Tunable Ultrafast Charge Transfer across Homojunction Interface.

Charge transfer at heterojunction interfaces is a fundamental process that plays a crucial role in modern electronic and photonic devices. The essence of such charge transfer lies in the band offset, making charge transfer uncommon in a homojunction. Recently, sliding ferroelectricity has been proposed and confirmed in two-dimensional van der Waals stacked materials such as bilayer boron nitride. During the sliding of these layers, the band alignment shifts, creating conditions for charge separation at the interface. We employ ab initio nonadiabatic molecular dynamics simulations to elucidate the excited state carrier dynamics in bilayer boron pnictides. We propose that, akin to ferroelectric polarization flipping, the precise modulation of the distribution of excited state carriers can also be reached by sliding. Our results demonstrate that sliding induces a reversal of the frontier orbital distribution on the upper and lower layers, facilitating a robust interlayer carrier transfer. Notably, the interlayer carrier transfer is more pronounced in boron phosphide than in boron nitride, attributed to strong electron scattering in momentum space in boron nitride. We propose this novel method to manipulate carrier distribution and dynamics in a homojunction exhibiting sliding ferroelectricity, in general, paving a new way for developing advanced electronic and photonic devices.

Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential.

Liquid-liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid's atomic arrangements. When examining CdSxTe1-x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x > 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x < 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system's behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes.

Theoretical study on the magnetic properties of cathode materials in the lithium-ion battery.

The layered LiMO2 (M = Co, Ni, and Mn) materials are commonly used as the cathode materials in the lithium-ion battery due to the distinctive layer structure for lithium extraction and insertion. Although their electrochemical properties have been extensively studied, the structural and magnetic properties of LiNiO2 are still under considerable debate, and the magnetic properties of monoclinic LiMnO2 are seldom reported. In this work, a detailed study of LiNiO2, LiMnO2, and a half-doped material LiNi0.5Mn0.5O2 is performed via both first-principles calculations and Monte Carlo simulations based on the effective spin Hamiltonian model. Through considering different structures, it is verified that a structure with a zigzag-type pattern is the most stable one of LiNiO2. Moreover, in order to figure out the magnetic properties, the spin exchange interactions are calculated, and then magnetic ground states are predicted in these three systems. The results show that LiNiO2 forms a spiral order that is caused by the competition from both the short-range and long-range spin exchange interactions, whereas the magnetic ground state of LiMnO2 is collinearly antiferromagnetic due to its nearest and next-nearest neighbor antiferromagnetic spin exchange interactions. However, LiNi0.5Mn0.5O2 is collinearly ferrimagnetic because of the ferromagnetic nearest neighbor Ni-Ni and Mn-Mn exchange interactions. Our work demonstrates the competition between the different exchange interactions in these cathode materials, which may be relevant to the performance of the lithium-ion battery.

Dimensionality-Inhibited Chemical Doping in Two-Dimensional Semiconductors: The Phosphorene and MoS2 from Charge-Correction Method.

Despite the great appeal of two-dimensional semiconductors for electronics and optoelectronics, to achieve the required charge carrier concentrations by means of chemical doping remains a challenge due to large defect ionization energies (IEs). Here, by decomposing the defect IEs into three parts based on ionization process, we propose a conceptual picture that the large defect IEs are caused by two effects of reduced dimensionality. While the quantum confinement effect makes the neutral single-electron point defect levels deep, the reduced screening effect leads to high energy cost for the electronic relaxation. The first-principles calculations for black phosphorus and MoS2 do demonstrate the general trend. Using BP monolayer either embedded into dielectric continuum or encapsulated between two hBN layers, we demonstrate the feasibility of increasing the screening to reduce the defect IEs. Our analysis is expected to help achieve effective carrier doping and open ways toward more extensive applications of 2D semiconductors.

More Se Vacancies in Sb2 Se3 under Se-Rich Conditions: An Abnormal Behavior Induced by Defect-Correlation in Compensated Compound Semiconductors.

It was believed that the Se-rich synthesis condition can suppress the formation of deep-level donor defect VSe (selenium vacancy) in Sb2 Se3 and is thus critical for fabricating high-efficiency Sb2 Se3 solar cells. However, here it is shown that by first-principles calculations the density of VSe increases unexpectedly to 1016 cm-3 when the Se chemical potential increases, so Se-rich condition promotes rather than suppresses the formation of VSe . Therefore, high density of VSe is thermodynamically inevitable, no matter under Se-poor or Se-rich conditions. This abnormal behavior can be explained by a physical concept "defect-correlation", i.e., when donor and acceptor defects compensate each other, all defects become correlated with each other due to the formation energy dependence on Fermi level which is determined by densities of all ionized defects. In quasi-1D Sb2 Se3 , there are many defects and the complicated defect-correlation can give rise to abnormal behaviors, e.g., lowering Fermi level and thus decreasing the formation energy of ionized donor VSe 2+ in Se-rich Sb2 Se3 . Such behavior exists also in Sb2 S3 . It explains the recent experiments that the extremely Se-rich condition causes the efficiency drop of Sb2 Se3 solar cells, and demonstrates that the common chemical intuition and defect engineering strategies may be invalid in compensated semiconductors.

Hyperdynamics simulations with ab initio forces.

By applying the locally optimal rotation method to deal with the lowest eigenvalue of a Hessian matrix, we have efficiently incorporated the hyperdynamics method into the ab initio scheme. In the present method, we only need to calculate the first derivative of the potential and several more force calls in each molecular dynamics (MD) step, which makes hyperdynamics simulation applicable in ab initio MD simulations. With this implementation, we are able to simulate defect diffusion in silicon with boost factors up to 105. We utilized both direct MD and the hyperdynamics method to investigate diffusion of lithium atoms and silicon vacancies in silicon. We identified the complex diffusion process. The obtained diffusion coefficients of Li atoms and Si vacancies are in good agreement with the direct MD results.

Topological Dirac States beyond π-Orbitals for Silicene on SiC(0001) Surface.

The discovery of intriguing properties related to the Dirac states in graphene has spurred huge interest in exploring its two-dimensional group-IV counterparts, such as silicene, germanene, and stanene. However, these materials have to be obtained via synthesizing on substrates with strong interfacial interactions, which usually destroy their intrinsic π(pz)-orbital Dirac states. Here we report a theoretical study on the existence of Dirac states arising from the px,y orbitals instead of pz orbitals in silicene on 4H-SiC(0001), which survive in spite of the strong interfacial interactions. We also show that the exchange field together with the spin-orbital coupling give rise to a detectable band gap of 1.3 meV. Berry curvature calculations demonstrate the nontrivial topological nature of such Dirac states with a Chern number C = 2, presenting the potential of realizing quantum anomalous Hall effect for silicene on SiC(0001). Finally, we construct a minimal effective model to capture the low-energy physics of this system. This finding is expected to be also applicable to germanene and stanene and imply great application potentials in nanoelectronics.

Branched artificial nanofinger arrays by mesoporous interfacial atomic rearrangement.

The direct production of branched semiconductor arrays with highly ordered orientation has proven to be a considerable challenge over the last two decades. Here we report a mesoporous interfacial atomic rearrangement (MIAR) method to directly produce highly crystalline, finger-like branched iron oxide nanoarrays from the mesoporous nanopyramids. This method has excellent versatility and flexibility for heteroatom doping of metallic elements, including Sn, Bi, Mn, Fe, Co, Ni, Cu, Zn, and W, in which the mesoporous nanopyramids first absorb guest-doping molecules into the mesoporous channels and then convert the mesoporous pyramids into branching artificial nanofingers. The crystalline structure can provide more optoelectronic active sites of the nanofingers by interfacial atomic rearrangements of doping molecules and mesopore channels at the porous solid-solid interface. As a proof-of-concept, the Sn-doped Fe2O3 artificial nanofingers (ANFs) exhibit a high photocurrent density of ∼1.26 mA/cm(2), ∼5.25-fold of the pristine mesoporous Fe2O3 nanopyramid arrays. Furthermore, with surface chemical functionalization, the Sn-doped ANF biointerfaces allow nanomolar level recognition of metabolism-related biomolecules (∼5 nm for glutathione). This MIAR method suggests a new growth means of branched mesostructures, with enhanced optoelectronic applications.

Mesoporous Fe2O3-CdS Heterostructures for Real-Time Photoelectrochemical Dynamic Probing of Cu(2+).

A three-dimensional (3D) mesoporous Fe2O3-CdS nanopyramid heterostructure is developed for solar-driven, real-time, and selective photoelectrochemical sensing of Cu(2+) in the living cells. Fabrication of the mesoporous Fe2O3 nanopyramids is realized by an interfacial aligned growth and self-assembly process, based on the van der drift model and subsequent selective in situ growth of CdS nanocrystals. The as-prepared mesoporous Fe2O3-CdS heterostructures achieve significant enhancement (∼3-fold) in the photocurrent density compared to pristine mesoporous Fe2O3, which is attributed to the unique mesoporous heterostructures with multiple features including excellent flexibility, high surface area (∼87 m(2)/g), and large pore size (∼20 nm), enabling the PEC performance enhancement by facilitating ion transport and providing more active electrochemical reaction sites. In addition, the introduction of Cu(2+) enables the activation of quenching the charge transfer efficiency, thus leading to sensitive photoelectrochemical recording of Cu(2+) level in buffer and cellular environments. Furthermore, real-time monitoring (∼0.5 nM) of Cu(2+) released from apoptotic HeLa cell is performed using the as-prepared 3D mesoporous Fe2O3-CdS sensor, suggesting the capability of studying the nanomaterial-cell interfaces and illuminating the role of Cu(2+) as trace element.

Solar-driven photoelectrochemical probing of nanodot/nanowire/cell interface.

We report a nitrogen-doped carbon nanodot (N-Cdot)/TiO2 nanowire photoanode for solar-driven, real-time, and sensitive photoelectrochemical probing of the cellular generation of H2S, an important endogenous gasotransmitter based on a tunable interfacial charge carrier transfer mechanism. Synthesized by a microwave-assisted solvothermal method and subsequent surface chemical conjugation, the obtained N-Cdot/TiO2 nanowire photoanode shows much enhanced photoelectrochemical photocurrent compared with pristine TiO2 nanowires. This photocurrent increase is attributed to the injection of photogenerated electrons from N-Cdots to TiO2 nanowires, confirmed by density functional theory simulation. In addition, the charge transfer efficiency is quenched by Cu(2+), whereas the introduction of H2S or S(2-) ions resets the charge transfer and subsequently the photocurrent, thus leading to sensitive photoelectrochemical recording of the H2S level in buffer and cellular environments. Moreover, this N-Cdot-TiO2 nanowire photoanode has been demonstrated for direct growth and interfacing of H9c2 cardiac myoblasts, with the capability of interrogating H2S cellular generation pathways by vascular endothelial growth factor stimulation as well as inhibition.

Prediction of silicon-based layered structures for optoelectronic applications.

A method based on the particle swarm optimization algorithm is presented to design quasi-two-dimensional materials. With this development, various single-layer and bilayer materials of C, Si, Ge, Sn, and Pb were predicted. A new Si bilayer structure is found to have a more favored energy than the previously widely accepted configuration. Both single-layer and bilayer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2 and Si6H2 possessing quasidirect band gaps of 0.75 and 1.59 eV, respectively. Their potential applications for light-emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

An approach for full space inverse materials design by combining universal machine learning potential, universal property model, and optimization algorithm.

We present a full space inverse materials design (FSIMD) approach that fully automates the materials design for target physical properties without the need to provide the atomic composition, chemical stoichiometry, and crystal structure in advance. Here, we used density functional theory reference data to train a universal machine learning potential (UPot) and transfer learning to train a universal bulk modulus model (UBmod). Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements. Interfaced with optimization algorithm and enhanced sampling, the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus, respectively. NaCl-type ZrC was found to be the material with the largest cohesive energy. For bulk modulus, diamond was identified to have the largest value. The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount, reliability, and diversity of the training data. The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

Accelerating the calculation of electron-phonon coupling strength with machine learning.

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 CsV3Sb5, 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.

Prediction of (TiO2)x(Cu2O)y alloys for efficient photoelectrochemical water splitting.

The formation of (TiO(2))(x)(Cu(2)O)(y) solid-solutions is investigated using a global optimization evolutionary algorithm. First-principles calculations based on density functional theory are then used to gain insight into the electronic properties of these alloys. We find that: (i) Ti and Cu in (TiO(2))(x)(Cu(2)O)(y) alloys have similar local environments as in bulk TiO(2) and Cu(2)O except for (TiO(2))(Cu(2)O) which has some trigonal-planar Cu ions. (ii) The predicted optical band gaps are around 2.1 eV (590 nm), thus having much better performance in the absorption of visible light compared with both binary oxides. (iii) (TiO(2))(2)(Cu(2)O) has the lowest formation energy amongst all studied alloys and the positions of its band edges are found to be suitable for solar-driven water splitting applications.

A General Tensor Prediction Framework Based on Graph Neural Networks.

Graph neural networks (GNNs) have been shown to be extremely flexible and accurate in predicting the physical properties of molecules and crystals. However, traditional invariant GNNs are not compatible with directional properties, which currently limits their usage to the prediction of only invariant scalar properties. To address this issue, here we propose a general framework, i.e., an edge-based tensor prediction graph neural network, in which a tensor is expressed as the linear combination of the local spatial components projected on the edge directions of clusters with varying sizes. This tensor decomposition is rotationally equivariant and exactly satisfies the symmetry of the local structures. The accuracy and universality of our new framework are demonstrated by the successful prediction of various tensor properties from first to third order. The framework proposed in this work will enable GNNs to step into the broad field of prediction of directional properties.

Si3AlP: a new promising material for solar cell absorber.

First-principles calculations were performed to study the structural and optoelectronic properties of the newly synthesized nonisovalent and lattice-matched (Si(2))(0.6)(AlP)(0.4) alloy (Watkins, T.; et al. J. Am. Chem. Soc.2011, 133, 16212). We found that the most stable structure of Si(3)AlP is a superlattice along the [111] direction with separated AlP and Si layers, which has a similar optical absorption spectrum to silicon. The ordered C1c1-Si(3)AlP is found to be the most stable one among all structures with a basic unit of one P atom surrounded by three Si atoms and one Al atom, in agreement with experimental suggestions. We predict that C1c1-Si(3)AlP has good optical properties, i.e., it has a larger fundamental band gap and a smaller direct band gap than Si; thus, it has much higher absorption in the visible light region. The calculated properties of Si(3)AlP suggest that it is a promising candidate for improving the performance of the existing Si-based solar cells. The understanding on the stability and band structure engineering obtained in this study is general and can be applied for future study of other nonisovalent and lattice-matched semiconductor alloys.

Abnormally weak intervalley electron scattering in MoS2 monolayer: insights from the matching between electron and phonon bands.

It is known that carrier mobility in layered semiconductors generally increases from two-dimensions (2D) to three-dimensions due to fewer scattering channels resulting from decreased densities of electron and phonon states. In this work, we find an abnormal decrease of electron mobility from monolayer to bulk MoS2. By carefully analyzing the scattering mechanisms, we can attribute such abnormality to the stronger intravalley scattering in the monolayer but weaker intervalley scattering caused by few intervalley scattering channels and weaker corresponding electron-phonon couplings compared to the bulk case. We show that it is the matching between the electronic band structure and phonon spectrum rather than their densities of electronic and phonon states that determines scattering channels. We propose, for the first time, the phonon-energy-resolved matching function to identify the intra- and inter-valley scattering channels. Furthermore, we show that multiple valleys do not necessarily lead to strong intervalley scattering if: (1) the scattering channels, which can be explicitly captured by the distribution of the matching function, are few due to the small matching between the corresponding electron and phonon bands; and/or (2) the multiple valleys are far apart in the reciprocal space and composed of out-of-plane orbitals so that the corresponding electron-phonon coupling strengths are weak. Consequently, the searching scope of high-mobility 2D materials can be reasonably enlarged using the matching function as useful guidance with the help of band edge orbital analysis.

Crystal structure prediction by combining graph network and optimization algorithm.

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.

Semiconductor-to-metal transition from monolayer to bilayer blue phosphorous induced by extremely strong interlayer coupling: a first-principles study.

Monolayer blue phosphorous has a large band gap of 2.76 eV but counterintuitively the most stable bilayer blue phosphorous has a negative band gap of -0.51 eV. Such a large band gap reduction from just monolayer to bilayer has not been revealed before, the underlying mechanism behind which is important for understanding interlayer interactions. In this work, we reveal the origin of the semiconductor-to-metal transition using first-principles calculations and tight-binding models. We find that the interlayer interactions are extremely strong, which can be attributed to the short layer distance and strong π-like atomic orbital couplings. Therefore, the upshift of the valence band maximum (VBM) from monolayer to bilayer blue-P is so large that the VBM in the bilayer gets higher than the conduction band minimum, leading to a negative band gap and an energy gain. Besides, the interlayer atomic misplacements weaken the couplings of out-of-plane orbitals. Therefore, the energy gain due to the semiconductor-to-metal transition is larger than the energy cost due to interlayer repulsions, thus stabilizing the metallic phase. The large band gap reduction with layer number increasing is expected to exist in other similar layered systems.

Enhancing Hole Density and Suppressing Recombination Centers through Illumination in Kesterite Thin Film Solar Cells.

Enhancing carrier density and increasing carrier lifetime are critical for the good performance of thin film solar cells. We apply illumination during the growth of kesterite Cu2ZnSnS4 (CZTS) to enhance hole density and suppress defects of nonradiative electron-hole recombination centers simultaneously. To examine the effect of the injected carriers generated by illumination, we first extend the scheme of detailed balance equations relating free carriers and defects beyond thermal equilibrium conditions by developing an extended Fermi level (EF') to characterize a homogeneous semiconductor with non-equilibrium carriers. On the basis of this scheme, we find that illumination can promote the formation of carrier-providing defects and suppress the formation of carrier-compensating defects. Then, we demonstrate that applying proper illumination during the growth of CZTS will help achieve a higher hole density and simultaneously suppress the formation of the SnZn antisite significantly, which are beneficial for the performance of CZTS solar cells.