Accelerating American Energy Deployment through Autonomous Design
Informational presentation on the use of AI for autonomous design at INL's Scientific Computing and AI division (C500). Presented to visitors on 05/15/2025.
42 - ENGINEERING↗
SEARCH · Search NASA
Results for “Autonomous Design”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Toward autonomous design and synthesis of novel inorganic materials
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.
36 MATERIALS SCIENCE↗
Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system
As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling. Travel-based multitasking has an indisputable influence on offering a pleasant travel experience to transit users during their rides, given that they can use their travel time to perform desirable activities and gain benefits in various form. For instance, the in-activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of a travel-based multitasking could be under the influence of: (1) the transit user’s lifestyle, and (2) socio-demographics, and (3) the characteristics of the transit trip. Towards this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using latent class modeling approach. Per the results, two classes of transit users could be identified: (1) worthwhileness seekers, productively travelers and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. Finally, the findings provide insights to policymakers for improving public transit systems in the current form, as well as designing an autonomous mobility system as the future form of public transit.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Machine learning in materials science: From explainable predictions to autonomous design
The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.
36 MATERIALS SCIENCE↗
Autonomous design of new chemical reactions using a variational autoencoder
Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Semi-autonomous design concepts for a Mars rover
Studies of rover mobility and surface rendezvous for a Mars Rover/Sample Return (MRSR) mission have been performed. The objective of these efforts has been to identify and address the most challenging issues and develop the most promising design options for the rover mobility and navigation subsystems. A series of trade studies culminated in three candidate design concepts: a large, but otherwise conventional four-wheel drive vehicle; a sophisticated and agile six-legged walking vehicle; and a greatly simplified legged vehicle. This paper summarizes the key design features for these three concepts. For each design, the approaches to vehicle locomotion, sensing and navigation are discussed first. Timelines are developed for the adopted concepts of operation which constrain the range of the remaining parameters. Estimates of rover mass, volume, and power are provided, and performance is projected for vehicle range, terrain traversibility and navigation accuracy.
Spiessbach, Andrew J.↗
The Right Size: Designing Autonomous Teams to go the Distance
Explore the source record for details and available documents.
Meredith Russell↗
Autonomous spacecraft design methodology
A methodology for autonomous spacecraft design blends autonomy requirements with traditional mission requirements and assesses the impact of autonomy upon the total system resources available to support fault-tolerance and automation. A baseline functional design can be examined for autonomy implementation impacts, and the costs, risk, and benefits of various options can be assessed. The result of the process is a baseline design that includes autonomous control functions.
Divita, E. L.↗
Command and telemetry in autonomous spacecraft design
Some major steps are summarized in the evolution of autonomous design features for planetary exploration spacecraft. The control and data architectures for the Viking, Voyager, and Galileo spacecraft are considered. Telemetry and command capabilities are fundamental features of spacecraft design that have been successfully used for autonomous control. Also discussed is the Autonomous Redundancy and Maintenance Management Subsystem (ARMMS) concept. The software approach to autonomous control provides for modifications to the control process or the addition of new operating features during flight operations.
Turner, P. R.↗
Titan/Mars hard lander. Volume 2 - Autonomous capsule system design study
Autonomous capsule system design study for Titan/ Mars hard lander
Source record↗
Double Asteroid Redirection Test (DART) Mission
NASA’s Double Asteroid Redirection Test (DART) mission was humanity’s first attempt to move a celestial body, demonstrating the capability to perform a kinetic impact on a planetary defense–relevant sized asteroid. DART was part of the international collaboration known as the Asteroid Impact & Deflection Assessment (AIDA), involving NASA, the European Space Agency (ESA), the Agenzia Spaziale Italiana (ASI), and scientists around the world. DART was a key step to demonstrating preparedness to respond to planetary defense scenarios, and it provides a crucial data point for likely outcomes. Near-Earth objects (NEOs) greater than 140 m in size are of particular interest to planetary defense because they have the potential to cause significant damage if they were to impact Earth, and also because they are difficult to detect, with less than 50% of the predicted population discovered as of 2022 (National Academies Press, 2022). With an appropriately sized spacecraft and enough warning (typically many years to decades), a kinetic impact can slightly alter the orbit of an asteroid in a way that, over time, prevents the asteroid from colliding with Earth in the future. DART’s target was Dimorphos, the smaller (~150-m-diameter) member of the binary asteroid system (65803) Didymos, which is a near-Earth, potentially hazardous, and well-characterized asteroid system. By simply observing changes to the system after impact and comparing them with a pre-impact reference, it was possible to use ground-based telescopes to observe the deflection in the orbit of Dimorphos after impact. Developed and operated by the Johns Hopkins University Applied Physics Laboratory (APL), the mission entered formulation in 2015 after multiple years of concept development. The project was administered according to NPR 7120.5, with technical oversight and funding through the Planetary Missions Program Office (PMPO) at Marshall Space Flight Center (MSFC) and overall support as a directed mission from NASA’s Planetary Defense Coordination Office (PDCO). The DART spacecraft hosted a singular payload, the Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO), and a deployable CubeSat contributed by ASI named the Light Italian CubeSat for Imaging of Asteroids (LICIACube). On 11 September 2022, DART deployed LICIACube, which subsequently followed the DART spacecraft at a safe distance and observed the immediate aftermath of the DART impact. DART was designed to autonomously detect, navigate to, and impact Dimorphos. This autonomous design was chosen to maximize the probability of impact, since commanding from the ground could result in course corrections arriving too late. On the day of impact, 26 September 2022, the spacecraft’s autonomous systems successfully detected and locked on to Dimorphos, impacting its surface within 2 m of the center of the illuminated figure (Jensenius et al., 2023). No human intervention was required for a successful impact, demonstrating that humanity possesses the technology to perform a kinetic impact. Within 2 weeks of impact, it was clear that the orbit of Dimorphos had been significantly altered. On 11 October 2022, NASA Administrator Bill Nelson announced that the new orbital period of Dimorphos was shortened by approximately 32 ± 2 min, from 11 h and 55 min before impact to 11 h and 23 min after impact. With additional observations over the following months, the accuracy of this measurement improved to a –33.24 min ± 1.4 s orbital period change (Naidu et al., 2023; Scheirich et al., 2023), and Beta (β), the momentum transfer enhancement parameter, was reported to be 3.6 (Cheng et al., 2023). Subsequent studies examined the details of DART’s impact site, modeled the impact event, investigated the ejecta produced, and analyzed the dynamics of the Didymos system. These combined results clearly demonstrate that the project met all Level 1 mission requirements.
asteroid↗
Evolution of the IEEE P7009 Standard: Towards Fail-Safe Design of Autonomous Systems
Autonomous systems are complex, diverse, and likely be used in environments that change unpredictably. We must develop mechanisms to cope with failures, potentially caused by both the natural stresses of the operating environment and the system's inadequate environmental models. Failures also arise from system component wear. In 2016, the IEEE launched the Global Initiative on Ethics of Autonomous and Intelligent Systems in response to the proliferation of increasingly-autonomous systems. A cornerstone of this initiative is the IEEE P7000 series of projects, which set the standards for the future of ethical intelligent and autonomous technologies. This paper describes the ongoing development of the IEEE P7009 standard on “Fail-Safe Design of Autonomous and Semi-Autonomous Systems”. We provide a collective perspective on the ongoing development of IEEE P7009, and outline the objectives, current approaches, issues under discussion, and two exemplar use cases from different sectors: one high-regulation, the other low-regulation.
Farrell, Marie↗
Component-Oriented Behavior Extraction for Autonomic System Design
Rich and multifaceted domain specific specification languages like the Autonomic System Specification Language (ASSL) help to design reliable systems with self-healing capabilities. The GEAR game-based Model Checker has been used successfully to investigate properties of the ESA Exo- Mars Rover in depth. We show here how to enable GEAR s game-based verification techniques for ASSL via systematic model extraction from a behavioral subset of the language, and illustrate it on a description of the Voyager II space mission.
Bakera, Marco↗
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↗
Formal Assurance for Cognitive Architecture Based Autonomous Agent
Autonomous systems are designed and deployed in different modeling paradigms. These environments focus on specific concepts in designing the system. We focus our effort in the use of cognitive architectures to design autonomous agents to collaborate with humans to accomplish tasks in a mission. Our research focuses on introducing formal assurance methods to verify the behavior of agents designed in Soar, by translating the agent to the formal verification environment Uppaal.
Bhattacharyya, Siddhartha↗
Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this paper, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
An innovative design for autonomous backup attitude control of the Gamma Ray Observatory
The Gamma Ray Observatory is a NASA funded three-axis stabilized spacecraft which will carry four scientific instruments to observe gamma ray phenomena. The requirement to protect the scientific mission from system failures led to the attitude control and determination system design described in this paper. The design employs nine control modes with error detection, hardware substitution, and autonomous mode switching. The system architecture evolved to eliminate cross-dependence between the primary on-board computer (OBC) and the backup control processor electronics. Cross strapping of sensors and actuators and separation of the input/output electronics ensure that a reliable set of sensors and actuators will be available for backup mode operation. The OBC software includes failure detection, hardware reconfiguration, and mode switching logic which provide the ability to autonomously transfer, upon anomaly, to a reliable backup mode. Verification of this mode transition design is done in four test programs: at the unit level, by analytical simulation, by a hybrid breadboard electronics-simulation setup, and by a flight hardware-simulation test.
Tai, F.↗
Proof-of-Concept for Sensor Modeling in MOOSE for the Design of Autonomous Nuclear Reactor Control
Autonomous operation is essential for the deployment of microreactors and fission batteries, both in terrestrial and space applications. For this reason, recent studies have investigated autonomous control by using adaptive model predictive control and multi-objective optimization for heat pipe–cooled microreactors under normal and heat pipe failure conditions. However, prototypes of microreactors and fission batteries do not exist yet, and even the design space has not been narrowed down conclusively, making the instrumentation and control system design difficult. For this reason, there is a need for flexible computational capabilities to create a numerical stand-in of potential microreactor and fission battery designs. The latter can be used to design and test control strategies to support autonomous operations. In this poster, we describe the initial implementation of a pluggable sensor system for the easy implementation of realistic sensor models in the multiphysics object-oriented simulation environment (MOOSE) framework. This new capability will enable MOOSE users to create a numerical stand-in of microreactors and fission batteries, ultimately allowing them to easily test new control algorithms, and instrumentation strategies for advanced systems in the design phase.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗