arXiv subjects

Riccardo Capelli

Publications and source records attributed to Riccardo Capelli.

8 recordsLinked to original sources

arXiv2026

Derivation of the Sample-Size Scaling of TWO-NN Intrinsic-Dimension Estimates from Molecular Dynamics Trajectories

The intrinsic dimension of a dataset is the number of independent directions needed to describe the space occupied by its data. Estimators based on nearest neighbors infer this number from how the probability to find a neighbor point grows around each sampled point. Because the distances $r$ between neighbor points decrease as the sample grows, the estimated dimension can depend strongly on the number of available points. Here, we derive the large-sample behavior of the TWO-NN estimator for data drawn from a smooth $d$-dimensional space. The typical nearest-neighbor distance scales as $N^{-1/d}$, and smooth deviations from a locally uniform distribution produce successive corrections proportional to $N^{-2/d}$. We test this result using the trajectories coming from ten independent $100~μ$s simulations of alanine dipeptide. Configurations are represented by all pairwise distances among the ten heavy atoms. This representation has a known geometric dimension of $3n_{\mathrm{at}}-6=24$. Over the investigated range, the TWO-NN estimate shows no systematic dependence on the temporal spacing between configurations, but increases from approximately $7.5$ to $15.6$ as the sample size grows from $10^2$ to $2\times10^5$. Extrapolations that retain corrections through $r^2$, $r^4$, and $r^6$ give limiting dimensions of $25.23$, $22.89$, and $27.00$, respectively. All three estimates lie close to the known dimension and collectively bracket it, supporting the proposed scaling. Their spread provides a direct estimate of the systematic uncertainty associated with the truncation. The derived scaling therefore explains the strong sample-size dependence of TWO-NN and provides a practical route from finite sample estimates to the underlying geometric dimension.

physics.chem-ph↗

arXiv2025

Kinetic rates calculation via non-equilibrium dynamics

This study introduces a novel computational approach based on ratchet-and-pawl molecular dynamics (rMD) for accurately estimating ligand dissociation kinetics in protein-ligand complexes. By integrating Kramers' theory with Bell's equation, our method systematically investigates the relationship between the effective biasing force applied during simulations and the ligand residence times. The proposed technique is demonstrated through extensive simulations of the benzamidine-trypsin complex, employing first an implicit solvent model (multi-eGO) to set up the approach parameters and thus an explicit solvent model. Our results illustrate the method's reliability, accuracy, and computational efficiency, with calculated kinetic rates closely matching experimental values. Overall, this study highlights rMD as a versatile and efficient non-equilibrium methodology, broadly applicable to kinetic analyses in chemical and biological systems.

physics.chem-ph↗

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↗

arXiv2022

Ephemeral ice-like local environments in classical rigid models of liquid water

Despite great efforts over the past 50 years, the simulation of water still presents significant challenges and open questions. At room temperature and pressure, the collective molecular interactions and dynamics of water molecules may form local structural arrangements that are non-trivial to classify. Here we employ a data-driven approach built on Smooth Overlap of Atomic Position (SOAP) that allow us to compare and classify how widely used classical models represent liquid water. Macroscopically, the obtained results are rationalized based on water thermodynamic observables. Microscopically, we directly observed how transient ice-like ordered environments may dynamically/statistically form in liquid water, even above the freezing temperature, by comparing the SOAP spectra for different ice structures with those of the simulated liquid systems. This confirms recent ab initio-based calculations, but also reveals how the emergence of ephemeral local ice-like environments in liquid water at room conditions can be captured by classical water models.

physics.chem-ph↗

arXiv2022

Automatic Multi-Objective Optimization of Coarse-Grained Lipid Force Fields Using SwarmCG

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behaving correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. But the parameters obtained via such automatic methods often make use of specifically-designed interaction potentials, and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here we use lipids, for which we can avail of reference experimental data (area per lipid, bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights on the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs, as well as to improve existing ones.

cond-mat.soft↗

arXiv2019

Exhaustive Search of Ligand Binding Pathways via Volume-based Metadynamics

Determining the complete set of ligands' binding/unbinding pathways is important for drug discovery and to rationally interpret mutation data. Here we have developed a metadynamics-based technique that addressed this issue and allows estimating affinities in the presence of multiple escape pathways. Our approach is shown on a Lysozyme T4 variant in complex with the benzene molecule. The calculated binding free energy is in agreement with experimental data. Remarkably, not only we were able to find all the previously identified ligand binding pathways, but also we uncovered 3 new ones. This results were obtained at a small computational cost, making this approach valuable for practical applications, such as screening of small compounds libraries.

physics.chem-ph↗

arXiv2018

Exact value for the average optimal cost of bipartite traveling-salesman and 2-factor problems in two dimensions

We show that the average cost for the traveling-salesman problem in two dimensions, which is the archetypal problem in combinatorial optimization, in the bipartite case, is simply related to the average cost of the assignment problem with the same Euclidean, increasing, convex weights. In this way we extend a result already known in one dimension where exact solutions are avalaible. The recently determined average cost for the assignment when the cost function is the square of the distance between the points provides therefore an exact prediction $$\overline{E_N} = \frac{1}π\, \log N$$ for large number of points $2N$. As a byproduct of our analysis also the loop covering problem has the same optimal average cost. We also explain why this result cannot be extended at higher dimensions. We numerically check the exact predictions.

cond-mat.dis-nn↗

arXiv2018

An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations

Inferential methods can be used to integrate experimental informations and molecular simulations. The maximum entropy principle provides a framework for using equilibrium experimental data and it has been shown that replica-averaged simulations, restrained using a static potential, are a practical and powerful implementation of such principle. Here we show that replica-averaged simulations restrained using a time-dependent potential are equivalent to the principle of maximum caliber, the dynamic version of the principle of maximum entropy, and thus may allow to integrate time-resolved data in molecular dynamics simulations. We provide an analytical proof of the equivalence as well as a computational validation making use of simple models and synthetic data. Some limitations and possible solutions are also discussed.

q-bio.BM↗