Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning.

Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. We apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.

Evolutionary Search and Theoretical Study of Silicene Grain Boundaries' Mechanical Properties.

Defects such as grain boundaries (GBs) are almost inevitable during the synthesis process of 2D materials. To take advantage of the fascinating properties of 2D materials, understanding the nature and impact of various GB structures on pristine 2D sheets is crucial. In this work, using an evolutionary algorithm search, we predict a wide variety of silicene GB structures with very different atomic structures compared with those found in graphene or hexagonal boron-nitride. Twenty-one GBs with the lowest energy were validated by density functional theory (DFT), a majority of which were previously unreported to our best knowledge. Based on the diversity of the GB predictions, we found that the formation energy and mechanical properties can be dramatically altered by adatom positions within a GB and certain types of atomic structures, such as four-atom rings. To study the mechanical behavior of these GBs, we apply strain to the GB structures stepwise and use DFT calculations to investigate the mechanical properties of 9 representative structures. It is observed that GB structures based on pentagon-heptagon pairs are likely to have similar or higher in-plane stiffness and strength compared to the zigzag orientation of pristine silicene. However, an adatom located at the hollow site of a heptagon ring can significantly deteriorate the mechanical strength. For all of the structures, the in-plane stiffness and strength were found to decrease with increasing formation energy. For the failure behavior of GB structures, it was found that GB structures based on pentagon-heptagon pairs have failure behavior similar to that of graphene. We also found that the GB structures with atoms positioned outside of the 2D plane tend to experience phase transitions before failure. Utilizing the evolutionary algorithm, we locate diverse silicene GBs and obtain useful information about their mechanical properties.

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials.

Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical, to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is nontrivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism) and density functional theory (DFT). Systematic downsampling of the training data sets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (<2000) DFT generated energy labels for training. We further couple the GNN model with a multiobjective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, is material agnostic, and is anticipated to accelerate the discovery of 2D GB structures.

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene.

We introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (ß-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis.

Resistance switching in metal-insulator-metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility. In this work, we have addressed these issues by demonstrating the use of ultraporous dielectrics as a pathway for high-performance resistive memory devices. Using a modified atomic layer deposition based technique known as sequential infiltration synthesis, which was developed originally for improving polymer properties such as enhanced etch resistance of electron-beam resists and for the creation of films for filtration and oleophilic applications, we are able to create ∼15 nm thick ultraporous (pore size ∼5 nm) oxide dielectrics with up to 73% porosity as the medium for filament formation. We show, using the Ag/Al2O3 system, that the ultraporous films result in ultrahigh on/off ratio (>109) at ultralow switching voltages (∼±600 mV) that are 10× smaller than those for the bulk case. In addition, the devices demonstrate fast switching, pulsed endurance up to 1 million cycles. and high temperature (125 °C) retention up to 104 s, making this approach highly promising for large-scale neuromorphic and memory applications. Additionally, this synthesis methodology provides a compatible, inexpensive route that is scalable and compatible with existing semiconductor nanofabrication methods and materials.

Cellulose based nanocomposite hydrogel films consisting of sodium carboxymethylcellulose-grapefruit seed extract nanoparticles for potential wound healing applications.

The impact of grapefruit seed extract (GFSE) as an antibacterial agent on citric acid (CA) crosslinked sodium carboxymethylcellulose (NaCMC)/hydroxypropylmethylcellulose (HPMC) hydrogel films has been studied by incorporating different quantities of GFSE. The prepared films were examined for their physical, thermal, mechanical and antibacterial properties. It was observed that crystallinity and initial decomposition temperature of hydrogel films decreased with GFSE concentration. Furthermore, the swelling degree and tensile strength of hydrogel films were found to be 257.29 ± 5.08%-162.06 ± 1.78% and 11.61 ± 0.27-2.21 ± 0.94 MPa for increasing GFSE concentration varying from 0.25% - 1.5% (v/v). The presence of nanoparticles in the films was observed by FESEM and FETEM analysis. It was confirmed that the formation of nanoparticles (micelles) is due to the addition of NaCMC and GFSE, probably glycerides, which is one of the main components in GFSE. The hydrogel films have demonstrated excellent antimicrobial activity and elongation at break (%). Moreover, zeta potential of nanoparticles was recorded to be -55.26 mV ascertaining their stability in water that contributed to a higher antimicrobial activity against gram negative bacteria. All these outcomes prove the nanocomposite films to be a potential substitute for hydrogels loaded with synthetic drugs in wound healing and other biological applications.

Effect of graphene oxide (GO) nanosheet sizes, pinhole defects and non-ideal lamellar stacking on the performance of layered GO membranes: an atomistic investigation.

The effect of non-idealities, namely pinhole defects and non-ideal lamellar stacking of nanosheets, on the performance of size-differentiated graphene oxide (GO) laminates is investigated using equilibrium molecular dynamics (MD) simulations. With the increase in sizes of the constituent GO nanosheets the water permeability of the layered GO membranes decreases and salt rejection increases. But with the inclusion of non-idealities the difference in water permeability between these membranes substantially reduced. The pinholes on the GO nanosheets provide shorter routes for trans-sheet flow, thereby increasing the water permeability of the membranes. The non-ideal stacking of the nanosheets without pinhole defects results in slight reduction in water permeability because of blockage of permeation pathways inside the membranes. However, with pinhole defects non-ideal stacking becomes favorable for water permeation through the layered GO membranes; as this time the non-ideal stacking leads to formation of voids inside the membranes, which act as routes for shorter permeation pathways. The effect of these non-idealities is more significant for layered GO membranes composed of large GO nanosheets. Although the water permeability through the layered GO membrane is greatly enhanced because of these non-idealities (about 10 times), the corresponding variation in the salt rejection is very low (<2%).