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Abstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N²AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N²AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N²AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N²AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials. 

Thin-film ferroelectrics have been pursued for capacitive and nonvolatile memory devices. They rely on polarizations that are oriented in an out-of-plane direction to facilitate integration and addressability with complementary metal-oxide semiconductor architectures. The internal depolarization field, however, formed by surface charges can suppress the out-of-plane polarization in ultrathin ferroelectric films that could otherwise exhibit lower coercive fields and operate with lower power. Here, we unveil stabilization of a polar longitudinal optical (LO) mode in the n=2 Ruddlesden–Popper family that produces out-of-plane ferroelectricity, persists under open-circuit boundary conditions, and is distinct from hyperferroelectricity. Our first-principles calculations show the stabilization of the LO mode is ubiquitous in chalcogenides and halides and relies on anharmonic trilinear mode coupling. We further show that the out-of-plane ferroelectricity can be predicted with a crystallographic tolerance factor, and we use these insights to design a room-temperature multiferroic with strong magnetoelectric coupling suitable for magneto-electric spin-orbit transistors. 

Abstract Spin‐state transitions are an important research topic in complex oxides with the diverse magnetic states involved. In particular, the low‐spin to high‐spin transition in LaCoO₃thin films has drawn a wide range of attention due to the emergent ferromagnetic state. Although various mechanisms (e.g., structural distortion, oxygen‐vacancy formation, spin‐state ordering) have been proposed, an understanding of what really underlies the emergent ferromagnetism remains elusive. Here, the ferromagnetism in LaCoO₃thin films is systematically modulated by varying the oxygen pressure during thin‐film growth. Although the samples show dramatic different magnetization, their cobalt valence state and perovskite crystalline structure remain almost unchanged, ruling out the scenarios of both oxygen‐vacancy and spin‐ordering. This work provides compelling evidence that the tetragonal distortion due to the tensile strain significantly modifies the orbital occupancy, leading to a low‐spin to high‐spin transition with emergent ferromagnetism, while samples grown at reduced pressure demonstrate a pronounced lattice expansion due to cation‐off‐stoichiometry, which suppresses the tetragonal distortion and the consequent magnetization. This result not only provides important insight for the understanding of exotic ferromagnetism in LaCoO₃thin films, but also identifies a promising strategy to design electronic states in complex oxides through cation‐stoichiometry engineering.