arXiv subjects

Massimo Bocus

Publications and source records attributed to Massimo Bocus.

3 recordsLinked to original sources

arXiv2026

A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids

Thermodynamic Integration (TI) is the state-of-the-art computational technique for accurate Gibbs free energy predictions of solids. Conventional TI schemes start from an NVT harmonic reference and require three successive corrections to recover the Gibbs free energy of the real crystal in the NPT ensemble. However, the NVT-to-NPT correction neglects full cell flexibility. Here, we present a rigorous (and only) two-step TI scheme that operates entirely in the NPT ensemble, eliminating the need for the approximate NVT-to-NPT step. The key methodological advancement is the novel NPT reference that explicitly accounts for full cell fluctuations. The new approach is compared with the conventional one via two complementary case studies. For ice polymorphs, having simple cell-shape distributions, the new approach reproduces conventional TI results with excellent agreement. For CsPbI3, whose black phase exhibits complex cell-shape behavior, we demonstrate that our novel method provides more accurate Gibbs free energy differences than the conventional one. Moreover, the proposed framework maintains comparable computational cost while offering a simplified workflow. Overall, the new NPT TI scheme provides rigorous and direct Gibbs free energy calculations for solids.

physics.comp-ph↗

arXiv2026

ThermoLIB -- A Python Library for Constructing and Post-Processing Free Energy Surfaces to Extract Thermodynamic and Kinetic Properties

ThermoLIB is Python/Cython library designed to be used as a post-processing tool for constructing free energy surfaces from the output of molecular simulations, transforming them between different collective variables (CVs) and extracting thermodynamic and kinetic information. ThermoLIB is available for download on GitHUB and comes with extended documentation as well as many tutorials. The implementation is based on the theory of maximum likelihood estimators and includes error bars on and full covariance matrix between all points on the free energy surface using the Fisher information matrix. The free energy surfaces can be transformed a posteriori to other collective variables, projected towards lower dimensional CV-spaces and even deprojected towards higher dimensional CV-spaces if additional information from the simulation is provided in the form of a conditional probability. Finally, one can extract usefull thermodynamic and kinetic properties such as the reaction free energy and kinetic rate constant. Error bars on the free energy surfaces are propagated throughout al these operations. We briefly illustrate the capabilities of ThermoLIB by means of some tutorials and case studies.

physics.chem-ph↗

arXiv2024

Rare Event Sampling using Smooth Basin Classification

The efficiency of atomic simulations of materials and molecules can rapidly deteriorate when large free energy barriers exist between local minima. We propose smooth basin classification, a universal method to define reaction coordinates based on the internal feature representation of a graph neural network. We achieve high data efficiency by exploiting their built-in symmetry and adopting a transfer learning strategy. We benchmark our approach on challenging chemical and physical transformations, and show that it matches and even outperforms reaction coordinates defined based on human intuition.

physics.chem-ph↗