Explore the vast range of titles available.

Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation model between the crystal structure and formation enthalpies at the given database, and an optimization algorithm (OA) is used to accelerate the search for crystal structure with lowest formation enthalpy. The framework of the utilized approach (a database + a GN model + an optimization algorithm) is flexible. We implemented two benchmark databases, i.e ., the open quantum materials database (OQMD) and Matbench (MatB), and three OAs, i.e ., random searching (RAS), particle-swarm optimization (PSO) and Bayesian optimization (BO), that can predict crystal structures at a given number of atoms in a periodic cell. The comparative studies show that the GN model trained on MatB combined with BO, i.e ., GN(MatB)-BO, exhibit the best performance for predicting crystal structures of 29 typical compounds with a computational cost three orders of magnitude less than that required for conventional approaches screening structures through density functional theory calculation. The flexible framework in combination with a materials database, a graph network, and an optimization algorithm may open new avenues for data-driven crystal structural predictions. Predicting crystal structure prior to experimental synthesis is highly desirable. Here the authors propose a machine-learning framework combining graph network and optimization algorithms for crystal structure prediction, which is about three orders of magnitude faster than DFT-based approach.

The effective Hamiltonian method has recently received considerable attention due to its power to deal with finite-temperature problems and large-scale systems. In this work, we put forward a machine learning (ML) approach to generate realistic effective Hamiltonians. In order to find out the important interactions among many possible terms, we propose some new techniques. In particular, we suggest a new criterion to select models with less parameters using a penalty factor instead of the commonly-adopted additional penalty term, and we improve the efficiency of variable selection algorithms by estimating the importance of each possible parameter by its relative uncertainty and the error induced in the parameter reduction. We also employ a testing set and optionally a validation set to help prevent over-fitting problems. To verify the reliability and usefulness of our approach, we take two-dimensional MnO and three-dimensional TbMnO3 as examples. In the case of TbMnO3, our approach not only reproduces the known results that the Heisenberg, biquadratic, and ring exchange interactions are the major spin interactions, but also finds out that the next most important spin interactions are three-body fourth-order interactions. In both cases, we obtain effective spin Hamiltonians with high fitting accuracy. These tests suggest that our ML approach is powerful for identifying the effective spin Hamiltonians. Our ML approach is general so that it can be adopted to construct other effective Hamiltonians.

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, N 2 AMD (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, N 2 AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N 2 AMD 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, N 2 AMD 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. Accurate nonadiabatic molecular dynamics (NAMD) is crucial for studying excited-state dynamics in solids but is computationally expensive. Here, authors use machine learning to enhance the efficiency and accuracy of NAMD simulations in solids.

Defects play significant roles in spin-current-related physical processes in intrinsic ferromagnetic semiconductors (FMSs), which are great promise for spintronics applications. However, current defect calculation methods cannot be used to investigate charged defects in FMSs due to the spin polarization of both the charged defect states and ionized carriers, which is not well treated in current defect calculation methods. In order to solve this problem, we propose a spin-distinguishable charge correction (SDCC) method that uses spin-polarized band edge charge density instead of spin-unpolarized uniform background charge density as the compensating charge for charged defects. We apply our method to study the defect properties of CrI3 monolayer and find it can be doped n-type under the Cr-rich growth condition but difficult to be doped p-type. The SDCC method proposed here is generally suitable for all FMSs, which will be useful for the studies of defect properties of magnetic semiconductors.

A high-frequency optical phonon mode of SrTiO 3 (STO) was found to assist the high-temperature superconductivity observed recently at the interface between monolayer FeSe and STO substrate. However, the origin of this mode is not clear. Through first-principles calculations, we find that there is a novel polar phonon mode on the surface layers of the STO substrate, which does not exist in the STO crystals. The oxygen vacancies near the FeSe/STO interface drives the dispersion of this phonon mode to be flat and lowers its energy, whereas the charge transfer between STO substrate and FeSe monolayer further reduces its energy to 81 meV. This energy is in good agreement with the experimental value fitted by Lee et al. for the phonon mode responsible for the observed replica band separations and the increased superconducting gap. The oxygen-vacancy-induced flat and polar phonon mode provides clues for understanding the origin of high Tc superconductivity at the FeSe/STO interface.

Despite the successes of machine learning methods in physical sciences, the prediction of the Hamiltonian, and thus the electronic properties, is still unsatisfactory. Based on graph neural network (NN) architecture, we present an extendable NN model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. The rotational equivariance of the Hamiltonian is achieved by our complete local coordinates (LCs). The LC information, encoded using a convolutional NN and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our NN model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, the present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials.

The calculation of electron-phonon couplings (EPCs) is essential for understanding various fundamental physical properties, including electrical transport, optical and superconducting behaviors in materials. However, obtaining EPCs through fully first-principles methods is notably challenging, particularly for large systems or when employing advanced functionals. Here we introduce a machine learning framework to accelerate EPC calculations by utilizing atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network. We demonstrate that our method not only yields EPC values in close agreement with first-principles results but also enhances calculation efficiency by several orders of magnitude. Application to GaAs using the Heyd-Scuseria-Ernzerhof functional reveals the necessity of advanced functionals for accurate carrier mobility predictions, while for the large Kagome crystal CsV Sb , our framework reproduces the experimentally observed double domes in pressure-induced superconducting phase diagrams. This machine learning framework offers a powerful and efficient tool for the investigation of diverse EPC-related phenomena in complex materials.

The lifetimes of non-equilibrium charge carriers in semiconductors calculated using non-adiabatic molecular dynamics often differ from experimental results by orders of magnitude. By revisiting the definition of carrier lifetime, we report a systematic procedure for calculating the effective carrier lifetime in semiconductor crystals under realistic conditions. The consideration of all recombination mechanisms and the use of appropriate carrier and defect densities are crucial to bridging the gap between modeling and measurements. Our calculated effective carrier lifetime of CH NH PbI agrees with experiments, and is limited by band-to-band radiative recombination and Shockley-Read-Hall defect-assisted non-radiative recombination, whereas the band-to-band non-radiative recombination is found to be negligible. The procedure is further validated by application to the compound semiconductors CdTe and GaAs, and thus can be applied in carrier lifetime simulations in other material systems.

We have studied the thermal conductivity of single-walled carbon nanotubes (SWCNTs) using the NEMD method. The results indicate that the thermal conductivity values are not profoundly influenced by the specific simulation-technique used in the MD simulations. Some possible reasons, which could be responsible for the discrepancy on thermal conductivity values of SWCNTs in the literatures, are discussed.

Charge transfer in type-II heterostructures plays important roles in determining device performance for photovoltaic and photocatalytic applications. However, current theoretical studies of charge transfer process don't consider the effects of operating conditions such as illuminations and yield systemically larger interlayer transfer time of hot electrons in MoS2/WS2 compared to experimental results. Here in this work, we propose a general picture that, illumination can induce interfacial dipoles in type-II heterostructures, which can accelerate hot carrier transfer by reducing the energy difference between the electronic states in separate materials and enhancing the nonadiabatic couplings. Using the first-principles calculations and the ab-initio nonadiabatic molecular dynamics, we demonstrate this picture using MoS2/WS2 as a prototype. The calculated characteristic time for the interlayer transfer (60 fs) and the overall relaxation (700 fs) processes of hot electrons is in good agreement with the experiments. We further find that illumination mainly affects the ultrafast interlayer transfer process but has little effects on the relatively slow intralayer relaxation process. Therefore, the overall relaxation process of hot electrons has a saturated time with increased illumination strengths. The illumination-accelerated charge transfer is expected to universally exist in type-II heterostructures.