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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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At least 55 records · Page 3

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗

Publishing Challenges in Energetic Materials Science

The editorial addressed the ethical dilemma that energetic materials scientists face in advancing their field for societal good and not providing information that could be used for nefarious purposes. The reaction to the editorial was heated, both pro and con. Here we concluded that PEP had done a good job in catalyzing a thoughtful debate in our community about what information should or should not be published.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dynamic Mesoscale Materials Science at the Advanced Photon Source [Slides]

We are modernizing our 1980s nuclear deterrent, and moving beyond life extensions (W76, B61, W88) to systems that have more newly (differently) manufactured components (W80, W87). For the first time since the 1980s, we are doing truly new designs (W93) and there will likely be more to respond to emerging deterrence gaps.

36 MATERIALS SCIENCE↗

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical experiments. Frugal twins range from simple toy examples to low-cost surrogates of high-cost research systems. For example, a color-mixing self-driving laboratory (SDL) can serve as a low-cost version of a costly multi-step chemical discovery SDL. Frugal twins already provide hands-on experience for SDLs with low costs and low risks. They can also offer as test beds for software prototyping (e.g., optimization, data infrastructure), and a low barrier to entry for democratizing SDLs. However, there is room for improvement. The true value of frugal twins can be realized in three core areas. Firstly, hardware and software modularity; secondly, purpose-built design (human-inspired vs. hardware-centric vs. human-in-the-loop); and thirdly state-of-the-art (SOTA) software (e.g., multi-fidelity optimization). We also describe the ethical benefits and risks that come with the democratization of science through frugal twins. For future work, we suggest ideas for new frugal twins, SDL educational course outcomes, and a classification scheme for autonomy levels.

36 MATERIALS SCIENCE↗

Discrete event cellular automata: A new approach to cellular automata for computational material science

Here, we explore the computational advantages of discrete event simulation for cellular automata models of grain growth. These benefits include a reduction in execution time by up to an order of magnitude and the elimination of numerical errors that stem from overshooting grain capture events and approximating a Poisson process with a Bernoulli process. The fundamental mechanisms speeding up the discrete event simulation are uncovered, and with these we create a speedup model that explains our experimental outcomes.

36 MATERIALS SCIENCE↗