Výsledky vyhledávání - "Messerly, Richard"

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Machine learning potentials with Iterative Boltzmann Inversion: training to experiment

Autor: Matin, Sakib, Allen, Alice, Smith, Justin S., Lubbers, Nicholas, Jadrich, Ryan B., Messerly, Richard A., Nebgen, Benjamin T., Li, Ying Wai, Tretiak, Sergei, Barros, Kipton

Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different character than simulated data, and most MLP training procedures cannot

Externí odkaz: http://arxiv.org/abs/2307.04712

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Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning

Autor: Allen, Alice E. A., Lubbers, Nicholas, Matin, Sakib, Smith, Justin, Messerly, Richard, Tretiak, Sergei, Barros, Kipton

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverag

Externí odkaz: http://arxiv.org/abs/2307.04012

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Akademický článek

Building a DFT+U machine learning interatomic potential for uranium dioxide

Autor: Stippell, Elizabeth, Alzate-Vargas, Lorena, Subedi, Kashi N., Tutchton, Roxanne M., Cooper, Michael W.D., Tretiak, Sergei, Gibson, Tammie, Messerly, Richard A.

Publikováno v: In Artificial Intelligence Chemistry June 2024 2(1)

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Bayesian inference-driven model parameterization and model selection for 2CLJQ fluid models

Autor: Madin, Owen C., Boothroyd, Simon, Messerly, Richard A., Chodera, John D., Fass, Josh, Shirts, Michael R.

A high level of physical detail in a molecular model improves its ability to perform high accuracy simulations, but can also significantly affect its complexity and computational cost. In some situations, it is worthwhile to add additional complexity

Externí odkaz: http://arxiv.org/abs/2105.07863

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