Murugesan, Vijayakumar

DOE OSTI2024

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Chemo-Mechanical Instabilities in Lithium Cobalt Oxide at Higher State-Of-Charge in Li-Ion Batteries

Transition metal oxide cathodes are widely used in commercial Li-ion batteries. However, their practical charge capacity is limited due to severe chemo-mechanical instabilities at higher charge voltage, and state-of-charge condition. Here, in situ stress and strain measurements were synchronized to probe mechanical deformations in the lithium cobalt oxide (LCO) cathode via a multi-beam stress sensor and digital image correlation, respectively. In situ mechanical measurements revealed how Li removal from the electrode structure induces deformations on the LCO composite cathodes during cycling. The structure and morphology of the LCO cathodes were further investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies. Two distinct electrochemical and mechanical behaviors were identified when the LCO was charged up to 4.65 V. The LCO undergoes a compressive stress generation when charged up to 4.2 V and surface fractures on the LCO particles were detected by SEM. LCO cathode experienced significantly large contractions (negative strains) when charged up to 4.65 V, where intergranular crack formation and phase transformation were detected on the LCO particles via SEM and XRD, respectively. Overall, the study bridges complicated structural deformations with in situ analysis of mechanical degradations in LCO cathodes charged at higher voltages. The correlation is vital to understanding instability mechanisms in transition metal oxides at high voltages for alkali metal ion batteries.

higher state-of-charge↗

DOE OSTI2024

Battery detectives: Uncovering cathode impact on anode-free Li cell performance by operando NMR

Here, Anode-free Li-ion batteries could attain new levels of energy density, but challenges remain in controlling deposition and managing degradation. In this issue of Chem, Kwon et al. probe Li metal growth using operando NMR to unravel how cathode materials impact performance and establish criteria for the high-energy/low-cost design tradeoff.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stabilizing Graphite Anode in Electrolytes with Nanoscale Anion Networking for High-Rate Lithium Storage

Graphite is the preferred anode material in commercial lithium-ion batteries (LIBs), but its limited compatibility with various organic molecules restricts the electrolyte solvent options. The primary challenge is solvent co-intercalation with Li ions, leading to graphite layer exfoliation. As a result, electrolyte selection often relies on ethylene carbonate (EC)-based solvents. Here, in this study, we introduce electrolytes featuring a nanoscale anion network ordering that hinders the liquid-phase exfoliation of graphite. This network, formed from concentrated long-chain lithium salts, traps free dioxolane (DOL) molecules, reducing the interactions between graphite particles and solvents during Li intercalation. Our findings reveal a mechanism that stabilizes graphite in otherwise unstable solvents with concentrated salts like LiTFSI, providing key insights for improving LIB performance by addressing electrolyte limitations on graphite anodes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

In situ characterization of metastable Pb3O5 and Pb2O3 phases during thermal decomposition of PbO2 to PbO

Nonstoichiometric lead oxides play a key role in the formation and cycling of the positive electrodes in a lead acid battery. These phases have been linked to the underutilization of the positive active material but also play a key role in the battery’s cycle life, providing inter-particle adhesion and the connection to the underlying lead grid. Similar phases have previously been identified by mass loss or color change during thermal annealing of PbO2 to PbO, suggesting that at least two intermediate PbOx phases exist. Using multiple in situ analysis techniques (powder diffraction, x-ray absorption, x-ray photoelectron spectroscopy) and ex situ nuclear magnetic resonance measurements, the structural conversion and changes in the lead oxidation states were identified during this process. Isolation of the PbOx phases enabled confirmation of Pb3O5 and Pb2O3 by diffraction and the first 207Pb NMR measurement of these intermediates.

Kinnibrugh, Tiffany L.↗

DOE OSTI2024

Electrochemical Imaging of Precisely‐Defined Redox and Reactive Interfaces

Abstract Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

Edgecomb, Joseph↗

DOE OSTI2024

Electrochemical Imaging of Precisely‐Defined Redox and Reactive Interfaces

Abstract Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗