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

犹他州立大学的David Farrelly在化学系化学理论、模型和计算方法计划的支持下，开发了一种新的量子扩散蒙特卡罗(DMC)方法的理论和计算方法。将应用于分子动力学和团簇动力学。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--在整个计算过程中被动态地计算和改进。将节点搜索问题转化为多维优化问题，并利用遗传算法进行求解。方法论和算法的开发都在进行中，这将使目前和未来的高端并行计算机得到有效利用。目前正在对当前感兴趣的问题进行模拟：其中包括掺杂到氦-4原子或对氢分子的小玻色子液滴中的分子和分子簇的光谱和动力学。玻色子溶剂液滴已被实验用作纳米晶体以形成外来物种和聚集体，用作化学纳米反应器以分离原本不稳定的反应中间产物，以及用作光谱矩阵来研究有机大分子和纳米结构。Farrelly和他的同事们正在开发一种新的量子蒙特卡罗算法的理论和计算方法，用于研究包括原子簇在内的复杂分子系统的量子动力学。通常，这些算法的输入之一是量子波函数的节点结构，即波函数为零的位置。不幸的是，这种结构通常在计算之前并不为人所知，因此它必须被“猜测”。在本项目中，节点结构在计算过程中使用最先进的优化方法进行“即时”计算，从而实现更准确的计算。量子蒙特卡罗方法被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(例如量子计算和使用冷原子开发基本上无噪声的电子模拟原子)等领域。本科生和研究生，包括那些来自代表不足的群体的学生，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进行设计，以便于其他研究人员使用和广泛使用。

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.