Towards Predictive Coarse-grained Models

宾夕法尼亚州立大学的威廉·诺德得到了化学系化学理论、模型和计算方法项目的支持，该项目旨在开发理论和计算方法，以提高化学和材料科学中粗粒度模型的预测能力。原子细节模拟提供了对分子结构、动力学和相互作用的精细洞察。然而，由于计算成本的原因，原子细节模拟只能有效地研究非常小的长度和时间尺度。相比之下，粗粒度(CG)模型通过消除不必要的原子细节，为模拟许多具有基础和技术意义的过程提供了必要的效率，这些过程远远超出了原子细节模型的范围，例如病毒入侵宿主细胞的机制或具有工业重要性的聚合物的相行为。不幸的是，现有的CG模型对热力学性质的描述相对较差。此外，CG模型往往表现出较差的可转移性，即它们需要对每个感兴趣的系统和环境进行重新参数化。这些根本性的限制严重削弱了当前CG模型的预测能力。William Noid和他的研究小组将推导、实施和评估理论和计算方法，以确保CG模型不仅高效，而且为液体和生物分子等软材料建模提供预测精度和可转移性。此外，威廉·诺德将继续发展一个跨代科学俱乐部，让所有年龄段的学生参与科学话语和发现。威廉·诺德和他的研究小组将开发严谨的理论和稳健的计算方法，以解决自下而上CG模型的根本局限性。Noid和他的研究小组将分析平均力(PMF)的多体势能，以揭示基本的见解，并推导出改进自下而上模型的可传递性和热力学性质的实用方法。由此得到的洞察力将提供一种双重的方法来解决CG对势的密度依赖以及多体局部密度势的温度和成分依赖。Noid和他的研究小组还将研究用CG模型描述自组装热力学驱动力的双重方法。Noid和他的研究小组将研究CG映射对精确PMF的影响以及对近似CG模型的性质的影响。Noid和他的团队将开发和分发实现这些方法的软件，作为自下而上的开源粗粒度软件(BOCS)包的一部分。Noid将为研究生提供指导和严格的培训。此外，诺德和他的团队将发展一个跨代科学俱乐部，将当地老年人、退休教师和本科生纳入其中，以在学术和民间社区之间架起桥梁，教育公众当代科学话题，分享科学发现的喜悦，并促进健康的终身学习生活方式。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

William Noid of the Pennsylvania State University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop theory and computational methods for improving the predictive power of coarse-grained models in the chemical and materials sciences. Atomically detailed simulations provide exquisite insight into molecular structure, dynamics, and interactions. However, due to their computational cost, atomically detailed simulations can only effectively investigate very small length- and time-scales. In contrast, by eliminating unnecessary atomic details, coarse-grained (CG) model promise the necessary efficiency for simulating many processes of fundamental and technological significance that are far beyond the scope of atomically detailed models, e.g., the mechanisms by which viruses invade host cells or the phase behavior of industrially important polymers. Unfortunately, existing CG models provide a relatively poor description of thermodynamic properties. Moreover, CG models often demonstrate poor transferability, i.e., they require reparameterization for each system and environment of interest. These fundamental limitations severely curtail the predictive powers of current CG models. William Noid and his research group will derive, implement, and assess both theory and computational methods for ensuring that CG models are not only efficient, but also provide predictive accuracy and transferability for modeling soft materials, such as liquids and biomolecules. In addition, William Noid will continue developing an intergenerational science club that engages students of all ages in scientific discourse and discovery. William Noid and his research group will develop rigorous theory and robust computational methods for addressing fundamental limitations of bottom-up CG models. Noid and his research group will analyze the many-body potential of mean force (PMF) to reveal fundamental insight and derive practical approaches for improving both the transferability and the thermodynamic properties of bottom-up models. The resulting insight will inform a dual approach for addressing the density-dependence of CG pair potentials, as well as the temperature-and composition-dependence of many-body local density potentials. Noid and his research group will also investigate the dual approach for describing the thermodynamic driving forces for self-assembly with CG models. Noid and his research group will investigate the influence of the CG mapping upon the exact PMF and upon the properties of approximate CG models. Noid and his group will develop and distribute software for implementing these methods as part of the Bottom-up Open-source Coarse-graining Software (BOCS) package. Noid will provide mentorship and rigorous training for graduate students. Moreover, Noid and his group will develop an intergenerational science club that integrates local senior citizens, emeritus faculty, and undergraduate students in order to build bridges between the academic and civic communities, educate the public about contemporary scientific topics, share the joy of scientific discovery, and promote a healthy lifestyle of life-long learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.