Ab-Initio Geometry Optimization of Large Molecules

描述（由申请人提供）：密度泛函理论（DFT）由于其准确性而非常适合建模生物分子。虽然能量的DFT计算现在在大分子中很常见，但DFT几何优化器的使用仍然局限于包含不超过30个原子的相对较小的分子。关键的限制是，达到最佳几何形状所需的步骤数量随着分子中原子数量的增加而增加。此外，传统的优化器对生物化学中通常遇到的软性分子表现得特别差。这一建议是基于DFT几何优化算法的根本改变，有可能实现分子大小的量子飞跃，可以优化。新的优化器采用比传统优化器更精确的几何步骤，因此只需少量步骤（对于Taxol大约10个步骤）即可达到最佳几何形状。每一个“智能步骤”都是通过一个方程的迭代解得到的，这个方程对于松软分子来说是病态的。在第一阶段，我们设计了一个经济的程序来解决这个病态方程，在第二阶段，我们建议完成基于这个智能步骤的几何优化器的开发。提出的算法将在Q-Chem软件包中实现，如果成功，将通过Q-Chem， Inc.和Spartan， Inc.广泛分发。

DESCRIPTION (provided by applicant): Density-Functional Theory (DFT) is highly desirable for modeling biomolecules due to its accuracy. While DFT calculations of the energy are now common for large molecules, the use of DFT geometry optimizers is still confined to relatively small molecules containing no more than thirty atoms. The key limitation is that the number of steps required to reach the optimum geometry increases with the number of atoms in the molecule. Furthermore, conventional optimizers perform particularly poorly for the floppy molecules typically encountered in biochemistry. This proposal is based on a radical change in the algorithm for DFT geometry optimization, potentially enabling a quantum leap in the size of molecules that can be optimized. The new optimizer takes a much more accurate geometry step than conventional optimizers, so that the optimum geometry is reached in a small number of steps (about 10 for Taxol). Each of these 'smart steps' is obtained via the iterative solution of an equation which is ill-conditioned for floppy molecules. In phase I we devised an economical procedure to solve this ill-conditioned equation, and in phase II we propose to complete the development of a geometry optimizer based on this smart step. The proposed algorithm will be implemented in the Q-Chem software package and, if successful, widely distributed through Q-Chem, Inc. and Spartan, Inc.