Items where Author is "Le Houx, James"

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. informed machine learning AI for science all-solid-state battery astrobiology autonomous experimentation battery degradation bone chemo-mechanics convolutional neural network correlative X-ray tomography and diffraction Dark-Field X-ray Microscopy (DFXM) data-driven image mechanics deep learning defect nucleation digital volume correlation dimensionality reduction Enceladus fluid inclusions fracture mechanics image subtraction method In-situ freezing large-scale user facilities Li metal micro-CT Natron operando characterisation operando XCT Operational Design Domains (ODD) physics quantum kernel quantum machine learning scientific AI sim-to-real transfer single particle analysis strain strain evolution support vector machine Swiss Nanoscience Institute Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number: Sinergia CRSII5_202296 Rutherford Appleton Laboratory Faraday Institution alloy interlayer taxonomy trabecular bone UMAP X-ray computed tomography XCT zero-excess lithium anode

Number of items: 45.

. informed machine learning

Lu, Emily, Perez, Gabriel, Baker, Peter, Irving, Daniel, Kumar, Santosh, Celorrio, Veronica, Britto, Sylvia, Headen, Thomas F., Gomez-Gonzalez, Miguel, Wright, Connor, Green, Calum, Young, Robert Scott, Kirichek, Oleg, Mortazavi, Ali, Day, Sarah, Antony, Isabel, Wright, Zoe, Wood, Thomas, Snow, Tim, Thiyagalingam, Jeyan, Quinn, Paul, Jones, Martin Owen, David, William and Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 (2026) Autonomous battery research: principles of heuristic operando experimentation. [Working Paper] (doi:10.48550/arxiv.2601.00851)

AI for science

Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 (2026) Benchmarking autonomy in scientific experiments: a hierarchical taxonomy for autonomous large-scale facilities. [Working Paper] (doi:10.48550/arXiv.2601.06978)

all-solid-state battery

Xu, Linfeng, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Kachkanov, Vyacheslav, Zhang, Jinsong, Norbert Wullich, Robin, Fankhauser, Matthias, Löffel, Kaspar, Schmidt, Thomas J. and El Kazzi, Mario (2026) Operando X‐Ray computed tomography reveals the role of interfacial nucleation nanolayers in suppressing mechanical failure in zero‐excess lithium all‐solid‐state batteries. Small, 22 (10):e12284. ISSN 1613-6810 (Print), 1613-6829 (Online) (doi:10.1002/smll.202512284)

astrobiology

Perera, Liam, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Leonardi, Alberto, Day, Sarah and Thompson, Stephen (2026) Fluid inclusions in natron: a window into the interior of Enceladus. [Working Paper] (doi:10.21203/rs.3.rs-8406896/v1)

autonomous experimentation

battery degradation

bone

Valijonov, Jon, Soar, Peter ORCID: https://orcid.org/0000-0003-1745-9443, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 and Tozzi, Gianluca (2026) Evaluation of direct strain field prediction in bone with data-driven image mechanics (D2IM-Strain). [Working Paper] (doi:10.64898/2026.03.31.715417)

chemo-mechanics

Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Mistry, Jamini Jessica, Li, Yaozhu, Lesage, Louis, Staeck, Steffen, Bird, Robert and Spencer-Jolly, Dominic (2026) The topographic origin of chemomechanical failure in high-nickel cathodes revealed by operando darkfield X-ray microscopy. [Working Paper] (doi:10.26434/chemrxiv.15002900/v1)

convolutional neural network

correlative X-ray tomography and diffraction

Dark-Field X-ray Microscopy (DFXM)

data-driven image mechanics

deep learning

defect nucleation

digital volume correlation

dimensionality reduction

Florez, Isabella, Farhat, Ahmed, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673, Altamura, Edoardo and Tozzi, Gianluca (2026) Quantum kernel support vector machines for trabecular bone classification: comparing feature reduction strategies on synthetic micro-CT data. [Working Paper] (doi:10.64898/2026.05.04.722627)

Enceladus

fluid inclusions

fracture mechanics

Wright, Conor, Ramsdale, Emily, McKay Fletcher, Daniel, Williams, Katherine, Le Houx, James ORCID: https://orcid.org/0000-0002-1576-0673 and Ruiz, Siul (2026) Understanding root biomechanics in high-strength environments- assessing the feasibility of penetration and fracture FE models with SRXCT. In: EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026. EGU26 (7232). Copernicus GmbH, Göttingen, Germany. (doi:10.5194/egusphere-egu26-7232)

image subtraction method

In-situ freezing

large-scale user facilities

Li metal

micro-CT

Natron

operando characterisation

operando XCT

Operational Design Domains (ODD)

physics

quantum kernel

quantum machine learning

scientific AI

sim-to-real transfer

single particle analysis

strain

strain evolution

support vector machine

Swiss Nanoscience Institute Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number: Sinergia CRSII5_202296 Rutherford Appleton Laboratory Faraday Institution alloy interlayer