Collaborative Research: CRIF: CRF: Extensible Cyberenvironments for Empirical and Semiempirical Hamiltonian Parameter Optimization and Dissemination

亚历山大D.位于巴尔的摩的马里兰州大学的麦克雷尔、伊利诺伊大学香槟分校的苏达卡尔、阿德里安·E.佛罗里达大学的Roitberg和John W. D.肯塔基州大学的Connolly和Michael Sheetz在网络基础设施和研究设施计划下得到了NSF化学部的支持。该项目将创建Cyberenstruments，以自动化经典分子力学（MM）和半经验（SE）Hamilton的参数化过程，并允许广泛传播所开发的参数。项目目标包括一个可扩展的cyberenarchment，用于i）快速和系统地参数化新的Hamilton和ii）系统地扩展当前可用的模型，从i）和ii）得到的参数集将通过cyberenarchment提供。集成环境将包括一个实验和量子力学参考数据的数据库，用于参数化过程沿着计算资源，用于数据采集、QM参考数据自动生成和参数优化过程自动化。GridChem计算化学网格将作为量子化学和分子力学服务的计算网络基础设施，并将包括创建和执行特定参数化方案的工作流程。将采用专门用于生成质量管理参考数据、监测参数优化和分析的工作流程管理工具，这些工具将包括允许专家干预参数化过程的界面。许多现有的流行MM和SE哈密顿将被整合，从广泛的参数，包括生物，有机和无机物种将可直接使用或进一步优化。该基础设施将在数据源和能源功能方面可扩展，使其适用于任何参数化方案。该项目将通过简化参数优化任务，使用理论方法更准确地描述各种材料、药理学和生物系统的静态和动态特性。这将允许为各种各样的分子系统生成高质量参数。建模精度的提高以及可访问的化学系统的范围将使化学，纳米技术，医学和生物学等领域受益。此外，用于工程领域的分析模型，如结构力学和流体动力学将成为分子水平的治疗。

Alexander D. MacKerrell of the University of Maryland at Baltimore, Sudhakar V. Pamidighantam of the University of Illinois at Urbana-Champaign, Adrian E. Roitberg of the University of Florida, and John W.D. Connolly and Michael Sheetz of the University of Kentucky are supported by the NSF Division of Chemistry under the Cyberinfrastructure and Research Facilities Program. This project will create Cyberenvironments to automate the process of parameterization for classical molecular mechanics (MM) and semi-empirical (SE) Hamiltonians and allow for wide dissemination of the developed parameters. Project goals include an extensible cyberenvironment for i) the rapid and systematic parameterization of novel Hamiltonians and ii) the systematic extension of currently available models, with the resulting parameters sets from both i) and ii) to be made available via the cyberenvironment. The integrated environment will include a database of experimental and quantum mechanical reference data to be used in the parameterization process along with computational resources for data acquisition, automatization of QM reference data generation and automatization of parameter optimization processes. GridChem Computational Chemistry Grid will serve as the computational cyberinfrastructure for Quantum Chemical and Molecular Mechanics services and will include workflows from which specific parameterization schemes will be created and executed. Workflow management tools specific to generating QM reference data, monitoring parameter optimization and analysis will be implemented and will include interfaces allowing for expert intervention in the parameterization process. Many existing popular MM and SE Hamiltonians will be integrated from which a wide range of parameters encompassing biological, organic and inorganic species will be accessible for direct use or further optimization. The infrastructure will be extensible in terms of data sources and energy functions allowing for its applicability in any parameterization scheme.This project will provide for more accurate descriptions of the static and dynamic properties of a wide range of material, pharmacological and biological systems using theoretical methods by simplifying the task of parameter optimization. This will allow for the generation of high quality parameters for a wide variety of molecular systems. Improvements in the accuracy of modeling as well as the range of accessible chemical systems will benefit such fields as chemistry, nanotechnology, medicine and biology, among others. In addition, analytical models used in engineering fields such as structural mechanics and fluid dynamics will become accessible to molecular level treatments.