arXiv subjects

Pedro A. Santos-Florez

Publications and source records attributed to Pedro A. Santos-Florez.

4 recordsLinked to original sources

arXiv2025

Atomistic Modeling of Martensitic Phase Transition in Hexamethylbenzene

Materials exhibiting a martensitic phase transition are essential for applications in shape memory alloys, actuators and sensors. Hexamethylbenzene (HMB) has long been considered as a classical example of ferroelastic organic crystals since Mnyukh's pioneering work in 1970s. However, the atomistic mechanism underlying this phase transition has never been clarified. In this work, we present a direct molecular dynamics simulation to investigate the phase transition mechanism in HMB. For the first time, we report a simulation results that can accurately reproduce both the transition temperature and hysteresis loop observed in previous experimental studies. By analyzing the MD trajectories, the potential energy surface, we identified that a low-barrier atomic sliding mode along the close-packed (11$\overline{1}$) plane of the low-temperature phase is the key to trigger the phase transition at the critical temperature window. This is further confirmed by the observed continuous softening of shear modulus around the transition window. Our results demonstrate that the integration of various atomistic modeling techniques can provide invaluable insight into the martensitic phase transition mechanisms in organic crystals and guide the development of new organic martensites.

cond-mat.mtrl-sci↗

arXiv2023

Bending Deformation Driven by Molecular Rotation

In recent years, certain molecular crystals have been reported to possess surprising flexibility by undergoing significant elastic or plastic deformation in response to mechanical loads. However, despite this experimental evidence, there currently exists no atomistic mechanism to explain the physical origin of this phenomenon from numerical simulations. In this study, we investigate the mechanical behavior of three naphthalene diimide derivatives, which serve as representative examples, using direct molecular dynamics simulations. Our simulation trajectory analysis suggests that molecular rotational freedom is the key factor in determining a crystal's mechanical response, ranging from brittle fracture to elastic or plastic deformation under mechanical bending. Additionally, we propose a rotation-dependent potential energy surface as a means to classify organic materials' mechanical responses and identify new candidates for future investigation.

cond-mat.mtrl-sci↗

arXiv2022

Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential

We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is applied to elucidate the SRO effects on the elasticity, vibrational modes, plasticity, and strength in the NbMoTaW multi-principal element alloy (MPEA). The results show the strong attraction among Mo-Ta pairs forming the local ordered B2 structures, which could be tuned via temperature and improved by Nb content. SRO increases the elastic constants and high-frequency phonon modes as well as introduces extra lattice friction of dislocation motion. This approach enables a rapid compositional screening, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization.

cond-mat.mtrl-sci↗

arXiv2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this work, we present an efficient framework that combines machine learning potential (MLP) and metadynamics to explore multi-dimensional free energy surfaces for investigating solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we have developed a computationally scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying the framework to the metadynamics simulation of B4-B1 phase transition of GaN under 50 GPa with different model sizes, we observe the sequential change of phase transition mechanism from collective modes to nucleation and growths. When the system size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nucleation tends to occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of atomistic mechanism manifests the importance of statistical sampling with large system size. The combination of MLP and metadynamics is likely to be applicable to a broad class of induced reconstructive phase transitions at extreme conditions.

cond-mat.mtrl-sci↗