Mathieu Doucet

Powder diffraction is a primary structural characterization tool in materials science, yet automated phase identification remains a major bottleneck for autonomous discovery. Existing workflows rely heavily on search–match heuristics and manual Rietveld refinement, and broadly usable end-to-end automation is especially limited for neutron powder diffraction, where comparable tools are largely absent. Here, we introduce Residual-Aware Deep-learning–Assisted Refinement for Powder Diffraction (RADAR-PD), a modality-aware machine learning framework for phase identification and quantification across both x-ray and neutron powder diffraction. RADAR-PD couples a mismatch-tolerant neural network operating on coarse momentum-transfer fingerprints with automated lattice nudging and physics-constrained Rietveld verification, enabling dominant-phase hypotheses to be generated from elemental constraints and secondary phases to be isolated recursively. On an experimental RRUFF powder x-ray diffraction benchmark, RADAR-PD outperforms data-driven automated Rietveld analysis (DARA) in recovering the reference phase. RADAR-PD further provides robust multiphase analysis on complex time-of-flight and constant-wavelength neutron datasets, addressing an important unmet need in automated neutron diffraction analysis. These results establish RADAR-PD as an auditable, instrument-agnostic framework for autonomous structural discovery.

Li-metal anodes are a key enabling technology for next-generation high-energy batteries, including Li–S, Li-air, and high-voltage cathodes. While most research enabling Li metal focuses on electrolyte design, especially in the solid state, the nature of the Li metal itself has a significant impact on the performance of both solid- and liquid-based batteries. Recent work has highlighted that both the microstructure and the surfaces of Li metal can vary dramatically from source to source. This presentation will detail how the Li surfaces, impurities and microstructure all play a role in the performance of Li metal with both liquid and solid electrolytes. Equally important to Li metal batteries is the nature of the interface between the electrolyte and Li metal. In solid state batteries these interfaces are especially difficult to characterize due to their buried nature. The second half of this presentation will focus on the use of neutron reflectometry and neutron depth profiling to characterize buried solid-state electrolyte-Li metal interfaces. The focus will be on using the solid electrolyte LiPON as an example, but also extend to polymers and other oxide ceramics. Acknowledgement: This work was sponsored by the US Department of Energy (DOE) Vehicle Technology Office (VTO)battery materials research program and the US-German Collaboration on Energy Storage: Lithium-Solid-Electrolyte Interfaces.

Electrified interfaces are critical to the performance of energy systems and often demonstrate substantial complexity under operating conditions. A nanoscale understanding of the interfacial microenvironment, i.e., the solid-electrolyte interphase (SEI), in lithium-mediated nitrogen reduction (Li-N₂R) is key for realizing efficient ammonia (NH₃) production. Herein, we used time-resolved neutron reflectometry (NR) to observe SEI formation under Li-N₂R conditions. We found that the LiBF₄-based electrolyte provided a substantially more well-defined SEI layer than previous SEI NR interrogations that used LiClO₄, highlighting the underlying chemistry that dictates electrolyte design and enabling new NR-based studies. Using in situ NR, we found that the LiBF₄-derived SEI under Li-N₂R conditions comprises a thick, diffuse outer layer and a thin, compact inner layer at low current cycling (2), revealing a structure which ex situ studies have not been able to probe. Increased current cycling and sustained current cycling led to the merging of the layers into a single-layer SEI. We used isotope contrast methods with d₆-EtOH and d₈-THF to drive time-resolved tracking of SEI growth at low current cycling, revealing that the proton donor modifies the inner layer, and the solvent modifies the outer layer. Li dendritic growth was observed in the absence of a proton donor. Neutron absorption also indicated the presence of boron in the SEI, underscoring the value of neutron-based interrogation. Our results inform Li-based systems and reaction microenvironments, and these methods can be applied broadly to interfacial energy technologies.

Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and physics-based models provides information about spatially resolved neutron scattering length density (NSLD) profiles. Recent improvements in data acquisition systems have allowed acquiring thousands of NR curves in a couple of hours, which has led to a need for automated data analysis tools to interpret NR measurements in real-time. Here, we present a machine learning analysis workflow that uses a series of models, based on a convolutional neural network (CNN), to learn the relation between the NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly from NRs. The usefulness of our CNN-based models is demonstrated by constructing NSLDs from NRs of several films containing homopolymer polyzwitterions and diblock copolymers mixed with different types of salts. Comparisons of the NSLDs with those constructed using ad hoc multi-layer models reveal a very good agreement, suggesting the potential of CNN-based models for real-time automated data analysis of NRs.

Electrified interfaces are critical to the performance of energy systems and often demonstrate substantial complexity under operating conditions. Nanoscale understanding of the interfacial microenvironment, i.e., the solid electrolyte interphase (SEI), in lithium-mediated nitrogen reduction (Li-N2R) is key for realizing efficient ammonia (NH3) production. Herein, we have used time-resolved neutron reflectometry (NR) to observe SEI formation under Li-N2R conditions. We found that LiBF4-based electrolyte provided a substantially more well-defined SEI layer than previous SEI NR interrogation that used LiClO4, highlighting the underlying chemistry that dictates electrolyte design and enabling new NR-based studies. Using in situ neutron reflectometry, we found the LiBF4-derived SEI under Li-N2R conditions comprises a thick, diffuse outer layer and a thin, compact inner layer at low current cycling (<2 mA/cm2), revealing a structure which ex situ studies have not been able to probe. Increased current cycling and sustained current cycling led to the merge of the layers to a single-layer SEI. We used isotope contrast methods with d6-EtOH and d8-THF to drive time-resolved tracking of SEI growth at low current cycling, revealing that the proton donor modifies the inner layer, and the solvent modifies the outer layer. Li dendritic growth was observed in the absence of a proton donor. Neutron absorption also indicated the presence of boron in the SEI, underscoring the value of neutron-based interrogation. Our results inform Li-based systems and reaction microenvironments, and these methods can be applied broadly to interfacial energy technologies.

Neutrons have many extraordinary properties, including no overall charge, highly sensitive to light elements like hydrogen and carbon, magnetic moment, and power of polarization, etc. All these characteristics make them excellent probes of materials, providing details about the structure and motion of atoms that cannot be easily obtained with other research techniques. Neutron reflectometry (NR), a neutron scattering technique, is a powerful tool to examine surface and interfacial structures of thin films in a non-destructive and non-invasive fashion. The Liquids Reflectometer (LR) at the Spallation Neutron Source at Oak Ridge National Laboratory measures specular neutron reflectivity in a horizontal sample geometry, and tracks changes of layer thickness, scattering length density, and roughness as a function of depth. LR provides valuable information over a wide variety of scientific and technological applications, such as, interfacial reactions in energy storage materials, phase separation in polymer films, surfactants at interfaces, biological membranes in intermolecular interaction, protein adsorption, and so on. The exceptional sample environment at LR offers the capability to conduct in situ electrochemistry to probe the morphological and structural changes of solid/liquid interfaces during operation as a function of chemical and electrochemical gradients over time. Furthermore, event mode data collection at LR can be carried out continuously over a reduced Q range and binned into 60-second intervals. The improved time resolution of the NR measurement can help elucidate the kinetic behavior of surface/interfaces under dynamic conditions and bring a unique perspective to equilibrium studies. A specific example of applications in electrochemically modulated separation for water remediation by using in situ/operando neutron reflectometry will be presented. The example study sheds light on fundamental understanding of selectivity mechanisms at electroactive interfaces, with direct benefits to the water separations research field. In addition to neutron techniques, some complementary capabilities for surface chemistry and electrochemistry available at both user facilities (Center for Nanophase Materials Sciences and Neutron Scattering Division) will also be outlined. Together, they will play a key role in creating new, and more efficient systems through rationally design of the structure and operating conditions.

Neutron-Transformer Reflectometry Advanced Computation Engine (N-TRACE), a neural network model using a transformer architecture, is introduced for neutron reflectometry data analysis. It offers fast, accurate initial parameter estimations and efficient refinements, improving efficiency and precision for real-time data analysis of lithium-mediated nitrogen reduction for electrochemical ammonia synthesis, with relevance to other chemical transformations and batteries. Despite limitations in generalizing across systems, it shows promises for the use of transformers as the basis for models that could accelerate traditional approaches to modeling reflectometry data.

Using neutron scattering, we probed lithium-ion battery cathode nanoscale structural features that arise from calendering and solvent addition. We also correlated quantitative component-specific interfacial surface area to long-term performance.