arXiv subjects

Daniele Rapetti

Publications and source records attributed to Daniele Rapetti.

5 recordsLinked to original sources

arXiv2025

Making PLUMED fly: a tutorial on optimizing performance

PLUMED is an open-source software package that is widely used for analyzing and enhancing molecular dynamics simulations that works in conjunction with most available molecular dynamics softwares. While the computational cost of PLUMED calculations is typically negligible compared to the molecular dynamics code's force evaluation, the software is increasingly being employed for more computationally demanding tasks where performance optimization becomes critical. In this tutorial, we describe a recently implemented tool that can be used to reliably measure code performance. We then use this tool to generate detailed performance benchmarks that show how calculations of large-numbers of distances, angles or torsions can be optimized by using vector-based commands rather than individual scalar operations. We then present benchmarks that illustrate how to optimize calculations of atomic order parameters and secondary structure variables. Throughout the tutorial and in our implementations we endeavor to explain the algorithmic tricks that are being used to optimize the calculations so others can make use of these prescriptions both when they are using PLUMED and when they are writing their own codes.

physics.comp-ph↗

arXiv2025

dynsight: an Open Python Platform for Simulation and Experimental Trajectory Data Analysis

The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as, (i) identifying and tracking the constitutive objects/particles, resolving their trajectories (e.g., in experimental cases, where these are not automatically available as in typical molecular simulations), (ii) translating the trajectories into data that are easier to handle/analyze by using well suited descriptors, and (iii) extracting meaningful information from such data. Each of these different tasks often requires non-negligible programming skills, the use of various types of representations or methods, and the availability/development of an interface between them. Despite the considerable potential that new tools contributed to each of these individual steps, their integration under a common framework would decrease the barrier to usage (especially by diverse communities of users), avoid fragmentation, and ultimately facilitate the development of new approaches in data analysis. To this end, here we introduce dynsight, an open Python platform that streamlines the extraction and analysis of time-series data from simulation- or experimentally-resolved trajectories. dynsight simplifies workflows, enhances accessibility, and facilitates time-series and trajectories data analysis offering a useful tool to unraveling the dynamic complexity of a variety of systems (or signals) across different scales. dynsight is open source (https://github.com/GMPavanLab/dynsight) and can be easily installed using pip.

cond-mat.mtrl-sci↗

arXiv2024

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

physics.ed-ph↗

arXiv2023

Innate Dynamics and Identity Crisis of a Metal Surface Unveiled by Machine Learning of Atomic Environments

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well-below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is thus important for many applications but is not easy. Here we report deep-potential molecular dynamics simulations allowing to resolve at atomic-resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ($\sim1/3$ of melting) temperature. The development of a deep neural network potential trained on DFT calculations provides a dynamically-accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervised machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are instead typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else in relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key for understanding their behaviors and properties.

cond-mat.mtrl-sci↗

arXiv2023

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time-variations of SOAP spectra

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena, and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progresses in the development of advanced high-dimensional structural descriptors (e.g., Smooth Overlap of Atomic Position (SOAP), etc.) have certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements remain still difficult to elucidate. Here, exploiting the structural-rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a time-dependent SOAP-based descriptor, TimeSOAP (tSOAP), which essentially tracks the time variations in the local SOAP environments surrounding each molecule (i.e., each SOAP center) in complex molecular systems along ensemble trajectories. We demonstrate how analysis of the time-series {tSOAP data and of their time-derivatives allows detecting dynamics domains and tracking instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect will help to shed light on a variety of complex dynamical phenomena.

physics.chem-ph↗