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Researcher profile

Steven R. Spurgeon

Steven R. Spurgeon contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciMachine LearningArtificial Intelligencecond-mat.mes-hallphysics.app-ph

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Published work

7 published item(s)

preprint2026arXiv

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This unique capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition using in-situ environmental TEM data, informing models of morphological evolution and catalytic properties. The model performance and achieved accuracy of predictions are desirable based on, for scientific data characteristic, based on limited size of training data sets. The model convergence and values for the loss function mean square error show dependence on the training strategy, and structural similarity measure between predicted structure images and ground truth reaches the value of about 0.7. This computed structural similarity is smaller than values obtained when the deep learning architecture is trained using much larger benchmark data sets, it is sufficient to show the structural transition of Au nanoparticles. While performance parameters of our model applied to scientific data fall short of those achieved for the non-scientific big data sets, we demonstrate model ability to predict the evolution, even including the particle structural phase transformation, of Au nano particles as catalyst for CO oxidation under the chemical reaction conditions. Using this approach, it may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Free-Standing Epitaxial SrTiO$_3$ Nanomembranes via Remote Epitaxy using Hybrid Molecular Beam Epitaxy

The epitaxial growth of functional materials using a substrate with a graphene layer is a highly desirable method for improving structural quality and obtaining free-standing epitaxial nano-membranes for scientific study, applications, and economical reuse of substrates. However, the aggressive oxidizing conditions typically employed to grow epitaxial perovskite oxides can damage graphene. Here, we demonstrate a technique based on hybrid molecular beam epitaxy that does not require an independent oxygen source to achieve epitaxial growth of complex oxides without damaging the underlying graphene. The technique produces films with self-regulating cation stoichiometry control and epitaxial orientation to the oxide substrate. Furthermore, the films can be exfoliated and transferred to foreign substrates while leaving the graphene on the original substrate. These results open the door to future studies of previously unattainable free-standing nano-membranes grown in an adsorption-controlled manner by hybrid molecular beam epitaxy, and has potentially important implications for the commercial application of perovskite oxides in flexible electronics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hybrid Molecular Beam Epitaxy of Ge-based Oxides

Germanium-based oxides such as rutile GeO2 are garnering attention owing to their wide band gaps and the prospects for ambipolar doping for application in high-power devices. Here, we present the use of germanium tetraisopropoxide (GTIP) (an organometallic chemical precursor) as a source of Ge for the demonstration of hybrid molecular beam epitaxy (MBE) for Ge-containing compounds. We use Sn1-xGexO2 and SrSn1-xGexO3 as model systems to demonstrate this new synthesis method. A combination of high-resolution X-ray diffraction, scanning transmission electron microscopy, and X-ray photoelectron spectroscopy confirms the successful growth of epitaxial rutile Sn1-xGexO2 on TiO2(001) substrates up to x = 0.54 and coherent perovskite SrSn1-xGexO3 on GdScO3(110) substrates up to x = 0.16. Characterization and first-principles calculations corroborate that Ge preferentially occupies the Sn site, as opposed to the Sr site. These findings confirm the viability of the GTIP precursor for the growth of germanium-containing oxides by hybrid MBE, and thus open the door to high-quality perovskite germanate films.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We demonstrate how a centralized controller, informed by machine learning combining limited $a$ $priori$ knowledge and task-based discrimination, can drive on-the-fly experimental decision-making. This platform unlocks practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Asymmetric Lattice Disorder Induced at Oxide Interfaces

Control of order-disorder phase transitions is a fundamental materials science challenge, underpinning the development of energy storage technologies such as solid oxide fuel cells and batteries, ultra-high temperature ceramics, and durable nuclear waste forms. At present, the development of promising complex oxides for these applications is hindered by a poor understanding of how interfaces affect lattice disordering processes and defect transport. Here we explore the evolution of local disorder in ion-irradiated La$_2$Ti$_2$O$_7$ / SrTiO$_3$ thin film heterostructures using a combination of high-resolution scanning transmission electron microscopy (STEM), position-averaged convergent beam electron diffraction (PACBED), electron energy loss spectroscopy (STEM-EELS), and \textit{ab initio} theory calculations. We observe highly non-uniform lattice disordering driven by asymmetric oxygen vacancy formation across the interface. Our calculations indicate that this asymmetry results from differences in the polyhedral connectivity and vacancy formation energies of the two interface components, suggesting ways to manipulate lattice disorder in functional oxide heterostructures.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Microscopic model of stacking-fault potential and exciton wave function in GaAs

Two-dimensional stacking fault defects embedded in a bulk crystal can provide a homogeneous trapping potential for carriers and excitons. Here we utilize state-of-the-art structural imaging coupled with density functional and effective-mass theory to build a microscopic model of the stacking-fault exciton. The diamagnetic shift and exciton dipole moment at different magnetic fields are calculated and compared with the experimental photoluminescence of excitons bound to a single stacking fault in GaAs. The model is used to further provide insight into the properties of excitons bound to the double-well potential formed by stacking fault pairs. This microscopic exciton model can be used as an input into models which include exciton-exciton interactions to determine the excitonic phases accessible in this system.

0 citations0 reviewsNo code linkedOpen access