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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Co-leveraging Scientific Advances in Space Biology and Astrobiology Towards Achieving NASA’s Life Science Objectives

Executive Summary: Distinct lines of scientific inquiry drives the separation of NASA’s fundamental life science research into Space Biology and Astrobiology. This division developed as a way to place life scientists alongside experts in the physical constraints that define the acclimation, adaptation and evolution of biology systems relevant to their respective subjects. For astrobiology, integration with disciplines such as geology, geochemistry, astronomy, planetary science, etc., enables a comprehensive assessment of the physical environment and its co-evolution with biological processes. Space Biology’s co-location with Physical Sciences places life science researchers adjacent to experts in the physical phenomena associated with microgravity and spaceflight, enabling an understanding of how the spaceflight environment affects biological systems. Despite this separation, aspects of both disciplines have converged on a similar, fundamental objective: to describe and understand the dynamics of complex living communities in the contexts of their physical environments. While the environmental systems and timescales are dramatically different, continuing to motivate the separation into distinct fields, similarities in the underlying objective present opportunities to find efficiencies, reduce overlap, and minimize duplication of effort. Space Biology and Astrobiology share a common need to understand microbial physiology in extreme environments – whether the ‘built’ spaceflight environment or the natural environments in which many astrobiology studies are conducted. In particular, open questions in each discipline require the development of quantitative frameworks, applicable at the ecosystem level, that support predictive capabilities for environments where observations are sparse. Additionally, both disciplines have a need to prepare, detect, and analyze the (potential) biological signal in complex samples-often in a completely autonomous fashion. The next decade will see NASA Space Biology moving to understand and describe the effects of the beyond low-earth orbit (BLEO) spaceflight environment on living systems. This new direction will dramatically reduce the opportunities for ground-based analysis of space-flown samples, driving space biology investigations towards fully autonomous experiments and missions. At the same time, astrobiology life detection missions aimed at detecting biosignatures on Mars and icy moons in the outer solar system could benefit from fully automated sample processing and analysis. There are opportunities to leverage instrument and method development between both disciplines within the context of these BLEO missions.

Astrobiology↗
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 OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2022

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Operational Uncrewed Aircraft Systems Development for Meteorology and Atmospheric Physics Field Campaign Report

The overarching goal of the project is to develop integrated small, unmanned aircraft systems (SUAS) capabilities for enhanced atmospheric physics measurements. This team included atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use SUAS in atmospheric physics. The flight campaigns at the Southern Great Plains (SGP) U.S. Department of Energy Atmospheric Radiation Measurement (ARM) observatory were an integrated evaluation of the systems in coordinated field tests. This enabled sensor testing, and validation of SUAS technology and integration of unmanned aircraft into the airspace. The set of experiments enabled evaluation and progression of remote UAS deployment operations with observing atmospheric sensors. Each flight test endeavor was notable for the significant lessons learned during every deployment event. Team size and roles, equipment use, aircraft instruments, meteorological instruments, and deployment logistics, were all evaluated and improved at every flight-testing day. The details of these are further discussed in the Results section.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Shear-Induced Gelation of Self-Yielding Active Networks

An enticing feature of active materials is the possibility of controlling macroscale rheological properties through the activity of the microscopic constituents. Using a unique combination of microscopy and rheology we study three dimensional microtubule-based active materials whose autonomous flows are powered by a continually rearranging connected network. We quantify the relationship between the microscopic dynamics and the bulk mechanical properties of these nonequilibrium networks. Experiments reveal a surprising nonmonotonic viscosity that strongly depends on the relative magnitude of the rate of internally generated activity and the externally applied shear. Here, a simple two-state mechanical model that accounts for both the solidlike and yielded fluidlike elements of the network accurately describes the rheological measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. Further, by combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Autonomous Navigation Above the GNSS Constellations and Beyond: GPS Navigation for the Magnetospheric Multiscale Mission and SEXTANT Pulsar Navigation Demonstration

This talk will describe two first-of-their-kind technology demonstrations attached to ongoing NASA science missions, both of which aim to extend the range of autonomous spacecraft navigation far from the Earth. First, we will describe the onboard GPS navigation system for the Magnetospheric Multiscale (MMS) mission which is currently operating in elliptic orbits reaching nearly halfway to the Moon. The MMS navigation system is a key outgrowth of a larger effort at NASA Goddard Space Flight Center to advance high-altitude Global Navigation Satellite System (GNSS) navigation on multiple fronts, including developing Global Positioning System receivers and onboard navigation software, running simulation studies, and leading efforts to characterize and protect signals at high-altitude in the so-called GNSS Space-Service Volume (SSV). In the second part of the talk, we will describe the Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) mission that aims to make the first in-space demonstration of X-ray pulsar navigation (XNAV). SEXTANT is attached to the NASA astrophysics mission Neutron-star Interior Composition ExploreR (NICER) whose International Space Station mounted X-ray telescope is investigating the fundamental physics of extremes in gravity, material density, and electromagnetic fields found in neutron stars, and whose instrument provides a nearly ideal navigation sensor for XNAV.

Winternitz, Luke↗
DOE OSTI2020

Tacky Elastomers to Enable Tear-Resistant and Autonomous Self-Healing Semiconductor Composites

Mechanical failure of π-conjugated polymer thin films is unavoidable under cyclic loading conditions, due to intrinsic defects and poor resistance to crack propagation. Here, the first tear-resistant and room-temperature self-healable semiconducting composite is presented, consisting of conjugated polymers and butyl rubber elastomers. This new composite displays both a record-low elastic modulus (<1 MPa) and ultrahigh deformability with fracture strain above 800%. More importantly, failure behavior is not sensitive to precut notches under deformation. Autonomous self-healing at room temperature, both mechanical and electronic, is demonstrated through the physical contact of two separate films. The composite film also shows device stability in the ambient environment over 5 months due to much-improved barrier property to both oxygen and water. Butyl rubber is broadly applicable to various p-type and n-type semiconducting polymers for fabricating self-healable electronics to provide new resilient electronics that mimic the tear resistance and healable property of human skin.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Preliminary Planning for NEAR's Low-Altitude Operations at 433 Eros

On February 14, 2000, an orbit insertion burn will place NASA's Near Earth Asteroid Rendezvous (NEAR) spacecraft (S/C) into orbit around asteroid 433 Eros. NEAR will initially orbit Eros with distances ranging from 500 to 100 km in order to characterize the shape, gravity and spin of Eros. Once the physical parameters of Eros are determined reasonably well, the plan is to establish an orbit of the NEAR S/C with increasingly lower altitudes as the one year orbital mission progresses while further characterizing the gravity and shape of Eros. Towards the end of the NEAR mission, after the shape, gravity and spin of Eros have been well characterized, the scientific interest of obtaining very close observations (< 5 km) can be realized. The navigation during this phase relies on a combination of NASA's Deep Space Network (DSN) radio metric tracking, laser ranging (LIDAR) data from the S/C to the surface of Eros, and onboard optical imaging of landmarks on Eros. This paper will provide preliminary plans for mission design and navigation during the last two months of the orbit phase, where several close passes to the surface will be incorporated to enhance the science return. The culmination of these close passes will result in the eventual landing of the S/C on the surface of Eros. Several considerations for these plans are given by Antreasian, et at. [1998]. The objective for the end of the mission will be to land the S/C autonomously using the surface relative information obtained from the onboard LIDAR instrument. The goal will be to soft land the S/C in such a way as to keep it operational. With the use of an onboard LIDAR landing algorithm as discussed by Antreasian et at. [1998], it is believed that the S/C impact velocity can be kept well under 7 m/s which is a requirement for allowing the S/C to remain operational.

Antreasian, P. G.↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗
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↗