Next Generation Methods for Advanced Condensed Phase Simulations in Q-Chem

Q-Chem 中高级凝聚相模拟的下一代方法

• 批准号: 10011528
• 负责人: Evgeny Epifanovsky
• 金额: $ 50.49万
• 依托单位: Q-CHEM, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-05 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10011528
• 关键词: Algorithms; Ally; Area; Benchmarking; Binding; Biological; Biophysics; Cell Nucleus; Code; Computer Assisted; Computer Models; Computer software; Computers; Computing Methodologies; Crystallization; Drug Design; Electrons; Environment; Enzymes; Evaluation; Free Energy; Generations; Glycine; Hybrids; Industry; Isotonic Exercise; Letters; Libraries; Liquid substance; Mechanics; Methodology; Methods; Modeling; Modernization; Molecular; Molecular Conformation; Motion; Nuclear; Peptides; Periodicity; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Polymorph; Process; Production; Property; Proteins; Quantum Mechanics; Reaction; Research; Role; Scheme; Science; Scientist; Software Framework; Spectrum Analysis; Statistical Mechanics; System; Technology; Testing; Time; Update; Valine; Work; aqueous; base; biophysical model; biophysical tools; complex biological systems; computational chemistry; cost; density; electron density; electronic structure; flexibility; improved; innovation; lead optimization; molecular dynamics; next generation; novel; preference; professor; programs; prototype; quantum; simulation; simulation software; small molecule; software infrastructure; stem; synergism; theories; tool

PROJECT SUMMARY Next Generation Methods for Advanced Condensed Phase Simulations in Q-Chem Biophysical systems exist in the condensed phase, and that is the environment in which their properties should be computer-modeled. The correct theory to describe the electrons is using ab initio (AI) quantum mechanics (QM), whilst nuclear motion requires molecular dynamics (MD). The combination, AIMD, is thus the appropriate tool for biophysical simulations. While use of AIMD is vastly more expensive than MD with empirical potentials, it is nonetheless the standard to aspire to. AIMD enables correct treatment of bond-breaking for reactive processes, as well an accurate description of the non-bonded interactions that determine solvation and conformational preferences. This Phase II proposal has the objective of bringing a production level AIMD code to the Q-Chem software package. The key justification for the proposed work, and the potential value of the resulting product is that it will bring together capabilities that are not found jointly in any other AIMD code. The valuable synergy between the density functional theory implementation for periodic boundary conditions (DFT-PBC), and advanced algorithms for efficiently and accurate propagating the MD is the core innovation of this project. With regard to DFT-PBC (the first specific aim), the focus is on implementing high precision, high efficiency algorithms for the critical components of DFT with advanced functionals. Our code will support the latest meta-generalized gradient approximations (mGGAs), with inclusion of non-local van der Waals density functionals, that are not available in DFT-PBC codes to date. We will addition- ally provide support for range-separated exact exchange, with high efficiency. These capabilities will come with energies and gradients. Our software framework can also permit all-electron calculations as needed e.g. for NMR properties that depend on the electron density at the nucleus. Our modular code will support efficient on-node parallelism. To propagate MD efficiently and stably (the second specific aim), we employ two innovative statis- tical mechanics (SM) algorithms that have been proven in conventional MD, but are not yet available in any production AIMD code. First, we are extending the inertial extended Lagrangian self-consistent field (iEL/SCF) method to work robustly and efficiently with AIMD, building upon promising Phase I results, by combining it with a stochastic-isokinetic integration (SII) scheme to enable a single but larger MD time step. Second, we will explore the combination of iEL/SCF-SII with a multiple time- stepping method in which will explore whether different components of the QM force can be updated on different timescales in the AIMD. In final Aim 3 we test the combined DFT-PBC and iEL/SCF-SII capabilities on biophysical appli- cations including zwitterionic glycine and valine peptides in aqueous solution and molecular crystals. 1

项目摘要 Q-Chem中先进凝聚相模拟的下一代方法 生物物理系统存在于凝聚相，这是他们的环境。 应该用计算机模拟这些特性。描述电子的正确理论是用ab 量子力学（QM），而核运动需要分子动力学（MD）。的 因此，AIMD组合是生物物理模拟的适当工具。使用AIMD时 比具有经验潜力的MD昂贵得多，但它仍然是追求的标准。 到. AIMD能够正确处理反应过程中的键断裂，以及准确的 描述决定溶剂化和构象偏好的非键合相互作用。 本阶段II提案旨在将生产级AIMD代码引入Q-Chem 软件包。拟议工作的关键合理性，以及由此产生的潜在价值 产品的一个重要特点是，它将汇集在任何其他AIMD代码中未共同发现的功能。 周期性边界的密度泛函理论实现之间有价值的协同作用 条件（DFT-PBC），以及用于有效和准确传播MD的先进算法， 这个项目的核心创新。 关于DFT-PBC（第一个具体目标），重点是实现高精度、高可靠性和高可靠性。 DFT关键部分的有效算法与先进的泛函。我们的代码将 支持最新的元广义梯度近似（mGGA），包含非局部 货车德瓦耳斯密度泛函，这是不可用的DFT-PBC代码的日期。我们将增加- 支持范围分隔的精确交换，效率高。这些能力将 带着能量和梯度。我们的软件框架也可以允许全电子计算 根据需要，例如对于依赖于核处的电子密度的NMR性质。我们的模块化 代码将支持有效的节点上并行性。 为了有效和稳定地传播MD（第二个具体目标），我们采用了两种创新的统计方法- 已经在传统MD中被证明但尚未可用的物理力学（SM）算法 在任何生产AIMD代码中。首先，我们扩展了惯性扩展拉格朗日自洽 场（iEL/SCF）方法与AIMD一起稳健有效地工作，建立在有前途的阶段基础上 I结果，通过将其与随机等速积分（SII）方案相结合， MD时间步长较大。第二，我们将探索iEL/SCF-SII与多时间的组合- 步进方法，其中将探讨是否可以更新QM力的不同分量 在不同的时间尺度上。 在最终目标3中，我们在生物物理应用上测试了DFT-PBC和iEL/SCF-SII的组合功能。 阳离子，包括两性离子甘氨酸和缬氨酸肽在水溶液和分子晶体中。 1