Engineering Papers⌕ Search

Engineering topics

Smith, Justin

Publications and source records attributed to Smith, Justin.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗
DOE OSTI2021

hippynn Python Package

hippynn is a python package for defining, training, and applying neural networks to atomistic systems. In particular, it focuses on Hierarchical Interacting Particle Neural Networks (HIP-NNs), a deep learning architecture for atomistic systems. HIP-NNs take input data describing the properties of atomistic systems (often generated using ab-initio quantum mechanics) to learn fast and accurate models. A trained HIP-NN can predict potential energy surfaces, atomic charges, and more. hippynn uses PyTorch for portable high-performance code, including both CPU and GPU support. hippynn allows for extensive customization, including user-defined models and loss functions, to facilitate future research into extensions of HIP-NN as well as other atomistic deep learning models.

Lubbers, Nicolas↗
NASA NTRS2020

IV&V Assurance Case Design for Artemis II

As human-rated missions like those in NASA's Artemis program continue to grow in both size and complexity, and the role of software in achieving mission objectives expands dramatically, NASA's Independent Verification and Validation (IV&V) Teams face evolving challenges in assuring the safety and performance of the safety- and mission-critical embedded software that is essential to landing astronauts on the surface of the Moon by 2024. Key among these challenges is IV&V's desire to present a cohesive, integrated assurance statement to its stakeholders that encapsulates and summarizes our assurance positions across the integrated Artemis systems and their combined role in support of a safe and successful flight. In order to meet this challenge, the IV&V Teams have begun a transition to using formal assurance case concepts and documentation in the Goal Structuring Notation (GSN) to build an argument in support of software assurance. IV&V recognizes significant benefits to the logical argumentation structure provided by assurance cases and GSN over our current practices for documenting and managing assurance claims. In order to reap these benefits, IV&V is integrating the use of assurance case concepts with our paradigm of follow-the-risk capability based assurance. Because of this, assurance cases created and used by IV&V are distinct from the sort of assurance case created by a development project or embedded software assurance organization. IV&V's assurance cases depend much less upon standards and regulations, and more on evidence captured by IV&V regarding the environment, requirements, design, and implementation. IV&V constructs an independent network of claims based on an independent decomposition of arguments. Based upon the risk posture of these claims and their associated software and software artifacts, IV&V then develops and executes engineering analyses and testing, which provide evidence to either support or refute the claim. This emerging risk-informed assurance case methodology is being put into practice as IV&V plans for support of the Artemis II mission, the first flight of the Orion capsule and Space Launch System with astronauts on board.

Whitman, Gerek↗
NASA NTRS2019

Agile Approach to Assuring Software for NASA's Orion Spacecraft

Agile software development is prevalent throughout the Government, and NASA is no exception. NASA's Orion spacecraft is being developed to return Astronauts to the moon in the next 5 years, and the role of software in achieving the ambitious mission objectives has expanded dramatically in the last few decades. This presentation is the story of how the Independent Verification and Validation team for Orion adapted to the Agile approach that the Orion Program was using to develop the flight software. Consider attending this session if you are working with software developers utilizing Agile development approaches, or are interested in learning about Agile and Lean principles that could help improve communication within your own team.

Smith, Justin↗
NASA NTRS2018

Agile Approach to Adding Assurance and Mitigating Overall Mission Risk for Orion Software on EM-1

Human-rated missions like Orion are becoming exceedingly complex in terms of software contribution to achieving mission objectives, and this creates a resource challenge for everyone whose job is to add assurance that the mission is going to fly safely. Orion IV&V has addressed this challenge by providing focused assurance results of critical mission capabilities prioritized by a dynamic assessment of risk level. Prior to this approach, Orion IV&V was evaluating areas of risk in much broader, and more static, terms. Due to the Agile software development cycle that Orion follows, IV&V findings were often reported months out of phase with the developer. As a result of evolving the approach to providing assurance on Orion, IV&V is able to incrementally deliver high-priority assurance data and more impactful issues more in phase with the developer activities, thereby increasing the value of the findings to the project. The agile IV&V approach employed by the Orion IV&V team strives to achieve a cadence of delivery that matches the pace of development. This agile approach provides increased flexibility for the assurance provider to become more efficient in reporting assurance conclusions and issues. This presentation will discuss the principles which drive the design of our approach, results to date, and aspirations for long-term performance.

Assurance↗