Physics

We present a geometry class for efficiently simulating particle transport through aerosols in GEANT4. It is demonstrated that aerosol granularity can strongly affect this transport and thus a generic aerosol model must respect this granularity, which the presented class achieves by modelling the aerosol as a collection of droplets. For large aerosols, this class is orders of magnitude quicker and less memory intensive than standard granularity-respecting methods to model aerosols in GEANT4. These gains are allowed by simpler voxelization optimization, by only populating droplets relevant to the transport, and by using droplet geometry to consider fewer droplets per calculation. The presented class allows differing aerosol bulk/droplet shape, droplets with structure, and spatially varying droplet position/rotation.

The real-space density-functional perturbation theory (DFPT) for the computations of the response properties with respect to the atomic displacement and homogeneous electric field perturbation has been recently developed and implemented into the all-electron, numeric atom-centered orbitals electronic structure package FHI-aims. It is found that the bottleneck for large scale applications is the computation of the response density matrix, which scales as O( N 3 ) . Here for the response properties with respect to the homogeneous electric field, we present an efficient parallel linear scaling algorithm for the response density matrix calculation. Our scheme is based on the second-order trace-correcting purification and the parallel sparse matrix-matrix multiplication algorithms. The new scheme reduces the formal scaling from O( N 3 ) to O(N) , and shows good parallel scalability over tens of thousands of cores. As demonstrated by extensive validation, we achieve a rapid computation of accurate polarizabilities using DFPT. Finally, the computational efficiency of this scheme has been illustrated by making the scaling tests and scalability tests on massively parallel computer systems.

We present a new efficient transition pathway search method based on the least action principle and the Gaussian process regression method. Most pathway search methods developed so far rely on string representations, which approximate a transition pathway by a series of slowly varying replicas of a system. Since those methods require a large number of replica images, they are computationally expensive in general. Our approach employs the Gaussian process regression method, which takes the Bayesian inference on the shape of a given potential energy surface with a few observed data and Gaussian-shaped kernel functions. Based on the inferred potential, we find multiple low-action pathways by carrying out the action optimization based on the Action-CSA (Conformational space annealing). Here we demonstrate a drastic elevation of computing efficiency about five orders of magnitude for the system with the Muller-Brown potential. Further, for the sake of demonstrating its real-world capabilities, we apply our method to ab initio calculations on alanine dipeptide. The improved efficiency of GPAO makes it possible to identify multiple transition pathways of alanine dipeptide and calculate their transition probabilities with ab initio accuracy. We are confident that our GPAO method is a powerful approach to investigate the mechanisms of complex chemical reactions

Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop a novel approach to reduce this bottleneck which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. Using Monte Carlo we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme, in order to substantially reduce computational resources at each step. For example problems of Quantitative Photoacoustic Tomography and Ultrasound Modulated Optical Tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high accuracy forward run of the same Monte Carlo model. This approach demonstrates significant computational savings when approaching the full non-linear inverse problem of optical property estimation using stochastic methods.

Numerically solving a second quantised many-body model in the permutation symmetric Fock space can be challenging for two reasons: (i) an increased complication in the calculations of the matrix elements of various operators, and (ii) a poor scaling of the cost of these calculations with the Fock space size. We present a method that solves both these problems. We find a mapping that can be used to simplify the calculations of the matrix elements. The mapping is directly generated so its computational cost scales only linearly with the space size and is negligible even for large enough sizes that approach the thermodynamic limit. A fortran implementation of the method as a library - FockMap - is provided along with a test program.

Robust automation of the shot peen forming process demands a closed-loop feedback in which a suitable treatment pattern needs to be found in real-time for each treatment iteration. In this work, we present a method for finding the peen-forming patterns, based on a neural network (NN), which learns the nonlinear function that relates a given target shape (input) to its optimal peening pattern (output), from data generated by finite element simulations. The trained NN yields patterns with an average binary accuracy of 98.8\% with respect to the ground truth in microseconds.

We develop a resource efficient step-merged quantum imaginary time evolution approach (smQITE) to solve for the ground state of a Hamiltonian on quantum computers. This heuristic method features a fixed shallow quantum circuit depth along the state evolution path. We use this algorithm to determine binding energy curves of a set of molecules, including H 2 , H 4 , H 6 , LiH, HF, H 2 O and BeH 2 , and find highly accurate results. The required quantum resources of smQITE calculations can be further reduced by adopting the circuit form of the variational quantum eigensolver (VQE) technique, such as the unitary coupled cluster ansatz. We demonstrate that smQITE achieves a similar computational accuracy as VQE at the same fixed-circuit ansatz, without requiring a generally complicated high-dimensional non-convex optimization. Finally, smQITE calculations are carried out on Rigetti quantum processing units (QPUs), demonstrating that the approach is readily applicable on current noisy intermediate-scale quantum (NISQ) devices.

We consider the effects of electron-hole interaction, 2D confinement and applied electric field on direct allowed transitions in III-V semiconductors, with InGaAs as a study case. Instead of Coulomb interaction, we use Gaussian potential. It is finite at the origin and has a finite effective range, which allows for a more efficient numerical solution of Schrödinger equation. Yet, we can expect electroabsorption phenomena to remain qualitatively similar to the ones observed for Coulomb excitons. Moreover, we use variation of parameters to fit both position and magnitude of the first absorption peak to the Coulomb case. We combine and compare several numerical and approximate methods, including spectral expansion, finite differences, separation of variables and variational approximation. We find that separation of variables approach works only for quantum well widths smaller than the exciton radius. After separation of variables, finite difference solution of the resulting interaction equation gives a much better agreement with the full spectral solution than naive variational approximation. We observe that electric field has a critical effect on the magnitudes of exciton absorption peaks, suppressing previously allowed transitions and enhancing forbidden ones. Moreover, for excited states, initially suppressed transitions are enhanced again at higher field strengths.

Open-source electromagnetic design software, Elmer FEM, was interfaced with data analytics toolkit, Dakota. Furthermore, the coupled software was validated against a benchmark test. The interface developed provides a unified open-source computational framework for electromagnetics and data analytics. Its key features include uncertainty quantification, surrogate modelling and parameter studies. This framework enables a richer understanding of model predictions to better design electric machines in a time sensitive manner.

Three-dimensional, time-dependent direct simulations of step emulsification micro-devices highlight two essential mechanisms for droplet formation: first, the onset of an adverse pressure gradient driving a back-flow of the continuous phase from the external reservoir to the micro-channel. Second, the striction of the flowing jet which leads to its subsequent rupture. It is also shown that such a rupture is delayed and eventually suppressed by increasing the flow speed of the dispersed phase within the channel, due to the stabilising effect of dynamic pressure. This suggests a new criterion for dripping-jetting transition, based on local values of the Capillary and Weber numbers.