Engineering topics

Nguyen, Julia H.

Publications and source records attributed to Nguyen, Julia H..

5 records

DOE OSTI2025

Generalizable Image Segmentation for Microstructure Characterization Through Integrated SEM and EBSD Analysis

We demonstrate generalizable semantic segmentation using minimal ground truth data. Correlated scanning electron microscopy (SEM) images and electron backscatter diffraction (EBSD) measurements of frictionstir processed 316L stainless steel plates were used to train deep learning models for grain boundary segmentation. Secondary electron (SE) imaging taken at an accelerating voltage of 10 keV correlated to EBSD-derived grain boundaries produced the best performing model. Notably, an ensemble of three models trained on a single SE image produced accurate segmentation over a series of BSE images of samples manufactured under different processing parameters, with a resultant mean absolute error in grain size of 0.34 µm. The striking generalizability of the models likely results from the similar escape depths of the SE training input and the EBSD training output and the reduced probability of dislocation artifacts appearing in the image. This finding highlights the importance of considering the physical principles behind imaging in the development of robust segmentation models for microstructure characterization.

Taufique, Mohammad Fuad Nur↗

DOE OSTI2024

Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

This report summarizes the activities performed under the data analytics Vertex in the Materials Characterization, Prediction and Control Project funded under laboratory directed research and development at Pacific Northwest National Laboratory. The data analytics Vertex developed models for associating global or local process parameters, microstructural features, and performance properties of friction-stir-processed 316L stainless steel plates. Statistical, machine learning, and deep learning models, as well as generative artificial intelligence approaches, were used to develop the associations between the process-structure-property data streams. These associations formed the basis for predicting global properties of parts manufactured under different process envelopes, providing a basis for predicting performance using data driven as well as physics-informed and physics-constrained approaches. Additionally, the associations were used to predict local process parameters and microstructural features of the product, predictive relationships that have the potential to form the basis of a control framework that could eventually modulate a friction-stir process to maintain product quality.

316L stainless steel↗

DOE OSTI2024

Advancing the Prediction of MS/MS Spectra Using Machine Learning

Tandem mass spectrometry (MS/MS) is an important tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. While popular, this strategy is limited by the contents of existing reference libraries. In response to this limitation, various methods are being developed for the in silico generation of spectra to augment existing libraries. Recently, machine learning and deep learning techniques have been applied to predict spectra with greater speed and accuracy. Here, in this work, we investigate the challenges these algorithms face in achieving fast and accurate predictions on a wide range of small molecules. The challenges are often amplified by the use of generic machine learning benchmarking tactics, which lead to misleading accuracy scores. Curating data sets, only predicting spectra for sufficiently high collision energies, and working more closely with experimental mass spectrometrists are recommended strategies to improve overall prediction accuracy in this nuanced field.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Programmable Catalyst Structures via Adsorbate-Induced Adatom Assembly

The electronic structure and geometric configuration of oxide-supported metal ions are important coordination properties that can be related to catalytic activity and stability. Herein, we interrogate the coordination environment of mononuclear Pd ions supported on ceria using CO adsorption, infrared vibrational spectroscopy, and DFT modeling. We observed the 15 h continuous co-evolution of a palladium- (2167 cm -1 ) and cerium-carbonyl (2177 cm -1 ) complex by monitoring the $\nu$(CO) infrared region. The slow CO adsorption kinetics were caused by the reactive ligand exchange between an oxygen atom of the support and the CO adsorbate to yield an oxygen vacancy and adsorbed CO 2 . We hypothesize that the co-evolved cerium-carbonyl complex was formed upon CO adsorption at or adjacent to this oxygen vacancy. Our hypothesis was experimentally supported by a dramatic attenuation of the cerium carbonyl signal upon pre-adsorption of water through an apparent competitive adsorption mechanism. The attenuation was also accompanied by a 6 cm -1 redshift of the palladium carbonyl band (2161 cm -1 ) attributed to hydrogen bonding between the carbonyl and a nearby hydroxyl. Characteristic n(CO) stretch frequencies catalogued through CO adsorption onto single crystal ceria by Wöll et al.1 led us to index the cerium carbonyl to the {100} nanofacet of the polycrystalline ceria support. It follows from the observed co-evolution of the two carbonyl complexes that Pd was also adsorbed at the {100} nanofacet. Redeployment of a previously developed DFT model by Ivanova-Shor et al.2 featuring square-planar coordination of Pd2+ at the {100} nanofacet (O 4 Pd) of a Ce 21 O 42 nanoparticle model qualitatively reproduced several experimental observations.

36 MATERIALS SCIENCE↗

DOE OSTI2024

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗