Zobrazeno 1 - 10
of 452
pro vyhledávání: '"Lane, E. P."'
Autor:
Jacobs, Ryan, Polak, Maciej P., Schultz, Lane E., Mahdavi, Hamed, Honavar, Vasant, Morgan, Dane
We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several mol
Externí odkaz:
http://arxiv.org/abs/2409.06080
Autor:
Jacobs, Ryan, Schultz, Lane E., Scourtas, Aristana, Schmidt, KJ, Price-Skelly, Owen, Engler, Will, Foster, Ian, Blaiszik, Ben, Voyles, Paul M., Morgan, Dane
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision
Externí odkaz:
http://arxiv.org/abs/2406.15650
Autor:
Meng, Jun, Sheikh, Md Sariful, Schultz, Lane E., Nachlas, William O., Liu, Jian, Polak, Maciej P., Jacobs, Ryan, Morgan, Dane
Oxygen ion conductors are crucial for enhancing the efficiency of various clean energy technologies, including fuel cells, batteries, electrolyzers, membranes, sensors, and more. In this study, LaBi2O4Cl is identified as an ultra-fast oxygen conducto
Externí odkaz:
http://arxiv.org/abs/2406.07723
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new approach of assessing model domain and demonstrate that our approach provides accura
Externí odkaz:
http://arxiv.org/abs/2406.05143
Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demanding compared to a single model when it is used for inference. In this wo
Externí odkaz:
http://arxiv.org/abs/2404.09896
Autor:
Afflerbach, Benjamin T., Francis, Carter, Schultz, Lane E., Spethson, Janine, Meschke, Vanessa, Strand, Elliot, Ward, Logan, Perepezko, John H., Thoma, Dan, Voyles, Paul M., Szlufarska, Izabela, Morgan, Dane
Publikováno v:
Chemistry of Materials, 2022, 34(7), 2945-2954
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and
Externí odkaz:
http://arxiv.org/abs/2305.15390
We explore the use of characteristic temperatures derived from molecular dynamics to predict aspects of metallic Glass Forming Ability (GFA). Temperatures derived from cooling curves of self-diffusion, viscosity, and energy were used as features for
Externí odkaz:
http://arxiv.org/abs/2109.13342
Autor:
Schultz, Lane E., Afflerbach, Benjamin, Francis, Carter, Voyles, Paul M., Szlufarska, Izabela, Morgan, Dane
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have used statist
Externí odkaz:
http://arxiv.org/abs/2107.14157
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Akademický článek
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