light on some of the stable boron allotropes. However, the demand for multifunctionality necessitates the need to go beyond the stable phases into the realm of metastability and explore the potentially vast but elusive metastable phases of boron. Traditional search for stable phases of materials has focused on identifying materials with the lowest enthalpy. Here, we introduce a workflow that uses reinforcement learning coupled with decision trees, such as Monte Carlo tree search, to search for stable and metastable boron phases, with enthalpy as the objective. We discover new boron metastable phases and construct a phase diagram that locates their phase space (T, P) at different levels of metastability (ΔG) from the ground state and provides useful information on the domains of relative stability of the various stable and metastable boron phases.
Advances in accelerator technology are enabling discoveries in particle physics and other fields.