A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C5N monolayers

Remarkably high tensile strength and lattice thermal conductivity in wide band gap oxidized holey graphene C2O nanosheet

Recently, the synthesis of oxidized holey graphene with the chemical formula C2O has been reported (J. Am. Chem. Soc. 2024, 146, 4532). We herein employed a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calculations to investigate the electronic, optical, mechanical and thermal properties of the C2O monolayer, and compared our findings with those of its C2N counterpart. Our analysis shows that while the C2N monolayer exhibits delocalized π-conjugation and shows a 2.47 eV direct-gap semiconducting behavior, the C2O counterpart exhibits an indirect gap of 3.47 eV. We found that while the C2N monolayer exhibits strong absorption in the visible spectrum, the initial absorption peaks in the C2O lattice occur at around 5 eV, falling within the UV spectrum. Notably, we found that the C2O nanosheet presents significantly higher tensile strength compared to its C2N counterpart. MLIP-based calculations show that at room temperature, the C2O nanosheet can exhibit remarkably high tensile strength and lattice thermal conductivity of 42 GPa and 129 W/mK, respectively. The combined insights from DFT and MLIP-based results provide a comprehensive understanding of the electronic and optical properties of C2O nanosheets, suggesting them as mechanically robust and highly thermally conductive wide bandgap semiconductors.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods

Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material's significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Highly-efficient heterojunction solar cells based on 2D Janus transition-metal nitride halide (TNH) monolayers with ultrahigh carrier mobility

Symmetry breaking has a crucial effect on electronic band structure and subsequently affects the light-absorption coefficient of monolayers. We systematically report a family of two-dimensional (2D) Janus transition-metal nitride halides (TNHs, T = Ti, Zr, Hf, Fe, Pd, Pt, Os, and Re; H = Cl and F) with breaking of both in-plane and out-of-plane structural symmetry. The dynamical, thermal and mechanical stabilities are calculated to check the stability of the Janus TNHs. The electric properties of ten TNHs are studied via the HSE06+SOC method and the band gaps range from 0.93 eV (PdNCl) to 4.74 eV (HfNCl). Desirable light adsorption coefficients of up to 105 cm-1 are obtained for the Janus TNHs with no central symmetry. The Janus OsNCl monolayer shows excellent electrical transport properties and ultrahigh carrier mobility (104 cm2 V-1 s-1). Heterojunctions formed by stacking two Janus TNH monolayers are further investigated for solar cell applications. Eight of the heterojunctions have type-II band alignments. Surprisingly, the OsNCl/FeNCl heterojunction has a power conversion efficiency (PCE) of 23.45%, which is a larger value compared to the PCE of GeSe/SnSe heterostructures (21.47%). The optical properties and the built-in electric field of the OsNCl/FeNCl heterojunction are investigated. These results indicate that the stable Janus TNH monolayers have potential applications in photoelectric devices, and the vertical heterojunctions can be used in solar cells.

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more accurate and reliable molecular dynamics calculations. As an exciting novel progress, in the last couple of years the applications of MLIPs have been extended towards the analysis of mechanical and failure responses, providing novel opportunities not heretofore efficiently achievable, neither by EIPs nor by density functional theory (DFT) calculations. In this minireview, we first briefly discuss the basic concepts of MLIPs and outline popular strategies for developing a MLIP. Next, by considering several examples of recent studies, the robustness of MLIPs in the analysis of the mechanical properties will be highlighted, and their advantages over EIP and DFT methods will be emphasized. MLIPs furthermore offer astonishing capabilities to combine the robustness of the DFT method with continuum mechanics, enabling the first-principles multiscale modeling of mechanical properties of nanostructures at the continuum level. Last but not least, the common challenges of MLIP-based molecular dynamics simulations of mechanical properties are outlined and suggestions for future investigations are proposed.

Investigation of the mechanical and transport properties of InGeX3 (X = S, Se and Te) monolayers using density functional theory and machine learning

Recently, novel 2D InGeTe₃ has been successfully synthesized and attracted attention due to its excellent properties. In this study, we investigated the mechanical properties and transport behavior of InGeX₃ (X = S, Se and Te) monolayers using density functional theory (DFT) and machine learning (ML). The key physical parameters related to mechanical properties, including Poisson's ratio, elastic modulus, tensile strength and critical strain, were revealed. Using a ML method to train DFT data, we developed a neuroevolution-potential (NEP) to successfully predict the mechanical properties and lattice thermal conductivity. The fracture behavior predicted using NEP-based MD simulations in a large supercell containing 20 000 atoms could be verified using DFT. Due to the effects of size, these predicted physical parameters have a slight difference between DFT and ML methods. At 300 K, these monolayers exhibited a low thermal conductivity with the values of 13.27 ± 0.24 W m^-1 K^-1 for InGeS₃, 7.68 ± 0.30 W m^-1 K^-1 for InGeSe₃, and 3.88 ± 0.09 W m^-1 K^-1 for InGeTe₃, respectively. The Boltzmann transport equation (BTE) including all electron-phonon interactions was used to accurately predict the electron mobility. Compared with InGeS₃ and InGeSe₃, the InGeTe₃ monolayer showed flexible mechanical behavior, low thermal conductivity and high mobility.

Grain boundary sliding and distortion on a nanosecond timescale induce trap states in CsPbBr3: ab initio investigation with machine learning force field

Grain boundaries (GBs) in perovskite solar cells and optoelectronic devices are widely regarded as detrimental defects that accelerate charge and energy losses through nonradiative carrier trapping and recombination, but the mechanism is still under debate owing to the diversity of GB configurations and behaviors. We combine ab initio electronic structure and machine learning force field to investigate evolution of the geometric and electronic structure of a CsPbBr₃ GB on a nanosecond timescale, which is comparable with the carrier recombination time. We demonstrate that the GB slides spontaneously within a few picoseconds increasing the band gap. Subsequent structural oscillations dynamically produce midgap trap states through Pb-Pb interactions across the GB. After several hundred picoseconds, structural distortions start to occur, increasing the occurrence of deep midgap states. We identify a distinct correlation of the average Pb-Pb distance and fluctuations in the ion coordination numbers with the appearance of the midgap states. Suppressing GB distortions through annealing and breaking up Pb-Pb dimers by passivation can efficiently alleviate the detrimental effects of GBs in perovskites. The study provides new insights into passivation of the detrimental GB defects, and demonstrates that structural and charge carrier dynamics in perovskites are intimately coupled.