Defect tolerance in CsPbI3: Reconstruction of potential energy landscape and band degeneracy in spin-orbit coupling

Lead halide perovskites have been intensively developed to be high-performance photovoltaic and optoelectronic materials, where the unique defect tolerance property is believed to contribute to their excellence. However, the defect...

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

Downfolding the Su-Schrieffer-Heeger model

Charge-density waves are responsible for symmetry-breaking displacements of atoms and concomitant changes in the electronic structure. Linear response theories, in particular density-functional perturbation theory, provide a way to study the effect of displacements on both the total energy and the electronic structure based on a single ab initio calculation. In downfolding approaches, the electronic system is reduced to a smaller number of bands, allowing for the incorporation of additional correlation and environmental effects on these bands. However, the physical contents of this downfolded model and its potential limitations are not always obvious. Here, we study the potential-energy landscape and electronic structure of the Su-Schrieffer-Heeger (SSH) model, where all relevant quantities can be evaluated analytically. We compare the exact results at arbitrary displacement with diagrammatic perturbation theory both in the full model and in a downfolded effective single-band model, which gives an instructive insight into the properties of downfolding. An exact reconstruction of the potential-energy landscape is possible in a downfolded model, which requires a dynamical electron-biphonon interaction. The dispersion of the bands upon atomic displacement is also found correctly, where the downfolded model by construction only captures spectral weight in the target space. In the SSH model, the electron-phonon coupling mechanism involves exclusively hybridization between the low- and high-energy bands and this limits the computational efficiency gain of downfolded models.

Ideal Gas Reference for Association and Dissociation Reactions: II. Kinetic Reference Potentials and Concentration Bias in Electrolysis

The ideal gas reference for association and dissociation reactions, developed in the first part of this series, is applied to electrochemical reactions. We obtain an ideal Nernst equation that quantifies the unspecific voltage contribution arising from an imbalance between the reactant and product concentrations of an electrochemical reaction for the given conditions. Subtracting this concentration bias from the equilibrium voltage/potential, we define the "kinetic reference voltage/potential" where the reactant and product states are "aligned" within the potential energy landscape of the system. The kinetic reference voltage/potential is a fundamental descriptor for a given electrochemical reaction, providing an intrinsic reference point which is most relevant in cases where the (standard) equilibrium voltage/potential is biased by large concentration differences between the reactant and product side. This is most dramatic for the case of water electrolysis, where the gaseous H₂ and O₂ product concentrations are several orders of magnitude smaller than the liquid water reactant concentration. The respective equilibrium voltage is strongly biased by the low H₂ and O₂ concentrations, although the latter do not directly influence the forward water splitting rate. The unbiased kinetic reference voltage agrees remarkably well with the experimentally observed onset of macroscopic water splitting rates. We further extend our analysis to the kinetic reference potentials of the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and lattice oxygen evolution reaction (LOER), providing an unconventional perspective on pH-dependent overpotentials, anticipated electrocatalysis improvements, and kinetic stabilization of electrocatalyst materials.

Multi-stability Property of Magneto-Kresling Truss Structures

The Kresling truss structure, derived from Kresling-ori, has been widely studied for its bi-stability and various other properties that are useful for diverse engineering applications. The stable states of Kresling trusses are governed by their geometry and elastic response, which involves a limited design space that has been well-explored in previous studies. In this work, we present a novel magneto-Kresling truss design that involves embedding nodal magnets in the structure, which results in a more complex energy landscape, and consequently, greater tunability under mechanical deformation. We explore this energy landscape first along the zero-torque folding path and then release the restraint on the path to explore the complete two-degree-of-freedom behavior for various structural geometries and magnet strengths. We show that the magnetic interaction could alter the potential energy landscape by either changing the stable configuration, adjusting the energy well depth, or both. Energy wells with different minima endow this magneto-elastic structure with an outstanding energy storage capacity. More interestingly, proper design of the magneto-Kresling truss system yields a tri-stable structure, which is not possible in the absence of magnets. We also demonstrate various loading paths that can induce desired conformational changes of the structure. The proposed magneto-Kresling truss design sets the stage for fabricating tunable, scalable magneto-elastic multi-stable systems that can be easily utilized for applications in energy harvesting, storage, vibration control, as well as active structures with shape-shifting capability.

Propelling and perturbing appendages together facilitate strenuous ground self-righting

Terrestrial animals must self-right when overturned on the ground, but this locomotor task is strenuous. To do so, the discoid cockroach often pushes its wings against the ground to begin a somersault which rarely succeeds. As it repeatedly attempts this, the animal probabilistically rolls to the side to self-right. During winged self-righting, the animal flails its legs vigorously. Here, we studied whether wing opening and leg flailing together facilitate strenuous ground self-righting. Adding mass to increase hind leg flailing kinetic energy increased the animal’s self-righting probability. We then developed a robot with similar strenuous self-righting behavior and used it as a physical model for systematic experiments. The robot’s self-righting probability increased with wing opening and leg flailing amplitudes. A potential energy landscape model revealed that, although wing opening did not generate sufficient kinetic energy to overcome the high pitch potential energy barrier to somersault, it reduced the barrier for rolling, facilitating the small kinetic energy from leg flailing to probabilistically overcome it to self-right. The model also revealed that the stereotyped body motion during self-righting emerged from physical interaction of the body and appendages with the ground. Our work demonstrated the usefulness of potential energy landscape for modeling self-righting transitions.