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.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning.

Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.

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.

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide.

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO2) profoundly modifies the ground state properties. The electrical behavior of HxVO2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. In this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a HxVO2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information sciences.

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices.

Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3, H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveils a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

Nano-fibre matrix loaded with multi-nutrients to achieve balanced crop nutrition in greengram (Vigna radiata L.).

This research paper focuses on exploring the possibility of delivering macro, micro and trace elements using seed encapsulation through nano-fibres that are known to improve the nutrient use efficiencies while reducing the loss of nutrients. The nano-fibres were developed using an electrospinning machine by subjecting the polymer solution (10% polyvinyl alcohol PVA) loaded with recommended quantities of nutrients under optimal solution (pH, concentration, viscosity) and process (voltage, flow rate, tip-to-collector distance) parameters. The nano-fibres were characterized using SEM, TEM, FT-IR, XRD, TGA and Impedance spectra besides nutrient release pattern by ICP-MS. The data have clearly shown that nano-fibres retained nutrients and released slowly up to 35 days. After the characterization, green gram (Vigna radiata L) seeds were encapsulated with nano-fibres loaded with multi-nutrients and each seed was coated with approximately 20-25 mg of nano-fibres, dibbled into the soil and the physiological, nutritional, growth and yield responses were assessed. Seeds encapsulated with nano-fibres fortified with nutrients (NF) had registered significantly higher crop emergence percentage (C 62%; NF 99.8%), root length (C 12.3; NF 27.1 cm), shoot length (C 28.7; NF 47.7 cm), dry matter production (C 16.2; NF 27.5 g) and grain yield (C 621.6; NF 796.3 kg ha-1). All the parameters measured in nano-fibre encapsulated seeds fortified with 100% of recommended dose of nutrients (NF) were higher than uncoated control (C) seeds but comparable with 100 % conventional fertilizer applied ones (RDF). Such phenomenal increase in growth and yield parameters associated with the extensive surface area of nano-fibres that is capable of retaining and releasing nutrients in a regulated pattern and assist in improving the pulses productivity by achieving balance crop nutrition which alleviating multi-nutrient deficiencies.

Adhesion of impure ice on surfaces.

The undesirable buildup of ice can compromise the operational safety of ships in the Arctic to high-flying airplanes, thereby having a detrimental impact on modern life in cold climates. The obstinately strong adhesion between ice and most functional surfaces makes ice removal an energetically expensive and dangerous affair. Hence, over the past few decades, substantial efforts have been directed toward the development of passive ice-shedding surfaces. Conventionally, such research on ice adhesion has almost always been based on ice solidified from pure water. However, in all practical situations, freezing water has dissolved contaminants; ice adhesion studies of which have remained elusive thus far. Here, we cast light on the fundamental role played by various impurities (salt, surfactant, and solvent) commonly found in natural water bodies on the adhesion of ice on common structural materials. We elucidate how varying freezing temperature & contaminant concentration can significantly alter the resultant ice adhesion strength making it either super-slippery or fiercely adherent. The entrapment of impurities in ice changes with the rate of freezing and ensuing adhesion strength increases as the cooling temperature decreases. We discuss the possible role played by the in situ generated solute enriched liquid layer and the nanometric water-like disordered ice layer sandwiched between ice and the substrate behind these observations. Our work provides useful insights into the elementary nature of impure water-to-ice transformation and contributes to the knowledge base of various natural phenomena and rational design of a broad spectrum of anti-icing technologies for transportation, infrastructure, and energy systems.

Correction: Accelerating copolymer inverse design using monte carlo tree search.

Correction for 'Accelerating copolymer inverse design using monte carlo tree search' by Tarak K. Patra et al., Nanoscale, 2020, 12, 23653-23662, https://doi.org/10.1039/D0NR06091G.

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid-liquid mixtures.

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid-liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid-liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

A Rare Cleptoparasitic Bee Tetralonioidella himalayana (Bingham, 1897) (Hymenoptera: Apidae) from India: Review and New Data.

Tetralonioidella Strand 1914 is a very rare apid genus globally, of which only two species, Tetralonioidella himalayana (Bingham, 1897) and Tetralonioidella tricolor (Lieftinck, 1972), are known from India. They are known to be cleptoparasitic on species of Habropoda Smith, 1854 and Elaphropoda Lieftinck, 1966. During hymenopteran survey in Arunachal Pradesh, we encountered Tetralonioidella himalayana (Bingham, 1897) and observed their behavioral regime, which is very poorly known. The identifying characteristics of both male and female, their foraging behavior, floral preference, distribution pattern, and possible host association have been studied. Exclusive floral association and host specialization are potential contributing factors to the rarity of T. himalayana. Such factors may limit the distribution range of the species. A priori sampling resolution with genetic and demographic exploration is required to evaluate the present status of such bee species.

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.

Selective area doping for Mott neuromorphic electronics.

The cointegration of artificial neuronal and synaptic devices with homotypic materials and structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate experimental realization of vanadium dioxide (VO2) artificial neurons and synapses on the same substrate through selective area carrier doping. By locally configuring pairs of catalytic and inert electrodes that enable nanoscale control over carrier density, volatility or nonvolatility can be appropriately assigned to each two-terminal Mott memory device per lithographic design, and both neuron- and synapse-like devices are successfully integrated on a single chip. Feedforward excitation and inhibition neural motifs are demonstrated at hardware level, followed by simulation of network-level handwritten digit and fashion product recognition tasks with experimental characteristics. Spatially selective electron doping opens up previously unidentified avenues for integration of emerging correlated semiconductors in electronic device technologies.

How Seafood Wholesale Markets Matter for Urban Food Security: Evidence from Chennai, India.

Urban sites gather poverty in particular locations and often require bulk food system approaches for addressing prevalent food security and nutrition needs. The food systems that service them are, however, characterized by perishability and large irregularities in supply. Seafood is currently recognized as contributing in a major way to food security and nutrition, and it is to assessing the role of wholesale markets in meeting the needs of the urban poor that this paper is directed. It zooms in on the city of Chennai, India, where an estimated 40% of the population is considered poor and marine fish plays a crucial role in diets. Building on one-and-a-half years of field research in the pre-COVID-19 period, the paper analyses the performance of one of the city's largest fish wholesale markets, Vanagaram, in relation to the four commonly recognized pillars of food security. Results demonstrate how urban food systems function as major suppliers of fish (and other food items) to thousands of low- and middle-income households. Most importantly, this case study demonstrates the crucial role that is played by wholesale markets in merging low-price fish supplies from different geographic regions and thereby ensuring food security of poorer inhabitants.

Dans les zones urbaines, la pauvreté se concentre dans des sites spécifiques qui nécessitent souvent des systèmes alimentaires qui s'appuient sur la vente en gros pour répondre aux besoins en matière de sécurité alimentaire et d'apport nutritionnel. Les systèmes alimentaires qui les desservent sont cependant caractérisés par la périssabilité des aliments et de grandes irrégularités dans l'approvisionnement. Actuellement, il est reconnu que les produits de la mer contribuent de façon significative à la sécurité alimentaire et aux apports nutritionnels. Cet article cherche à évaluer le rôle du marché de la vente en gros dans la satisfaction des besoins des personnes en situation de pauvreté dans les villes. Il se concentre sur la ville de Chennai, en Inde, où environ 40% de la population est considérée comme étant en situation de pauvreté et où les poissons de mer jouent un rôle crucial dans l'alimentation. Cet article s'appuie sur des recherches menées sur le terrain pendant un an et demi avant la COVID-19, et analyze la performance de l'un des plus grands marchés de vente en gros de poisson de la ville, Vanagaram, par rapport aux quatre piliers communément reconnus de la sécurité alimentaire. Les résultats montrent la façon dont les systèmes alimentaires urbains fonctionnent comme d'importants fournisseurs de poisson (et d'autres produits alimentaires) pour des milliers de ménages à revenu faible ou moyen. Plus important encore, cette étude de cas démontre le rôle crucial que jouent les marchés de vente en gros pour faire fusionner l'approvisionnement en poisson à bas prix issu de différentes zones géographiques et pour garantir la sécurité alimentaire des habitant·e·s les plus pauvres.

Complex Oxides for Brain-Inspired Computing: A Review.

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Atomic Cross-Talk at the Interface: Enhanced Lubricity and Wear and Corrosion Resistance in Sub 2 nm Hybrid Overcoats via Strengthened Interface Chemistry.

Friction, wear, and corrosion remain the major causes of premature failure of diverse systems including hard-disk drives (HDDs). To enhance the areal density of HDDs beyond 1 Tb/in2, the necessary low friction and high wear and corrosion resistance characteristics with sub 2 nm overcoats remain unachievable. Here we demonstrate that atom cross-talk not only manipulates the interface chemistry but also strengthens the tribological and corrosion properties of sub 2 nm overcoats. High-affinity (HA) atomically thin (∼0.4 nm) interlayers (ATIs, XHA), namely Ti, Si, and SiNx, are sandwiched between the hard-disk media and 1.5 nm thick carbon (C) overlayer to develop interface-enhanced sub 2 nm hybrid overcoats that consistently outperform a thicker conventional commercial overcoat (≥2.7 nm), with the C/SiNx bilayer overcoat bettering all other <2 nm thick overcoats. These hybrid overcoats can enable the development of futuristic 2-4 Tb/in2 areal density HDDs and can transform various moving-mechanical-system based technologies.

Protein-Ligand Binding Free-Energy Calculations with ARROW─A Purely First-Principles Parameterized Polarizable Force Field.

Protein-ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)─a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein-ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ∼0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.

Stability and Metastability of Liquid Water in a Machine-Learned Coarse-Grained Model with Short-Range Interactions.

Coarse-grained water models are ∼100 times more efficient than all-atom models, enabling simulations of supercooled water and crystallization. The machine-learned monatomic model ML-BOP reproduces the experimental equation of state (EOS) and ice-liquid thermodynamics at 0.1 MPa on par with the all-atom TIP4P/2005 and TIP4P/Ice models. These all-atom models were parametrized using high-pressure experimental data and are either accurate for water's EOS (TIP4P/2005) or ice-liquid equilibrium (TIP4P/Ice). ML-BOP was parametrized from temperature-dependent ice and liquid experimental densities and melting data at 0.1 MPa; its only pressure training is from compression of TIP4P/2005 ice at 0 K. Here we investigate whether ML-BOP replicates the experimental EOS and ice-water thermodynamics along all pressures of ice I. We find that ML-BOP reproduces the temperature, enthalpy, entropy, and volume of melting of hexagonal ice up to 400 MPa and the EOS of water along the melting line with an accuracy that rivals that of both TIP4P/2005 and TIP4P/Ice. We interpret that the accuracy of ML-BOP originates from its ability to capture the shift between compact and open local structures to changes in pressure and temperature. ML-BOP reproduces the sharpening of the tetrahedral peak of the pair distribution function of water upon supercooling, and its pressure dependence. We characterize the region of metastability of liquid ML-BOP with respect to crystallization and cavitation. The accessibility of ice crystallization to simulations of ML-BOP, together with its accurate representation of the thermodynamics of water, makes it promising for investigating the interplay between anomalies, glass transition, and crystallization under conditions challenging to access through experiments.

Machine learning overcomes human bias in the discovery of self-assembling peptides.

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.