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Stefano de Gironcoli

Publications and source records attributed to Stefano de Gironcoli.

At least 19 recordsLinked to original sources
arXiv2026

Melting Behavior and Phase Stability of CaO from Neural Network Potentials: a Molecular Dynamics Study

We investigate the melting behavior of calcium oxide (CaO) under extreme conditions, a problem that remains poorly constrained due to experimental limitations despite its relevance for geophysical and technological applications. We develop a Machine Learning Interatomic Potential (MLIP) for CaO with PANNA 2.0 and the LATTE descriptor, training it on a dataset of $\sim$12,000 configurations including solid, liquid, interfacial, and void-containing structures, extracted from ab-initio molecular dynamics data employing PBEsol exchange-correlation functional. We perform large-scale molecular dynamics simulations to compute the melting temperature at ambient pressure using both the void-nucleated melting (VNM) and two-phase coexistence (TPC) methods, obtaining $T_m=3055\pm11$ K and $T_m=2847\pm15$ K, respectively.\\ We calculate an enthalpy of fusion of $ΔH_f\sim73$ kJ/mol, in agreement with thermodynamic assessments and ab initio calculations. We also reproduce the thermal expansion and obtain a volume increase of $\sim$29% at Tm, consistent with the corresponding decrease in density extracted from spatially resolved number density profiles. Finally, we calculate the high-pressure melting curve of CaO up to 20 GPa, providing one of the very few computational determinations of this quantity to date. The results confirm that the overheating ratio $η$ is not constant under pressure, increasing from 17% at ambient pressure to 24% at 20 GPa, confirming previous findings and ruling out the assumption of a fixed overheating ratio. Our results establish MLIP-based simulations as a robust and efficient framework for investigating phase stability in ionic oxides and provide new insight into the melting behavior of CaO under extreme conditions.

cond-mat.mtrl-sci↗
arXiv2026

Long-range electrostatics in atomistic machine learning: a physical perspective

The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription for how long-range physical effects should be incorporated into a model while preserving the well-established locality principles underlying most transferable ML representations. Here, we provide a physical perspective on this problem by discussing how distinct contributions to a system's electrostatics can be captured through different learning paradigms. Specifically, we distinguish between local charge models, which rely either on explicit charge-density decompositions or implicit auxiliary variables, and models in which a notion of nonlocality is deliberately introduced, either via self-consistent procedures or by using nonlocal descriptors and learning architectures. We further address the related aspect of incorporating finite-field effects through coupling to the system's polarization, relevant for applications involving an external electric bias. We conclude by discussing implications for simulations of electrochemical interfaces, where long-range electrostatics are essential to capture the interplay between charge redistribution, interfacial dynamics, and ionic screening, as well as for ionic transport phenomena, which, although less explored, appear far less sensitive to their inclusion.

cond-mat.mtrl-sci↗
arXiv2025

An accurate alternative to hybrid functionals for germanium: DFT+$α$

The accuracy of bulk property predictions in density functional theory (DFT) calculations depends on the choice of exchange-correlation functional. While the Perdew-Burke-Ernzerhof (PBE) functional systematically overestimates lattice parameters and strongly underestimates electronic band gaps, hybrid functionals such as Heyd-Scuseria-Ernzerhof (HSE) offer better overall agreement across a broad range of materials. Using germanium as a critical test case, we challenge the ability of both functionals to capture semiconductor properties. Although HSE improves PBE's gap error, it fails to reproduce germanium's correct $Γ$-L indirect and $Γ$-$Γ$ band gaps simultaneously. Noting that the PBE underestimated energy separation between the 4p valence-band maximum and 4s conduction-band minimum causes unphysical $sp$ mixing, we propose DFT+$α$, a semi-empirical correction scheme applied selectively to 4s-like orbitals. For germanium, DFT+$α$ restores the proper ordering and orbital character of the band edges and yields accurate lattice constant, bulk modulus, elastic constants and phonon frequencies at a fraction of hybrid-functional computational cost.

cond-mat.mtrl-sci↗
arXiv2025

1D Transition Metal Oxide Chains as a Challenging Model for Ab Initio Calculations

Providing highly simplified models of strongly correlated electronic systems that challenge {\it ab initio} calculations can serve as a valuable testing ground to improve these methods. In this study, we present a comprehensive study of the structural, magnetic, and electronic properties of one-dimensional transition metal mono-oxide chains (VO, CrO, MnO, FeO, CoO, and NiO) using density functional theory (DFT), DFT+$U$, and coupled-cluster singles and doubles (CCSD) calculations. The Hubbard $U$ parameter for DFT+$U$ is determined using the linear response theory. In all systems studied except MnO, the presence of multiple local minima -- primarily due to the electronic degrees of freedom associated with the d-orbitals -- leads to significant challenges for DFT, DFT+U, and Hartree-Fock methods in finding the global minimum in ab initio calculations. Our results indicate that the antiferromagnetic (AFM) state is energetically favored for all chains, except CrO, when using DFT+$U$ and PBE. We analyze the electronic band structures and find that while the PBE approximation often predicts metallic or half-metallic ground states for the ferromagnetic (FM) state, DFT+$U$ approach successfully opens band gaps, correctly predicting insulating behavior in all cases. Furthermore, we compared the energy differences between the AFM and FM states using DFT+$U$ and CCSD for CrO, MnO, FeO, CoO and NiO. Our findings indicate that CCSD predicts larger energy differences in some cases compared to DFT+$U$, suggesting that the Hubbard $U$ parameter obtained through linear response theory may be overestimated when used to calculate energy differences between different magnetic states. For CrO, CCSD predicts an AFM ground state, in contrast to the predictions from DFT+$U$ and PBE methods.

cond-mat.str-el↗
arXiv2025

Convergence of Body-Orders in Linear Atomic Cluster Expansions

We study the convergence of a linear atomic cluster expansion (ACE) potential with respect to its basis functions, in terms of the effective two-body interactions of elemental Carbon and Silicon systems. We build ACE potentials with descriptor sets truncated at body-orders $K=2$ to $K=5$ trained on a diverse Carbon dataset and on Silicon dimers to pentamers. The potentials trained on diverse structures with standard ACE bases are not able to recover the correct dimer curves much less produce stable curves or solutions. While employing ACE bases removed of self-interactions still does not generalize to the DFT-expected results, properly tailored datasets and basis sets are able to show signs of convergence and stability in the curves and expansions, suggesting a method to build potentials with interpretable bases with respect to the cluster expansion.

physics.comp-ph↗
arXiv2024

Discovery of Novel Silicon Allotropes with Optimized Band Gaps to Enhance Solar Cell Efficiency through Evolutionary Algorithms and Machine Learning

In the pursuit of advancing solar energy technologies, this study presents 20 direct and quasi-direct band gap silicon crystalline semiconductors that satisfy the Shockley-Queisser limit, a benchmark for solar cell efficiency. Employing two evolutionary algorithm-based searches, we optimize structures and calculate fitness function using the DFTB method and Gaussian approximation potential. Following the preselection of structures based on energy considerations, we further optimize them using PBEsol DFT. Subsequently, we screen the structures based on their band gap, employing a DFTB method tailored for band gap calculation of silicon crystals. To ensure accurate band gap determination, we employ HSE and GW methods. To validate the structural stability, we employ phonon analysis via linear regression algorithm applied to PBEsol DFT data. Significantly, the structures unveiled in this study are of great importance due to their proven stability from both mechanical and dynamic perspectives. Furthermore, the ductility and low density of certain structures enhance their potential application. We examine the optical properties by studying the imaginary part of the dielectric function by solving the Bethe-Salpeter Equation on top of GW approximation. By calculating the SLME, we achieve an efficiency of 32.7% for Si$_{22}$ at a thickness of 500 nm. Moreover, the study harnesses various machine learning algorithms to develop a predictive model for the band gap energy of these silicon structures. Input data for machine learning models are derived from structural MBTR and SOAP descriptors, as well as DFT outputs. Notably, the results reveal that features extracted from DFT outperform the MBTR and SOAP descriptors.

cond-mat.mtrl-sci↗
arXiv2024

LATTE: an atomic environment descriptor based on Cartesian tensor contractions

We propose a new descriptor for local atomic environments, to be used in combination with machine learning models for the construction of interatomic potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the efficient construction of a variable number of many-body terms with learnable parameters, resulting in a descriptor that is efficient, expressive, and can be scaled to suit different accuracy and computational cost requirements. We compare this new descriptor to existing ones on several systems, showing it to be competitive with very fast potentials at one end of the spectrum, and extensible to an accuracy close to the state of the art.

physics.comp-ph↗
arXiv2022

Growing borophene on metal substrates: a theoretical study of the role of oxygen on Al(111)

Charge transfer from a metal substrate stabilizes honeycomb borophene, whose electron deficit would otherwise spoil the hexagonal order of a $π$-bonded 2D atomic network. However, the coupling between the substrate and the boron overlayer may result in the formation of strong chemical bonds that would compromise the electronic properties of the overlayer. In this paper we present a theoretical study, based on state-of-the-art density-functional and genetic-optimization techniques, of the electronic and structural properties of borophene grown on Al(111), with emphasis on the impact of oxygen on the strength of the coupling between substrate and overlayer. While our results confirm the formation of Al-B bonds, they also predict that oxygen doping reduces charge transfer between aluminum and borophene, thus allowing modulation of their strength and paving the way to engineering the electronic properties of 2D-supported borophene sheets for industrial applications. Our study is completed by a thorough study of the thermodynamic stability of the oxygenated borophene-Al(111) interface.

cond-mat.mtrl-sci↗
arXiv2022

Data-driven simulation and characterisation of gold nanoparticle melting

The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

physics.comp-ph↗
arXiv2022

Mathematical Analysis and Numerical Approximations of Density Functional Theory Models for Metallic Systems

In this paper, we investigate the energy minimization model of the ensemble Kohn-Sham density functional theory for metallic systems, in which a pseudo-eigenvalue matrix and a general smearing approach are involved. We study the invariance and the existence of the minimizer of the energy functional. We propose an adaptive double step size strategy and the corresponding preconditioned conjugate gradient methods for solving the energy minimization model. Under some mild but reasonable assumptions, we prove the global convergence of our algorithms. Numerical experiments show that our algorithms are efficient, especially for large scale metallic systems. In particular, our algorithms produce convergent numerical approximations for some metallic systems, for which the traditional self-consistent field iterations fail to converge.

math.NA↗
arXiv2021

Quantum ESPRESSO toward the exascale

Quantum ESPRESSO is an open-source distribution of computer codes for quantum-mechanical materials modeling, based on density-functional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware architectures, from laptops to massively parallel computers, as well as for the breadth of its applications. In this paper we present a motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently hindering the way towards exascale computing.

physics.comp-ph↗
arXiv2020

A Systematic Approach to Generating Accurate Neural Network Potentials: the Case of Carbon

Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approaches for generating potentials are promising; however neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for Carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

cond-mat.mtrl-sci↗
arXiv2020

The CECAM Electronic Structure Library and the modular software development paradigm

First-principles electronic structure calculations are very widely used thanks to the many successful software packages available. Their traditional coding paradigm is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, from the compiler up, with the exception of linear-algebra and message-passing libraries. This model has been quite successful for decades. The rapid progress in methodology, however, has resulted in an ever increasing complexity of those programs, which implies a growing amount of replication in coding and in the recurrent re-engineering needed to adapt to evolving hardware architecture. The Electronic Structure Library (\esl) was initiated by CECAM (European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure programs and redesign them as free, open-source libraries. They include "heavy-duty" ones with a high degree of parallelisation, and potential for adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and re-engineering from the computational science done by scientists when implementing new ideas. It is a community effort, undertaken by developers of various successful codes, now facing the challenges arising in the new model. This modular paradigm will improve overall coding efficiency and enable specialists (computer scientists or computational scientists) to use their skills more effectively. It will lead to a more sustainable and dynamic evolution of software as well as lower barriers to entry for new developers.

cond-mat.mtrl-sci↗
arXiv2020

Hubbard-corrected density functional perturbation theory with ultrasoft pseudopotentials

We present in full detail a newly developed formalism enabling density functional perturbation theory (DFPT) calculations from a DFT+$U$ ground state. The implementation includes ultrasoft pseudopotentials and is valid for both insulating and metallic systems. It aims at fully exploiting the versatility of DFPT combined with the low-cost DFT+$U$ functional. This allows to avoid computationally intensive frozen-phonon calculations when DFT+$U$ is used to eliminate the residual electronic self-interaction from approximate functionals and to capture the localization of valence electrons e.g. on $d$ or $f$ states. In this way, the effects of electronic localization (possibly due to correlations) are consistently taken into account in the calculation of specific phonon modes, Born effective charges, dielectric tensors and in quantities requiring well converged sums over many phonon frequencies, as phonon density of states and free energies. The new computational tool is applied to two representative systems, namely CoO, a prototypical transition metal monoxide and LiCoO$_2$, a material employed for the cathode of Li-ion batteries. The results show the effectiveness of our formalism to capture in a quantitatively reliable way the vibrational properties of systems with localized valence electrons.

cond-mat.str-el↗
arXiv2018

Beyond the random phase approximation with a local exchange vertex

With the aim of constructing an electronic structure approach that systematically goes beyond the GW and random phase approximation (RPA) we introduce a vertex correction based on the exact-exchange (EXX) potential of time-dependent density functional theory. The EXX vertex function is constrained to be local but is expected to capture similar physics as the Hartree-Fock vertex. With the EXX vertex we then consistently unify different beyond-RPA approaches such as the various re-summations of RPA with exchange (RPAx) and the second order screened exchange (SOSEX) approximation. The theoretical analysis is supported by numerical studies on the hydrogen dimer and the electron gas, and we discuss the importance of including the vertex correction in both the screened interaction and the self energy. Finally, we give details on our implementation within the plane-wave pseudo potential framework and demonstrate the excellent performance of the different RPAx methods in describing the energetics of hydrogen and van der Waals bonds.

cond-mat.mtrl-sci↗
arXiv2017

A parallel orbital-updating based plane-wave basis method for electronic structure calculations

Motivated by the recently proposed parallel orbital-updating approach in real space method, we propose a parallel orbital-updating based plane-wave basis method for electronic structure calculations, for solving the corresponding eigenvalue problems. In addition, we propose two new modified parallel orbital-updating methods. Compared to the traditional plane-wave methods, our methods allow for two-level parallelization, which is particularly interesting for large scale parallelization. Numerical experiments show that these new methods are more reliable and efficient for large scale calculations on modern supercomputers

physics.comp-ph↗
arXiv2016

Isobaric first-principles molecular dynamics of liquid water with nonlocal van der Waals interactions

We investigate the structural properties of liquid water at near ambient conditions using first-principles molecular dynamics simulations based on a semilocal density functional augmented with nonlocal van der Waals interactions. The adopted scheme offers the advantage of simulating liquid water at essentially the same computational cost of standard semilocal functionals. Applied to the water dimer and to ice Ih, we find that the hydrogen-bond energy is only slightly enhanced compared to a standard semilocal functional. We simulate liquid water through molecular dynamics in the NpH statistical ensemble allowing for fluctuations of the system density. The structure of the liquid departs from that found with a semilocal functional leading to more compact structural arrangements. This indicates that the directionality of the hydrogen-bond interaction has a diminished role as compared to the overall attractions, as expected when dispersion interactions are accounted for. This is substantiated through a detailed analysis comprising the study of the partial radial distribution functions, various local order indices, the hydrogen-bond network, and the selfdiffusion coefficient. The explicit treatment of the van der Waals interactions leads to an overall improved description of liquid water.

cond-mat.soft↗
arXiv2016

Crystal Structure Prediction of Molecular Crystals from First Principles: Are we there yet?

Accurate molecular crystal structure prediction is a fundamental goal in academic and industrial condensed matter research and polymorphism is arguably the biggest obstacle on the way. We tackle this challenge in the difficult case of the repeatedly studied, abundantly used aminoacid Glycine that hosts still little-known phase transitions and we illustrate the current state of the field through this example. We demonstrate that the combination of recent progress in structure search algorithms with the latest advances in the description of van der Waals interactions in Density Functional Theory, supported by data-mining analysis, enables a leap in predictive power: we resolve, without prior empirical input, all known phases of glycine, as well as the structure of the previously unresolved $ζ$ phase after a decade of its experimental observation [Boldyreva et al. \textit{Z. Kristallogr.} \textbf{2005,} \textit{220,} 50-57]. The search for the well-established $α$ phase instead reveals the remaining challenges in exploring a polymorphic landscape.

cond-mat.mtrl-sci↗