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We provide an approach to sample rare events during classical ab initio molecular dynamics and quantum wavepacket dynamics. For classical AIMD, a set of fictitious degrees of freedom are introduced that may harmonically interact with the electronic and nuclear degrees of freedom to steer the dynamics in a conservative fashion toward energetically forbidden regions. A similar approach when introduced for quantum wavepacket dynamics has the effect of biasing the trajectory of the wavepacket centroid toward the regions of the potential surface that are difficult to sample. The approach is demonstrated for a phenol-amine system, which is a prototypical problem for condensed phase-proton transfer, and for model potentials undergoing wavepacket dynamics. In all cases, the approach yields trajectories that conserve energy while sampling rare events. 

Abstract Desiccation cracking is a frequent natural phenomenon that occurs in drying soil and has a significant negative impact on the mechanical and hydraulic properties of clay or geomaterials in various engineering applications. In this study, recycled glass sand (RGS) was used to reduce the plasticity of clay soil and mitigate desiccation cracks in clay soils. The effect of the RGS particle size and content was investigated using a desiccation crack observation test. Digital image processing technology was used to evaluate the crack rate, length, width, and area during the observation test. The results reveal that the cracking rate was inversely proportional to the RGS content and directly proportional to the RGS particle size. For instance, the cracking rate of clay soil treated with 25% RGS with a particle size of 0.15 mm was reduced to 0.17% compared with untreated soil. The strengths of the untreated and RGS-treated soils were evaluated through unconfined compression tests. The unconfined compressive strength of the RGS-treated clay soil decreased slightly with the addition of RGS. In general, the addition of RGS has great potential for mitigating desiccation cracks in clay soils. 

Modern High Performance Computing (HPC) systems are built with innovative system architectures and novel programming models to further push the speed limit of computing. The increased complexity poses challenges for performance portability and performance evaluation. The Standard Performance Evaluation Corporation (SPEC) has a long history of producing industry-standard benchmarks for modern computer systems. SPEC’s newly released SPEChpc 2021 benchmark suites, developed by the High Performance Group, are a bold attempt to provide a fair and objective benchmarking tool designed for stateof-the-art HPC systems. With the support of multiple host and accelerator programming models, the suites are portable across both homogeneous and heterogeneous architectures. Different workloads are developed to fit system sizes ranging from a few compute nodes to a few hundred compute nodes. In this work we present our first experiences in performance benchmarking the new SPEChpc2021 suites and evaluate their portability and basic performance characteristics on various popular and emerging HPC architectures, including x86 CPU, NVIDIA GPU, and AMD GPU. This study provides a first-hand experience of executing the SPEChpc 2021 suites at scale on production HPC systems, discusses real-world use cases, and serves as an initial guideline for using the benchmark suites. 

The SPEChpc™ 2021 suites are application-based benchmarks designed to measure performance of modern HPC systems. The benchmarks support MPI, MPI+OpenMP, MPI+OpenMP target offload, MPI+OpenACC and are portable across all major HPC platforms. 

In recent years the possibility of relaxing the so- called Faithfulness assumption in automated causal discovery has been investigated. The investiga- tion showed (1) that the Faithfulness assumption can be weakened in various ways that in an impor- tant sense preserve its power, and (2) that weak- ening of Faithfulness may help to speed up meth- ods based on Answer Set Programming. However, this line of work has so far only considered the dis- covery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.