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.

Surface premelting of ice far below the triple point.

Premelting of ice, a quasi-liquid layer (QLL) at the surface below the melting temperature, was first postulated by Michael Faraday 160 y ago. Since then, it has been extensively studied theoretically and experimentally through many techniques. Existing work has been performed predominantly on hexagonal ice, at conditions close to the triple point. Whether the same phenomenon can persist at much lower pressure and temperature, where stacking disordered ice sublimates directly into water vapor, remains unclear. Herein, we report direct observations of surface premelting on ice nanocrystals below the sublimation temperature using transmission electron microscopy (TEM). Similar to what has been reported on hexagonal ice, a QLL is found at the solid-vapor interface. It preferentially decorates certain facets, and its thickness increases as the phase transition temperature is approached. In situ TEM reveals strong diffusion of the QLL, while electron energy loss spectroscopy confirms its amorphous nature. More significantly, the premelting observed in this work is thought to be related to the metastable low-density ultraviscous water, instead of ambient liquid water as in the case of hexagonal ice. This opens a route to understand premelting and grassy liquid state, far away from the normal water triple point.

Unusual High Hardness and Load-Dependent Mechanical Characteristics of Hydrogenated Carbon-Nitrogen Hybrid Films.

Mechanical components are exposed to a rigorous environment in a number of applications including engineering, aerospace, and automobiles. Thus, their service lifetime and reliability are always on the verge of risk. Protective coatings with high hardness are required to enhance their service lifetime and minimize the replacement cost and waste burden. Hydrogenated amorphous carbon including nitrogen-incorporated films, that are commonly deposited by plasma-enhanced chemical vapor deposition, are widely used for commercial protective coating applications. However, their mechanical hardness still falls into the moderate hard regime. This needs to be substantially enhanced for advanced applications. Here, we report the synthesis of very hard nanostructured hydrogenated carbon-nitrogen hybrid (n-C:H:N) films. The optimized n-C:H:N film displays a hardness of about 36 GPa, elastic modulus of 360 GPa, and reasonably good elastic recovery (ER) of 62.7%. The mechanical properties of n-C:H:N films are further tailored when nitrogen pressure is tuned during the growth. The realized remarkably improved mechanical properties are correlated with the films' structural properties and experimental growth conditions. We also conducted density functional theory calculations that show the trend for the elastic modulus of the amorphous carbon films with varying nitrogen concentrations matches well with experimentally measured values. Finally, we probed load-dependent mechanical properties of n-C:H:N films and found an anomalous behavior; some of the mechanical parameters, for instance, ER, reveal an irregular trend with indentation load, which we explain in the framework of the film-substrate composite concept. Overall, this work uncovers many unknown and exciting mechanical phenomena that could pave the way for new technological developments.

Reconfigurable perovskite nickelate electronics for artificial intelligence.

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

Accurate determination of solvation free energies of neutral organic compounds from first principles.

The main goal of molecular simulation is to accurately predict experimental observables of molecular systems. Another long-standing goal is to devise models for arbitrary neutral organic molecules with little or no reliance on experimental data. While separately these goals have been met to various degrees, for an arbitrary system of molecules they have not been achieved simultaneously. For biophysical ensembles that exist at room temperature and pressure, and where the entropic contributions are on par with interaction strengths, it is the free energies that are both most important and most difficult to predict. We compute the free energies of solvation for a diverse set of neutral organic compounds using a polarizable force field fitted entirely to ab initio calculations. The mean absolute errors (MAE) of hydration, cyclohexane solvation, and corresponding partition coefficients are 0.2 kcal/mol, 0.3 kcal/mol and 0.22 log units, i.e. within chemical accuracy. The model (ARROW FF) is multipolar, polarizable, and its accompanying simulation stack includes nuclear quantum effects (NQE). The simulation tools' computational efficiency is on a par with current state-of-the-art packages. The construction of a wide-coverage molecular modelling toolset from first principles, together with its excellent predictive ability in the liquid phase is a major advance in biomolecular simulation.

Learning in continuous action space for developing high dimensional potential energy models.

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

Learning with Delayed Rewards-A Case Study on Inverse Defect Design in 2D Materials.

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS2, we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS2 are discussed. Overall, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.