arXiv subjects

Vittorio Del Tatto

Publications and source records attributed to Vittorio Del Tatto.

8 recordsLinked to original sources

arXiv2026

Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics

In molecular liquids such as water, time-delayed influences between microscopic or mesoscopic variables are typically probed using time-correlation functions, which are symmetric under detailed balance and therefore blind to dynamical asymmetries. Here, we characterize water's dynamics using a causal inference metric that captures asymmetric couplings between collective variables. Analyzing equilibrium molecular dynamics simulations at ambient conditions and in the high-density liquid (HDL) regime of supercooled water, we uncover pronounced asymmetries in the couplings between orientational and translational degrees of freedom across multiple time and length scales. At room temperature, rotational modes exert a modest directional influence on translational dynamics. In contrast, in the supercooled HDL regime, translational motions emerge as the primary drivers of the dynamics, suggesting facilitation-like relaxation mechanisms characteristic of glassy systems. These results reveal a qualitative reorganization of dynamical couplings across thermodynamic conditions, implying that molecular liquids at thermal equilibrium can exhibit an emergent directionality in their fluctuation couplings. As a consequence, our analysis reveals that external perturbations acting on specific degrees of freedom can induce a stronger arrow of time in the causal relations between translational and orientational modes.

physics.chem-ph↗

arXiv2026

Linear scaling causal discovery from high-dimensional time series by dynamical community detection

Understanding which parts of a dynamical system cause each other is extremely relevant in fundamental and applied sciences. However, inferring causal links from observational data, namely without direct manipulations of the system, is still computationally challenging, especially if the data are high-dimensional. In this study we introduce a framework for constructing causal graphs from high-dimensional time series, whose computational cost scales linearly with the number of variables. The approach is based on the automatic identification of dynamical communities, groups of variables which mutually influence each other and can therefore be described as a single node in a causal graph. These communities are efficiently identified by optimizing the Information Imbalance, a statistical quantity that assigns a weight to each putative causal variable based on its information content relative to a target variable. The communities are then ordered starting from the fully autonomous ones, whose evolution is independent from all the others, to those that are progressively dependent on other communities, building in this manner a community causal graph. We demonstrate the computational efficiency and the accuracy of our approach on time-discrete and time-continuous dynamical systems including up to 80 variables.

physics.data-an↗

arXiv2026

Phase Transitions in Unsupervised Feature Selection

Identifying minimal and informative feature sets is a central challenge in data analysis, particularly when few data points are available. Here we present a theoretical analysis of an unsupervised feature selection pipeline based on the Differentiable Information Imbalance (DII). We consider the specific case of structural and physico-chemical features describing a set of proteins. We show that if one considers the features as coordinates of a (hypothetical) statistical physics model, this model undergoes a phase transition as a function of the number of retained features. For physico-chemical descriptors, this transition is between a glass-like phase when the features are few and a liquid-like phase. The glass-like phase exhibits bimodal order-parameter distributions and Binder cumulant minima. In contrast, for structural descriptors the transition is less sharp. Remarkably, for physico-chemical descriptors the critical number of features identified from the DII coincides with the saturation of downstream binary classification performance. These results provide a principled, unsupervised criterion for minimal feature sets in protein classification and reveal distinct mechanisms of criticality across different feature types.

q-bio.BM↗

arXiv2025

Towards a robust approach to infer causality in molecular systems satisfying detailed balance

The ability to distinguish between correlation and causation of variables in molecular systems remains an interesting and open area of investigation. In this work, we probe causality in a molecular system using two independent computational methods that infer the causal direction through the language of information transfer. Specifically, we demonstrate that a molecular dynamics simulation involving a single Tryptophan in liquid water displays asymmetric information transfer between specific collective variables, such as solute and solvent coordinates. Analyzing a discrete Markov-state and Langevin dynamics on a 2D free energy surface, we show that the same kind of asymmetries can emerge even in extremely simple systems, undergoing equilibrium and time-reversible dynamics. We use these model systems to rationalize the unidirectional information transfer in the molecular system in terms of asymmetries in the underlying free energy landscape and/or relaxation dynamics of the relevant coordinates. Finally, we propose a computational experiment that allows one to decide if an asymmetric information transfer between two variables corresponds to a genuine causal link.

physics.chem-ph↗

arXiv2024

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy.

cs.LG↗

arXiv2024

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

stat.ME↗

arXiv2022

A sensitivity study of VBS and diboson WW to dimension-6 EFT operators at the LHC

We present a parton-level study of electro-weak production of vector-boson pairs at the Large Hadron Collider, establishing the sensitivity to a set of dimension-six operators in the Standard Model Effective Field Theory (SMEFT). Different final states are statistically combined, and we discuss how the orthogonality and interdependence of different analyses must be considered to obtain the most stringent constraints. The main novelties of our study are the inclusion of SMEFT effects in non-resonant diagrams and in irreducible QCD backgrounds, and an exhaustive template analysis of optimal observables for each operator and process considered. We also assess for the first time the sensitivity of vector-boson-scattering searches in semileptonic final states.

hep-ph↗

arXiv2021

A Fully Anisotropic Formulation of Stochastic Cell Rescaling

Anisotropic barostats are employed to carry out Molecular Dynamics simulations where the volume is allowed to fluctuate with no constraints on the shape of the simulation cell. Most of these algorithms are based on second-order differential equations and share some common drawbacks, namely they can lead to slowly damped oscillations in the equilibration phase, and they do not allow to control efficiently the volume autocorrelation time. This work develops the anisotropic version of stochastic cell rescaling, a first-order stochastic barostat that overcomes these limits and can also be employed in the production phase, resulting in the correct physical fluctuations of the cell. The algorithm can be easily implemented in the existing codes on top of the anisotropic Berendsen barostat. The validation tests, performed on a number of crystal systems, show that the method is robust against wide variations of the input parameter, which allows an efficient control of the volume autocorrelation time.

physics.chem-ph↗