At this colloquium, we are happy to welcome:
Thomas Kühne (Helmholtz-Zentrum Dresden-Rossendorf)
The sub matrix method within electronic-structure theory and approximate computing
We present the submatrix method and a novel linear-scaling electronic-structure method in conjunction with approximate computing, as well as the implementation of the technique in CP2K. Even though initially proposed for inverse p-th roots, it has recently been recognized that the submatrix method represents a general method to approximate arbitrary matrix functions such as the matrix-sign function of large sparse matrices. The Matrix-sign function is the essential workhorse of linear-scaling electronic- structure theory, and we present an intuitive chemical justification for the accuracy of the submatrix method. We will discuss the efficient implementation of the submatrix method into CP2K with a special focus on limiting communication between compute nodes. The resulting compute kernel is the sign function of a relatively small but dense matrix. Our optimized implementation with a simple diagonalization-based evaluation of the sign function of the submatrices outperforms the Newton-Schulz Sign iteration in initial results, especially for larger cutoffs of matrix elements. This observation shows that the submatrix method will be a valuable tool in the context of approximate computing.
Matej Praprotnik (National Institute of Chemistry / Kemijski inštitut)
Sub-THz acoustic excitation of protein motion
Ultrasound is widely used as a noninvasive method in therapeutic and diagnostic applications. These can be further optimized by computational approaches, as they allow for controlled testing and rational optimization of the ultrasound parameters, such as frequency and amplitude. Usually, continuum numerical methods are used to simulate ultrasound propagating through different tissue types. In contrast, ultrasound simulations using particle description are less common, as the implementation is challenging. In this talk, I will present a dissipative particle dynamics model for performing ultrasound simulations in liquid water. The effects of frequency and thermostat parameters will be discussed. The results of our particle-based ultrasound simulations show that our approach is capable of reproducing the fluctuating hydrodynamics description of ultrasound in the continuum limit. Using the developed approach, we have studied the susceptibility of the protein’s internal dynamics to mechanical stress induced by acoustic pressure waves. By analyzing the dynamic fluctuations of the protein subunits, we have demonstrated that the protein is highly susceptible to acoustic waves with frequencies corresponding to those of the internal protein vibrations. The present studies pave the way for development and optimization of a virtual ultrasound machine for large-scale biomolecular simulations.
Tristan Bereau (Heidelberg University)
Transferable coarse-grained models accelerate chemical-space exploration
Advanced statistical methods are rapidly impregnating many scientific fields, offering new perspectives on long-standing problems. In materials science, data-driven methods are already bearing fruit in various disciplines, such as hard condensed matter or inorganic chemistry, while comparatively little has happened in soft matter. I will describe how we use multiscale simulations to leverage data-driven methods in soft matter. We aim at establishing structure-property relationships for complex thermodynamic processes across the chemical space of small molecules. Akin to screening experiments, we devise a high-throughput coarse-grained simulation framework. Coarse-graining is an appealing screening strategy for two main reasons: it significantly reduces the size of chemical space and it can suggest a low-dimensional representation of the structure-property relationship. I will briefly mention a biological application of our methodology that led to the discovery of in vivo active compounds. Finally, I will present recent developments on transferable coarse-grained models from bottom-up structure-based approaches.