Results for “Active Learning” — Page 15

DOE OSTI2021

A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers

Here we develop a fast multi-fidelity modeling method for very complex correlations between high- and low-fidelity data by working in modal space to extract the proper correlation function. We apply this method to infer the amplitude of motion of a flexible marine riser in cross-flow, subject to vortex-induced vibrations (VIV). VIV are driven by an absolute instability in the flow, which imposes a frequency (Strouhal) law that requires a matching with the impedance of the structure; this matching is easily achieved because of the rapid parametric variation of the added mass force. As a result, the wavenumber of the riser spatial response is within narrow bands of uncertainty. Hence, an error in wavenumber prediction can cause significant phase-related errors in the shape of the amplitude of response along the riser, rendering correlation between low- and high-fidelity data very complex. Working in modal space as outlined herein, dense data from low-fidelity data, provided by the semi-empirical computer code VIVA, can correlate in modal space with few high-fidelity data, obtained from experiments or fully-resolved CFD simulations, to correct both phase and amplitude and provide predictions that agree very well overall with the correct shape of the amplitude response. We also quantify the uncertainty in the prediction using Bayesian modeling and exploit this uncertainty to formulate an active learning strategy for the best possible location of the sensors providing the high fidelity measurements.

42 ENGINEERING↗

DOE OSTI2016

SU-A-BRA-01: Introduction

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

SU-A-BRA-02: Making the Most of a One Hour Lecture with Alternative Teaching Methodologies: Implementing Project-Based and Flipped Learning

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

SU-A-BRA-03: Creative Stimulation: A Flexible Hands-On Approach to Building a Deeper Understanding of Critical Concepts in Radiation Physics

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

SU-A-BRA-05: Panel Discussion

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2016

SU-A-BRA-00: Education Council Symposium: Revitalizing Your Medical Physics Classroom: Some Examples and Thoughts from the Trenches

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Abstract Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water‐gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine‐learning interatomic potentials (MLIPs) integrated with an active‐learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high‐resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

Lee, Yonghyuk [Chemistry and Biochemistry Universi↗

DOE OSTI2025

An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li7P3S11

Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding interphase formation in lithium metal anodes. This work presents a computational framework based on high-throughput first-principles density functional theory and machine-learning interatomic potentials (MLIPs) including automated iterative, active learning to enable robust computational exploration of interphase formation between lithium metal anodes and an inorganic solid-state electrolyte. As a demonstration, we apply the framework to a Li/Li7P3S11 interface and find that it accurately identifies the experimentally observed, thermodynamically stable interphase products as well as their overall spatial arrangement within a heterogeneous, amorphous layered structure, with Li2S domains of nanocrystallinity. Our simulations show two stages, a fast and slow diffusion reaction regime, that corroborate the relative phase formation rate of Li x P, Li2S, and Li3P. Using the Onsager transport theory, we capture time-dependent ionic diffusion within the reacting interface, including cross-correlation effects. We found that cross-correlation effects between Li-P and P-S ionic motion significantly influence P-ion diffusion, making it highly sensitive to the local environment and potentially leading to "kinetic trapping" of Li-P phases. The passivation of the interface is shown as the ionic fluxes all approach zero, effectively halting interphase growth.

Diffusion↗

DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗

DOE OSTI2017

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Active Learning↗

DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantum annealing-aided design of an ultrathin-metamaterial optical diode

Abstract Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal–dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices

Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x–y-Gly sequence is the defining motif of CMPs and is critical to their triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues—N-substituted glycine derivatives—present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone flexibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising peptoid substitutions predicted to stabilize the desired quaternary structure through side chain interactions and produce stable peptoid-based collagen-like triple helices. To experimentally test the computational predictions, a top candidate identified by the screen was synthesized and imaged using scanning electron microscopy to resolve fibril-like bundles consistent with collagen-like triple helices. This work predicts a number of CMP peptoid substitutions capable of forming stable triple-helical structures, presents a generalizable design strategy for engineering desired peptoid structures, and opens new avenues for the design of peptoid-based biomimetic materials.

active learning↗

DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Active deep kernel learning of molecular properties from structural embeddings

As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular properties. This paper presents an active learning approach for molecular discovery using deep kernel learning (DKL), demonstrated on the QM9 dataset. DKL links structural embeddings directly to properties, creating organized latent spaces that prioritize relevant property information. By iteratively recalculating embedding vectors in alignment with target properties, DKL uncovers concentrated maxima representing key molecular properties and reveals unexplored regions with potential for innovation. This approach underscores DKL’s potential in advancing molecular research and discovery.

Artificial neural networks↗

DOE OSTI2024

WIP: Collaborating to Introduce Second-Life Battery Technology Research in an Informal STEM Learning Environment

This work in progress, innovative practice, paper outlines an interdisciplinary collaborative model utilized by a team of educational researchers, and engineers to design a series of community-centered, place-based learning activities suitable for a middle or high school informal STEM learning environment. Based on the potential applications and benefits of Second-Life Electric Vehicle Batteries, the activities connect cutting-edge STEM research with multidimensional real-world challenges within their communities related to climate change, renewable energy integration and storage, intelligent transportation systems, and optimized energy management, that empowers students to think critically about the role they can play in shaping a more resilient, environmentally sustainable community.

Whiteside, Hope↗