Quantum Monte Carlo studies of rovibrational dynamics and quantum solvation in ultracold bosonic clusters

犹他州州立大学的大卫法雷利得到了化学系化学理论、模型和计算方法项目的支持，为量子扩散蒙特卡罗（DMC）方法开发了一种新的理论和计算方法。应用将作出分子和集群动力学。在所提出的算法中，波函数的节点超曲面-DMC计算的关键输入-在整个计算过程中进行计算和改进。节点搜索被配制成一个多维优化问题，并使用遗传算法来解决。方法和算法的发展正在进行中，这些将允许有效利用当前和未来的高端并行计算机。当前感兴趣的问题的模拟正在进行：这些包括掺杂到氦-4原子或仲氢分子的小玻色子液滴中的分子和分子簇的光谱学和动力学。玻色子溶剂液滴已经在实验中被用作纳米低温恒温器以形成外来物种和聚集体，作为化学纳米反应器以分离不稳定的反应中间体，以及作为光谱基质以研究大有机分子和纳米结构。Farrelly和他的同事正在开发一种新的理论和计算方法，用于研究复杂分子系统的量子动力学的量子蒙特卡罗算法，包括原子簇。 通常，这些算法的输入之一是量子波函数的节点结构，即，波函数为零的地方。 不幸的是，这种结构在计算之前通常是不知道的，所以必须“猜测”。 在该项目中，节点结构在计算过程中使用最先进的优化方法进行“动态”计算，从而实现更精确的计算。 量子蒙特卡罗方法广泛应用于先进新材料的合理设计、大气化学中重要过程的模拟（例如，气溶胶形成），和冷原子物理学（例如，量子计算和开发使用冷原子的电子学的基本上无噪声的原子类似物）。本科生和研究生，包括那些来自代表性不足的群体，正在接受最先进的计算方法的培训。由此产生的计算机程序正在设计，以便对其他研究人员有用和广泛访问。

David Farrelly of Utah State University is supported by the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop a new theoretical and computational approach to the quantum Diffusion Monte Carlo (DMC) method. Application will be made to molecular and cluster dynamics. In the proposed algorithm, the nodal hypersurfaces of wave functions - a key input to DMC calculations - are computed and improved on-the-fly throughout the calculation. The nodal search is formulated as a multi-dimensional optimization problem, and is solved using genetic algorithms. Both methodological and algorithmic developments are underway and these will allow efficient utilization of current and future high-end parallel computers. Simulations of problems of current interest are being performed: these include the spectroscopy and dynamics of molecules and molecular clusters doped into small bosonic droplets of helium-4 atoms or para-hydrogen molecules. Bosonic solvent droplets have been used experimentally as nanocryostats to form exotic species and aggregates, as chemical nanoreactors to isolate otherwise unstable reaction intermediates, and as spectroscopic matrices to study large organic molecules and nanostructures.Farrelly and his coworkers are developing a new theoretical and computational approach to Quantum Monte Carlo algorithms used for investigating the quantum dynamics of complex molecular systems, including clusters of atoms. Usually one of the inputs to these algorithms is the nodal structure of the quantum wavefunctions, i.e., the places where the wavefunction is zero. Unfortunately, this structure is not usually known prior to the calculation so it must be "guessed at". In this project, the nodal structure is computed "on the fly" during the calculation using state-of-the art optimization methods, leading to a more accurate calculation. Quantum Monte Carlo methods are widely used in fields such as the rational design of advanced new materials, simulations of processes important in atmospheric chemistry (e.g., aerosol formation), and cold-atom physics (e.g., quantum computing and developing essentially noise-free atomic analogs of electronics using cold atoms). Undergraduate and graduate students, including those from underrepresented groups, are being trained in state-of-the art computational methods. The resulting computer program is being designed so as to be useful and broadly accessible to other researchers.