Ab Initio molecular Dynamics with Quantum Nuclear Effects: Potential Surfaces and Gradients from on-the-fly Graph-Theory-Based Molecular Fragmentation Methods

具有量子核效应的从头算分子动力学：基于动态图论的分子断裂方法的势表面和梯度

• 批准号: 2102610
• 负责人: Srinivasan Iyengar
• 金额: $ 45万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102610&HistoricalAwards=false
• 关键词: Ab; Initio; molecular; Dynamics; Quantum

Srinivasan S. Iyengar of Indiana University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. Many problems at the forefront of energy, environmental and biological research demand the quantum mechanical treatment of electrons and nuclei, but the detailed quantum-mechanical description of such problems is much too complex even in today’s high performance computing environments. This is because the computational complexity in these problems grows exponentially with system size, which makes them intractable. Iyengar and his research group are developing new computational methods to address these issues. These methods are based on a mathematical idea called graph-theory that allows Iyengar and co-workers to partition a molecular system into regions that communicate through an idea called electron correlation. This is very similar to Google maps, where cities are connected through highways, and in the same way, in Iyengar’s formalism molecular domains are connected through similar roads and bridges that provide pathways for electrons to communicate through a concept called electron correlation. Unfortunately, while electron correlation allows electrons to communicate and has a critical role in all chemical processes, this concept is also responsible for the catastrophic computational complexity of obtaining accurate molecular properties. By creating such graph-theoretic methods, Iyengar will help to reduce the computational complexity of these problems, to allow state of art calculations. These methods are poised to have major impact on the study of a wide class of problems in fields ranging from enzymology to atmospheric chemistry to materials science, including the study of hydrogen transfer in polymer electrolyte fuel cells. In addition, the methods are also poised to allow innovative implementations on a mixed set of hybrid quantum and classical computing systems. The methods being developed by Iyengar are at the intersection of modern computational quantum chemistry and chemical physics. Hence students in the group have the opportunity to learn and develop new theoretical methods and apply these methods to important problems. The results, involving computer codes as well as novel scientific ideas are to be disseminated to the scientific community. Specifically, the computer programs developed by Iyengar will appear as part of the NSF-funded SEAGrid science gateway. Furthermore, as a member of the quantum science center at Indiana University, and as the director of the university-wide scientific computing program, Iyengar will be involved in the organization of summer workshops for middle- and high-school teachers from the local Bloomington, Indiana area to provide cross-disciplinary training in chemistry, physics and computer science. These workshops will focus on the quantum nature of matter, providing a unified treatment of problems in physics, chemistry and biochemistry; furthermore, modeling these problems is then to be done through connections to computational algorithms. Through involvement in the Holland Hudson Scholars Program (HHSP) and the Indiana Louis Stokes Alliance for Minority Participation (LSAMP) program, the PI will work to recruit students from under-represented groups. Ab initio molecular dynamics (AIMD) is appealing, since it does not need a priori fitted potentials. This allows application of AIMD as a self-contained black box. But this advantage is deeply affected by the cost of evaluating the electronic potential and forces. Hence, most applications of AIMD are limited to density functional theoretic (DFT) treatment. While there has been substantial progress in developing accurate DFT functionals, fundamental challenges remain. This proposal deals with the development and application of on-the-fly graph-theoretic techniques to compute accurate, low-scaling AIMD trajectories that are in agreement with post-Hartree-Fock electronic structure, but at the cost of DFT. These developments are applicable for both cluster studies as well as periodic condensed phase problems, such as reactions on surfaces. In addition, during a single AIMD step, the approach can integrate multiple electronic structure packages. Current capabilities include the ability to use Gaussian, ORCA, Psi4, Quantum Espresso and OpenMX within a single AIMD umbrella. There are three specific aims in this proposal: (1) to implement the team's graph theory-based approach in an asynchronous fashion on novel hybrid, interleaved, quantum/classical computing hardware. This will allow the steep scaling aspects of our method to be treated on quantum hardware, the lower scaling aspects and graph-theoretic decomposition of molecular structure on classical hardware and provide a new thrust for studying reactive chemical problems; (2) to study hydrogen transfer reactions on the surface of water. The systems studied are in the condensed phase, and of critical importance in atmospheric chemistry. The reactions considered deal with isoprene-based hydroxy-peroxy radicals, thought to be pivotal on hydroxyl radical concentrations in the atmosphere. (3) The graph theory-based approach will be used to construct multi-dimensional potential surfaces for hydrogen-transfer reactions to gauge quantum nuclear effects.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.

印第安纳大学的 Srinivasan S. Iyengar 获得了化学系化学理论、模型和计算方法项目的奖项支持，能源、环境和生物研究前沿的许多问题都需要电子和原子核的量子力学处理，但即使在当今的高性能计算环境中，此类问题的详细量子力学描述也过于复杂，这是因为这些问题的计算复杂性随着系统规模呈指数增长，这使得艾扬格和他的研究变得棘手。研究小组开发了解决这些问题的新计算方法，这些方法基于一种称为图论的数学思想，该思想允许艾扬格和同事将分子系统划分为通过称为电子关联的思想进行通信的区域。类似于谷歌地图，城市通过高速公路连接起来，同样，在艾扬格的形式主义中，分子域通过类似的道路和桥梁连接起来，这些道路和桥梁为电子提供了通过电子相关概念进行通信的路径，不幸的是，电子关联允许。电子进行通信和在所有化学过程中都起着至关重要的作用，这个概念也是获得准确分子特性的灾难性计算复杂性的原因。通过创建这种图论方法，艾扬格将有助于降低这些问题的计算复杂性，从而实现最先进的技术。这些方法将对酶学、大气化学和材料科学等领域的一系列问题的研究产生重大影响，包括聚合物电解质燃料电池中的氢转移研究。准备允许在混合量子和经典计算系统上进行创新实现，艾扬格正在开发的方法处于现代计算量子化学和化学物理学的交叉点，因此该小组的学生有机会学习和开发新的理论方法和方法。将这些方法应用于重要问题。具体来说，艾扬格开发的计算机程序将作为 NSF 资助的 SEAGrid 科学网关的一部分出现。作为会员作为印第安纳大学量子科学中心的主任，以及全校科学计算项目的主任，艾扬格将参与为印第安纳州布卢明顿当地初中和高中教师组织夏季研讨会，以提供交叉-化学、物理和计算机科学方面的学科培训。这些研讨会将重点关注物质的量子性质，提供物理、化学和生物化学问题的统一处理；此外，通过与计算算法的连接来对这些问题进行建模； .通过参与荷兰哈德逊学者计划(HHSP) 和印第安纳州路易斯斯托克斯少数族裔参与联盟 (LSAMP) 项目中，PI 将致力于从代表性不足的群体中招收学生。从头算分子动力学 (AIMD) 很有吸引力，因为它不需要先验拟合势。这允许 AIMD 作为独立的黑匣子进行应用，但这种优势深受评估电子势和力的成本的影响，因此，AIMD 的大多数应用仅限于密度泛函理论 (DFT)。虽然在开发精确的 DFT 泛函方面取得了实质性进展，但该提案涉及动态图论技术的开发和应用，以计算与数据一致的精确、低尺度的 AIMD 轨迹。后 Hartree-Fock 电子结构，但以 DFT 为代价，这些进展适用于簇研究以及周期性凝聚相问题，例如表面反应。此外，在单个 AIMD 步骤中，该方法可以集成。多种的目前的功能包括在单个 AIMD 框架内使用 Gaussian、ORCA、Psi4、Quantum Espresso 和 OpenMX。该提案有三个具体目标：(1) 在一个模型中实现团队基于图论的方法。新型混合、交错、量子/经典计算硬件上的异步方式这将允许我们的方法的陡峭缩放方面在量子硬件上处理，较低的缩放方面以及分子结构的图论分解在经典硬件上处理。并为研究反应化学问题提供新的推动力；（2）研究水表面的氢转移反应，所研究的反应在大气化学中至关重要。羟基过氧自由基，被认为对大气中的羟基自由基浓度至关重要 (3) 基于图论的方法将用于构建氢转移反应的多维势表面，以测量量子核效应。该奖项反映了。 NSF 的法定使命通过使用基金会的智力价值和更广泛的影响审查标准进行评估，该项目被认为值得支持。

项目成果

Synthesis of Hidden Subgroup Quantum Algorithms and Quantum Chemical Dynamics

隐藏子群量子算法和量子化学动力学的综合

• DOI: 10.1021/acs.jctc.3c00404
• 发表时间: 2023-09
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Iyengar, Srinivasan S.;Kumar, Anup;Saha, Debadrita;Sabry, Amr
• 通讯作者: Sabry, Amr

Quantum Computing with Dartboards

使用飞镖进行量子计算

• DOI: 10.1021/acs.jpca.3c04262
• 发表时间: 2023-09
• 期刊: The Journal of Physical Chemistry A
• 作者: Ganti, Ishaan;Iyengar, Srinivasan S.
• 通讯作者: Iyengar, Srinivasan S.

Graph-| Q ⟩⟨ C |: A Quantum Algorithm with Reduced Quantum Circuit Depth for Electronic Structure

图-|

• DOI: 10.1021/acs.jpca.3c04261
• 发表时间: 2023-11
• 期刊: The Journal of Physical Chemistry A
• 作者: Iyengar, Srinivasan S.;Zhang, Juncheng Harry;Saha, Debadrita;Ricard, Timothy C.
• 通讯作者: Ricard, Timothy C.

Graph-| Q ⟩⟨ C |, a Graph-Based Quantum/Classical Algorithm for Efficient Electronic Structure on Hybrid Quantum/Classical Hardware Systems: Improved Quantum Circuit Depth Performance

图-|

• DOI: 10.1021/acs.jctc.1c01303
• 发表时间: 2022-05
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Zhang, Juncheng Harry;Iyengar, Srinivasan S.
• 通讯作者: Iyengar, Srinivasan S.

Graph-Theory-Based Molecular Fragmentation for Efficient and Accurate Potential Surface Calculations in Multiple Dimensions

基于图论的分子断裂，高效准确的多维势面计算

• DOI: 10.1021/acs.jctc.1c00065
• 发表时间: 2021-11
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Kumar, Anup;DeGregorio, Nicole;Iyengar, Srinivasan S.
• 通讯作者: Iyengar, Srinivasan S.