arXiv subjects

Francesco Maresca

Publications and source records attributed to Francesco Maresca.

7 recordsLinked to original sources

arXiv2026

Atomic Cluster Expansion Potentials for Screw Dislocations in BCC Refractory Metals

Accurate atomistic modeling of screw dislocations in body-centered cubic (bcc) metals remains challenging because their plasticity is governed by a complex dislocation glide behavior due to their compact three-fold symmetric core structure and a strongly temperature-dependent flow stress induced by the large Peierls barrier. In the context of group 6 (V, Nb, Ta) and group 5 (Mo, W) refractory metals (RMs), both classical interatomic potentials and some machine learning potentials consistently fail to reproduce density functional theory (DFT) Peierls barriers and the glide plane. Here, we developed an array of atomic cluster expansion (ACE) potentials for these RMs by extending an existing DFT database. The developed ACE potentials significantly improve the description of screw dislocation properties, achieving near-DFT accuracy for Mo and W and substantial improvement for V, Nb, and Ta. The results show that transferability to screw dislocation behavior depends sensitively on both database composition and element-specific energetics, and that achieving a single-humped Peierls barrier alone is not a sufficient validation metric for accurate prediction of dislocation glide. For Nb, Mo, and W, the developed ACE models also enable reliable calculation of kink-pair activation enthalpies, which are well described by both Kocks' law and a line-tension model.

cond-mat.mtrl-sci↗

arXiv2025

To crack, or not to crack: How hydrogen favors crack propagation in iron at the atomic scale

Steel is a key structural material because of its considerable strength and ductility. However, when exposed to hydrogen, it is prone to embrittlement. Mechanistic understanding of the origin of hydrogen embrittlement is hampered by the lack of reliable interatomic potentials. Here, we perform large-scale molecular dynamics simulations of crack propagation after having developed and validated an efficient yet density-functional-theory-accurate machine-learning potential for hydrogen in iron. Simulations based on our potential reveal that in the absence of H, iron is intrinsically ductile at finite temperatures with crack-tip blunting assisted by dislocation emission. By contrast, minute (part-per-million) hydrogen concentrations can switch the crack-tip behavior from ductile blunting to brittle propagation. Detailed analysis of our molecular dynamics results reveals that the combination of fast hydrogen diffusion and diminished surface energy is at the origin of embrittlement. Our results set the stage for a modified Griffith's criterion for hydrogen-induced brittle fracture, which closely captures the simulations and that can be used to assess embrittlement in iron-based alloys.

cond-mat.mtrl-sci↗

arXiv2023

Efficiency, Accuracy, and Transferability of Machine Learning Potentials: Application to Dislocations and Cracks in Iron

Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems larger than DFT supercells are not fully explored, posing a question regarding transferability to large-scale simulations with defects (e.g. dislocations, cracks). Here, we apply a three-step validation approach to body-centered-cubic iron. First, accuracy and efficiency are assessed by optimizing ML-IAPs based on four state-of-the-art ML packages. The Pareto front of computational speed versus testing root-mean-square-error (RMSE) is computed. Second, benchmark properties relevant to plasticity and fracture are evaluated. Their average relative error Q with respect to DFT is found to correlate with RMSE. Third, transferability of ML-IAPs to dislocations and cracks is investigated by using per-atom model uncertainty quantification. The core structures and Peierls barriers of screw, M111 and three edge dislocations are compared with DFT. Traction-separation curve and critical stress intensity factor (K_Ic) are also predicted. Cleavage on the pre-existing crack plane is found to be the zero-temperature atomistic fracture mechanism of pure body-centered-cubic iron under mode-I loading, independent of ML package and training database. Quantitative predictions of dislocation glide paths and KIc can be sensitive to database, ML package, cutoff radius, and are limited by DFT accuracy. Our results highlight the importance of validating ML-IAPs by using indicators beyond RMSE. Moreover, significant computational speed-ups can be achieved by using the most efficient ML-IAP package, yet the assessment of the accuracy and transferability should be performed with care.

cond-mat.mtrl-sci↗

arXiv2022

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip deformation mechanisms under mode-I loading based on classical interatomic potentials yield contradicting predictions. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a maximum predicted error of 8 meV/atom over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the crack tip mechanism for {100} and {110} crack planes at T=0K, thus settling a long-standing dispute. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g. nanovoids, dislocations) should be taken explicitly into account.

cond-mat.mtrl-sci↗

arXiv2021

Atomistic Graph Neural Networks for metals: Application to bcc iron

The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour of metals. Density functional theory (DFT) can enable the quantum-accurate prediction of some of these properties, however at high computational costs and thus limited to systems of ~1,000 atoms. In order to predict with quantum-accuracy the mechanical behaviour of nanoscale structures involving from thousands to several millions of atoms, machine learning interatomic potentials have been recently developed. Here, we explore the performance of a new class of interatomic potentials based on Graph Neural Networks (GNNs), a recent field of research in Deep Learning. Two state-of-the-art GNN models are considered, SchNet and DimeNet, and trained on an extensive DFT database of ferromagnetic bcc iron. We find that the DimeNet GNN Fe potential including three-body terms can reproduce with DFT accuracy the equation of state and the Bain path, as well as defected configurations (vacancy and surfaces). To the best of our knowledge, this is the first demonstration of the capability of GNN of reproducing the energetics of defects in bcc iron. We provide an open-source implementation of DimeNet that can be used to train other metallic systems for further exploration of the GNN capabilities.

cond-mat.mtrl-sci↗

arXiv2020

Edge Dislocations Can Control Yield Strength in Refractory Body-Centered-Cubic High Entropy Alloys

Energy efficiency is motivating the search for new high-temperature metals. Some new body-centered-cubic random multicomponent "high entropy alloys (HEAs)" based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known. Here we show, using integrated neutron-diffraction (ND), high-resolution transmission electron microscopy (HRTEM), and theory, that the high strength and strength retention of a NbTaVTi alloy and a new high-strength/low-density CrMoNbV alloy are attributable to edge dislocations. This is surprising because plastic-flow in BCC elemental metals and dilute alloys is universally accepted to be controlled by screw dislocations. We use the insight and theory to perform a computationally-guided search over $10^7$ BCC HEAs and identify over $10^6$ possible ultra-strong high-temperature alloy compositions for future exploration.

cond-mat.mtrl-sci↗

arXiv2019

Mechanistic origin of high retained strength in refractory BCC high entropy alloys up to 1900K

The body centered cubic (BCC) high entropy alloys MoNbTaW and MoNbTaVW show exceptional strength retention up to 1900K. The mechanistic origin of the retained strength is unknown yet is crucial for finding the best alloys across the immense space of BCC HEA compositions. Experiments on Nb-Mo, Fe-Si and Ti-Zr-Nb alloys report decreased mobility of edge dislocations, motivating a theory of strengthening of edge dislocations in BCC alloys. Unlike pure BCC metals and dilute alloys that are controlled by screw dislocation motion at low temperatures, the strength of BCC HEAs can be controlled by edge dislocations, and especially at high temperatures, due to the barriers created for edge glide through the random field of solutes. A parameter-free theory for edge motion in BCC alloys qualitatively and quantitatively captures the strength versus temperature for the MoNbTaW and MoNbTaVW alloys. A reduced analytic version of the theory then enables screening over >600,000 compositions in the Mo-Nb-Ta-V-W family, identifying promising new compositions with high retained strength and/or reduced mass density. Overall, the theory reveals an unexpected mechanism responsible for high temperature strength in BCC alloys and paves the way for theory-guided design of stronger high entropy alloys.

cond-mat.mtrl-sci↗