Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T12: Energy Research and Data ScienceInvited Undergrad Friendly
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Sponsoring Units: GERA GDS Chair: Davis Unruh, Argonne National Laboratory Room: Room 235 |
Thursday, March 9, 2023 11:30AM - 12:06PM |
T12.00001: TBD Invited Speaker: Oleg Borodin
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Thursday, March 9, 2023 12:06PM - 12:42PM |
T12.00002: Data-driven Subcomponent Design and Engineering Invited Speaker: Soo Kim Developing new and effective energy storage technologies is a complex challenge that is difficult to tackle through trial-and-error experimentation alone. However, computational simulation, in conjunction with data science, has emerged as a complementary tool to accelerate the development process and enhance understanding. In the first part of this talk, we will present various case studies, such as a multi-faceted high-throughput screening method, a query-based method utilizing material databases, and a descriptor-based materials design. In the second half of the talk, we will discuss the multi-scale design and product integration efforts at Rivian Automotive. Our approach encompasses the smallest scale (e.g., materials), subcomponents (e.g., electrodes), and entire systems (e.g., battery packs, electric vehicles). While there is a strong focus on developing better "predictive" models that require less experimental input, there are still several ongoing challenges in utilizing the data collected from R&D, pilot testing, and the automated factory floor. Data-driven subcomponent engineering represents one of the key methods that can help accelerate the EV transition underway at Rivian. |
Thursday, March 9, 2023 12:42PM - 1:18PM |
T12.00003: It's all about that Bayes: data-driven insights into energy devices without the black box Invited Speaker: Rachel Kurchin Large black-box models such as neural networks are exciting and powerful tools, but they're not the only way to learn from data! In this talk, I will showcase prior and ongoing work in using Bayesian parameter estimation to extract unprecedented materials-level insights from simple, automated electrical characterization of photovoltaic devices. By running a physical model many times to span the space of properties we wish to fit, and comparing its results to the physical measurements, we obtain a posterior distribution over the properties of interest. This approach has numerous advantages. First, with the democratization of computational power, the tradeoff between the extensive researcher time and labor to directly measure these properties and the computational effort to run a large number of simulations is increasingly favorable. Second, not only are the inferred values of comparable accuracy (and sometimes superior precision!) to direct physical probes, we can also be confident that they represent the most performance-relevant information about those properties, because we've measured them in the device context, rather than in a specially prepared sample that may have unrepresentative characteristics and measurement conditions. I will demonstrate the ability not only to extract these parameters (properties of both bulk materials and interfaces), but to easily observe relationships between them. Finally, I will also discuss ongoing work in utilizing this approach to directly inform/guide device engineering. |
Thursday, March 9, 2023 1:18PM - 1:54PM |
T12.00004: The Materials Experiment Knowledge Graph Invited Speaker: John M Gregoire Materials knowledge is inherently hierarchical. High-level data descriptors can be provided by the chemical elements, crystal structure motifs, and types of materials properties, although a given piece of data must ultimately be considered in the context of its acquisition. Detailed descriptors of a piece of experimental data include not only the metadata for the experiment that generated it, but also the prior history of synthesis and metrology experiments. Graph databases offer an opportunity to represent such hierarchical relationships among data, organizing semantic relationships into a knowledge graph. Initial reports of knowledge graphs in materials science highlight the breadth of approaches for their development. Herein, we establish a knowledge graph of materials experiments whose construction encodes the complete provenance of each material sample and its associated experimental data and metadata. Additional relationships among materials and experiments further encode knowledge and facilitate data exploration. The Materials Experiment Knowledge Graph is sufficiently large and complex to demonstrate a path toward a global materials knowledge graph. We characterize the scalability of this approach, especially with respect to executing queries, illustrating the value that modern graph databases can provide to the enterprise of data-driven materials science. |
Thursday, March 9, 2023 1:54PM - 2:30PM |
T12.00005: Accelerating energy materials discovery in practice Invited Speaker: Linda Hung Data science, AI, and autonomy underlie new capabilities for the design and discovery of new materials. However, in many cases it is not clear if these capabilities accelerate the development of new materials in an industry setting. In this talk, I review difficulties in benchmarking and achieving acceleration, and highlight our research aimed at predicting synthesis, making simulation more efficient, and developing new representations of materials. |
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