Wei Gao

An experiment has shown that ∼10 nm Si nanoparticles undergo a Si–I (diamond cubic) to Si–V (simple hexagonal) phase transition under compression, in contrast to the Si–I to Si–II (tetragonal) transition observed in bulk silicon. However, the atomistic mechanism underlying this size-dependent transition pathway remains unclear. Here, we employ molecular dynamics simulations with a machine learning interatomic potential to reveal a stress triaxiality-driven, two-step Si–I to Si–V transition pathway in a spherical Si nanoparticle subjected to an idealized triaxial contact loading model. An intermediate amorphous phase nucleates at the nanoparticle surface and propagates inward around the Si–I core in regions of low stress triaxiality dominated by shear. Within this amorphous shell, Si–V recrystallizes at locations with increased stress triaxiality and hydrostatic pressure. Upon unloading, the Si–V structure transforms into an amorphous state. A subsequent loading–unloading cycle applied to this amorphous nanoparticle reveals a reversible amorphous-to-Si–V transformation, demonstrating a nanoscale phase memory effect.

Molecular dynamics simulations using a Gaussian Approximation Potential (GAP) reveal a stress triaxiality driven, two-step Si-I (diamond cubic) to Si-V (simple hexagonal) phase transition pathway in a spherical Si nanoparticle with a 10 nm diameter under triaxial compression. A transient amorphous phase first forms at the surface and propagates inward around Si-I core, where stress triaxiality is low (shear-dominated). Within the amorphous shell, the material recrystallizes into Si-V at locations of elevated stress triaxiality and hydrostatic pressure. The resulting Si-V structure transforms into a fully amorphous state upon unloading. A subsequent loading-unloading cycle applied to this amorphous nanoparticle reveals a reversible amorphous to Si-V transformation, demonstrating a nanoscale phase memory effect.

Two‐photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP‐fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate‐based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross‐sectional areas ranging from 1 to 36 μm². Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

We investigate the kinetics of phase transition between the 2H and 1T$^\prime$ phases in monolayer MoTe$_2$ using atomistic simulations based on a machine learning interatomic potential trained on SCAN-DFT data, combined with mean field kinetic theory to interpret the underlying mechanisms. The transition is found to involve both diffusive and diffusionless mechanisms. Nucleation of 1T$^\prime$ phase is initiated by the coalescence of neighboring Te monovacancies into divacancies, which are found to be mobile and can interact with other Te vacancies to form small triangular 1T$^\prime$ islands. Growth of these islands proceeds either by incorporating pre-existing vacancies at the phase boundaries or, in their absence, by absorbing divacancies that migrate from the surrounding lattice. Once a critical island size is reached, vacancy-free growth becomes possible although with a higher activation barrier. Upon removal of external stimuli, the system reverts to 2H phase, during which Te vacancies reorganize into three-fold spoke-like vacancy lines at the island center. This reverse process and the subsequent 1T$^\prime$$\leftrightarrow$2H reversible transitions are diffusionless, rapid, do not require additional vacancies and can be driven by mild external stimuli. Although our analysis focuses on strain-induced transitions, the kinetic mechanisms are expected to be generalizable to other types of stimuli.

In many biological materials such as nacre and bone, the material structure consists of hard grains and soft interfaces, with the interfaces playing a significant role in the material's mechanical behavior. This type of structures has been utilized in the design of various bio-inspired composite materials. Such applications often require the materials to exhibit a specified nonlinear stress-strain relationship. A key challenge lies in identifying appropriate interface properties from an infinite search space to achieve a given target stress-strain curve. This study introduces a Bayesian optimization (BO) framework specifically tailored for the inverse design of interfaces in bio-inspired composites. As a notable advantage, this method is capable of expanding the design space, allowing the discovery of optimal solutions even when the target curve deviates significantly from the initial dataset. Furthermore, our results show that BO can identify distinct interface designs that produce similar target stress-strain responses, yet differ in their deformation and failure mechanisms. These findings highlight the potential of the proposed BO framework to address a wide range of inverse design challenges in nonlinear mechanics problems.

Molecular dynamics simulations are utilized to unravel the temperature-driven phase transition in double-layered butylammonium (BA) methylammonium (MA) lead halide perovskite (BA)(2)(MA)Pb2I7, which holds great promise for a wide range of optoelectronics and sensor applications. The simulations successfully capture the structural transition from low to high symmetry phases with rising temperatures, consistent with experimental observations. The phase transition is initiated at two critical interfaces: the first is between the inorganic and organic layers, where the melting of N-H bonds in BA leads to a significant reduction in hydrogen bonding between BA and iodides, and the second is at the interface between the top and bottom organic layers, where the melting of the tail bonds in BA triggers the phase transition. Following this, BA cations exhibit a patterned and synchronized motion reminiscent of a conical pendulum, displaying a mix of ordered and disordered behaviors prior to evolving into a completely molten and disordered state. While the melting of BA cations is the primary driver of the phase transition, the rotational dynamics of MA cations also plays a critical role in determining the phase transition temperature, influenced by the BA-MA interaction. Such an interaction alters the polarization patterns of MA cations across the phase transition. In particular, an antiparallel polarization pattern is observed in the low-temperature phase. Additionally, displacive elements of the phase transition are identified in the simulations, characterized by the shear and distortion of the inorganic octahedra. Notably, at lower temperatures, the octahedral distortion follows a bimodal distribution, reflecting significant variations in distortion among octahedra. This variation is attributed to an anisotropic hydrogen bonding network between iodides and BA cations. Our study reveals the phenomena and mechanisms extending beyond the order-disorder transition mechanism, suggesting potential phase engineering through strategic tuning of organic and inorganic components.

Molecular dynamics simulations are utilized to unravel the temperature-driven phase transition in double-layered butylammonium (BA) methylammonium (MA) lead halide perovskite (BA)$_{2}$(MA)Pb$_{2}$I$_{7}$, which holds great promise in a wide range of optoelectronics and sensor applications. The simulations successfully capture the structural transition from low to high symmetry phases with rising temperatures, consistent with experimental observations. The phase transition initiates at two critical interfaces: the first is between the inorganic and organic layers, where the melting of N-H bonds in BA leads to significant reduction in hydrogen bonding between BA and iodides, and the second is at the interface between top and bottom organic layers, where the melting of the tail bonds in BA triggers the phase transition. Following this, BA cations exhibit a patterned and synchronized motion reminiscent of a conical pendulum, displaying a mix of ordered and disordered behaviors, prior to evolving into a completely molten and disordered state. While the melting of BA cations is the primary driver of the phase transition, the rotational dynamics of MA cations also plays a critical role in determining the phase transition temperature, influenced by the BA-MA interaction. Such interaction alters the polarization patterns of MA cations across the phase transition. In particular, an anti-parallel polarization pattern is observed in low temperature phase. Additionally, displacive elements of the phase transition are identified in the simulations, characterized by the shear and distortion of the inorganic octahedra. Notably, at lower temperatures, the octahedral distortion follows a bimodal distribution, reflecting significant variations in distortion among octahedra. This variation is attributed to an anisotropic hydrogen bonding network between iodides and BA cations. Our study reveals new phenomena and mechanisms extending beyond the order-disorder transition mechanism, shedding new light on potential phase engineering through strategic tuning of organic and inorganic components.