Selection, calibration, and validation of coarse-grained models of atomistic systems
Selection, calibration, and validation of coarse-grained models of atomistic systems
复制标题
原子系统粗粒度模型的选择、校准和验证
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Kathryn Anne Farrell
中科院分区：
文献类型：
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作者：
Kathryn Anne Farrell
致谢 言语无法表达我对那些给予我无尽帮助和不断鼓励的人的感激之情，没有他们我就不可能完成这一壮举。我要感谢我的本科生导师迈克尔·霍尔斯特（Michael Holst），他在我之前就信任我，并在我的研究生生涯中不断为我提供建议和鼓励。我还必须感谢我的委员会，特别是我的顾问 J. Ron Elber 和前委员会成员 Peter Rossky，他们的指导和挑战提高了这项工作的质量。 J. Tinsley Oden 对预测科学富有感染力的热情、他对计算科学与工程学科的愿景和发展，以及他对学习的永不满足的胃口启发并塑造了这项工作。与 Serge Prudhomme 进行的许多发人深省的理论讨论无疑对这项工作以及我对这一领域的理解和欣赏产生了不可量化的影响。我还要向 Peter 和 Edith O'Donnell 表示感谢，感谢他们在过去六年中给予的许多富有成效的讨论和支持。我对劳伦·戴乌托 (Lauren Daiuto)、马修·莫平 (Matthew Maupin)、我的父母以及我的家人的无条件的爱和鼓励怀有难以形容的感激之情，本书就是献给他们的。本论文研究了原子系统粗粒度模型的开发，其目的是在存在不确定性的情况下预测感兴趣的目标量。它解决了计算科学和工程中有关模型选择、校准和验证过程的基本问题，这些过程用于通过统一的贝叶斯框架构建预测降阶模型。该框架通过信息论、敏感性分析和奥卡姆剃刀的概念得到增强，提供了构建适合在预测场景中使用的粗粒度模型的系统方法。提出了统计校准和验证分子系统的通用框架的新颖应用。原子模型本身包含不确定性，被视为基本事实，并为模型参数的贝叶斯更新提供数据。通过贝叶斯模型合理性的强大概念解决了选择适当的粗粒度模型的开放问题。 vi 提出了一种新的模型验证自适应算法。奥卡姆合理性算法 (OPAL) 因其遵守奥卡姆剃刀和贝叶斯模型似然性的使用而得名，它在大量模型中识别出通过贝叶斯验证测试的最简单模型，因此可用于预测所选数量……
Acknowledgments Words cannot express my gratitude for the endless help and unfailing encouragement of those without whom I would not have accomplished this feat. I would like to thank my undergraduate advisor, Michael Holst, who believed in me before I did and offered me continuous advice and encouragement throughout my graduate career. I must also thank my committee, especially my advisors, J. Ron Elber, and former committee member, Peter Rossky, for their guidance and challenges that have enhanced the quality of this work. J. Tinsley Oden's contagious passion for predictive science, his vision and development of the discipline of computational science and engineering, and his insatiable appetite for learning have inspired and shaped this work. The many thought-provoking, theoretical discussions with Serge Prudhomme have, no doubt, had an unquantifiable impact on this work and my understanding and appreciation for this field. I would also like to extend my gratitude to Peter and Edith O'Donnell for the support they have given the for the many fruitful discussions and support throughout the past six years. I am indescribably grateful for the unconditional love and encouragement of Lauren Daiuto, Matthew Maupin, my parents, and the rest iv of my family, to whom this work is dedicated. This dissertation examines the development of coarse-grained models of atomistic systems for the purpose of predicting target quantities of interest in the presence of uncertainties. It addresses fundamental questions in computational science and engineering concerning model selection, calibration, and validation processes that are used to construct predictive reduced order models through a unified Bayesian framework. This framework, enhanced with the concepts of information theory, sensitivity analysis, and Occam's Razor, provides a systematic means of constructing coarse-grained models suitable for use in a prediction scenario. The novel application of a general framework of statistical calibration and validation to molecular systems is presented. Atomistic models, which themselves contain uncertainties, are treated as the ground truth and provide data for the Bayesian updating of model parameters. The open problem of the selection of appropriate coarse-grained models is addressed through the powerful notion of Bayesian model plausibility. vi A new, adaptive algorithm for model validation is presented. The Occam-Plausibility ALgorithm (OPAL), so named for its adherence to Occam's Razor and the use of Bayesian model plausibilities, identifies, among a large set of models, the simplest model that passes the Bayesian validation tests, and may therefore be used to predict chosen quantities …
混合原子和粗粒度分子动力学：膜结合离子通道的模拟。
DOI：
10.1021/jp062700h
发表时间：
2006
期刊：
The journal of physical chemistry. B
影响因子：
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作者：
Shi,Qiang;Izvekov,Sergei;Voth,GregoryA
通讯作者：
Voth,GregoryA
DOI：
10.1021/jp9107206
发表时间：
2010-05-27
期刊：
The journal of physical chemistry. B
影响因子：
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作者：
Shinoda W;DeVane R;Klein ML
通讯作者：
Klein ML