Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic‐structure methods ...such as density‐functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by “learning” electronic‐structure data, ML‐based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase‐change materials for memory devices; nanoparticle catalysts; and carbon‐based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML‐based interatomic potentials in diverse areas of materials research.
Machine‐learning‐driven simulations are emerging as a powerful addition to the toolkit of materials modeling, giving atomic‐scale insight that would otherwise be inaccessible. This Progress Report provides a brief introduction to the new methods, highlights a few example applications in the broad field of materials science, and looks toward the future.
By combining different allotropic forms of carbon at the nanoscale it is possible to fabricate tailor made surfaces with unique properties. These novel materials have shown high potential especially ...in the electrochemical detection of different biomolecules, such as dopamine, glutamate and ascorbic acid, which are important neurotransmitters in the mammalian central nervous system. Thus, more information about their material properties must be obtained in order to realize their high potential to the maximum. The results presented in this review clearly point out that although there is an extensive amount of data available on the structural, chemical and electrochemical properties on different carbon nanoforms, the data are scattered, often inconsistent and even contradictory. Hybrid carbon nanomaterials are much less investigated than the individual allotropes, but based on the existing data they possess extremely interesting electrochemical properties. Thus, it is of utmost importance to carry out extensive step-by-step characterization of these materials by utilizing combination of detailed computational and experimental work. In this way it will become possible to avoid approaches to material design that are based solely on trial-and-error approach, which has, unfortunately, been more a rule than an exception.
Abstract
Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an outstanding challenge. ...Here, we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum-mechanical results. Our model is fitted to density-functional theory plus many-body dispersion (DFT + MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of “phosphorene” and “hittorfene”); its transferability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale exemplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chemistry, physics, and materials science.
We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor Bartók et al., Phys. Rev. B 87, 184115 (2013).. Our aim is to improve ...the computational efficiency of SOAP-based similarity kernel construction. While these improved atomic descriptors can be used for general characterization and interpolation of atomic properties, their main target application is accelerated evaluation of machine-learning-based interatomic potentials within the Gaussian approximation potential (GAP) framework Bartók et al., Phys. Rev. Lett. 104, 136403 (2010). We achieve this objective by expressing the atomic densities in an approximate separable form, which decouples the radial and angular channels. We then express the elements of the SOAP descriptor (i.e., the expansion coefficients for the atomic densities) in analytical form given a particular choice of radial basis set. Finally, we derive recursion formulas for the expansion coefficients. This new SOAP-based descriptor allows for tenfold speedups compared to previous implementations, while improving the stability of the radial expansion for distant atomic neighbors, without degradation of the interpolation power of GAP models.
High entropy alloys (HEA) have unique properties including the potential to be radiation tolerant. These materials with extreme disorder could resist damage because disorder, stabilized by entropy, ...is the equilibrium thermodynamic state. Disorder also reduces electron and phonon conductivity keeping the damage energy longer at the deposition locations, eventually favoring defect recombination. In the short time-scales related to thermal spikes induced by collision cascades, phonons become the relevant energy carrier. In this work, we perform a systematic study of phonon thermal conductivity in multiple component solid solutions represented by Lennard-Jones (LJ) potentials. We explore the conditions that minimize phonon mean free path via extreme alloy complexity, by varying the composition and the elements (differing in mass, atomic radii, and cohesive energy). We show that alloy complexity can be tailored to modify the scattering mechanisms that control energy transport in the phonon subsystem. Our analysis provides a qualitative guidance for the selection criteria used in the design of HEA alloys with low phonon thermal conductivity.
•We show coupling between phonon scattering sources in multicomponent alloys.•We find criteria for combination of atomic masses and sizes to maximize scattering.•We show that more alloy components does not necessarily reduce conductivity.
Entropy in molecular recognition by proteins Caro, José A.; Harpole, Kyle W.; Kasinath, Vignesh ...
Proceedings of the National Academy of Sciences - PNAS,
06/2017, Letnik:
114, Številka:
25
Journal Article
Recenzirano
Odprti dostop
Molecular recognition by proteins is fundamental to molecular biology. Dissection of the thermodynamic energy terms governing protein–ligand interactions has proven difficult, with determination of ...entropic contributions being particularly elusive. NMR relaxation measurements have suggested that changes in protein conformational entropy can be quantitatively obtained through a dynamical proxy, but the generality of this relationship has not been shown. Twenty-eight protein–ligand complexes are used to show a quantitative relationship between measures of fast side-chain motion and the underlying conformational entropy. We find that the contribution of conformational entropy can range from favorable to unfavorable, which demonstrates the potential of this thermodynamic variable to modulate protein–ligand interactions. For about one-quarter of these complexes, the absence of conformational entropy would render the resulting affinity biologically meaningless. The dynamical proxy for conformational entropy or “entropy meter” also allows for refinement of the contributions of solvent entropy and the loss in rotational-translational entropy accompanying formation of high-affinity complexes. Furthermore, structure-based application of the approach can also provide insight into long-lived specific water–protein interactions that escape the generic treatments of solvent entropy based simply on changes in accessible surface area. These results provide a comprehensive and unified view of the general role of entropy in high-affinity molecular recognition by proteins.
This study evaluated the potential of using poly(lactic acid)/poly(s-caprolactone) (PLA/PCL) blends for fused filament fabrication (FFF) and assembly with pure PLA for biomedical applications. ...PLA/PCL binary blends were meltblended in a twin-screw extruder at different ratios (20/80 to 80/20) and then formed into filaments with a calibrated diameter for FFF. The microstructure, surface properties, and rheological and mechanical behaviors of the blends were assessed. The blends were immiscible but showed signs of adhesion between the phases. It was determined that the fibrillar morphology of inclusions for PLA/PCL ratios higher than 30/70 proved to be driven by the manufacturing process. The tensile mechanical behaviors of printed and injected samples were similar, and their Young's modulus was simulated using Halpin-Tsai and Mori Tanaka models based on the sample microstructure. The ductility of the blends was strongly driven by the behavior of its majority phase. Finally, specific samples were designed to characterize the tensile strength between PLA and its blends by entangling layers of both materials. The strength of the assembly was found to be dependent on the phase that was continuous and was governed by the strength and the viscosity of the blend.
Swarm intelligence is a relatively novel field. It addresses the study of the collective behaviors of systems made by many components that coordinate using decentralized controls and ...self-organization. A large part of the research in swarm intelligence has focused on the reverse engineering and the adaptation of collective behaviors observed in natural systems with the aim of designing effective algorithms for distributed optimization. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. These are key properties in the context of network routing, and in particular of routing in wireless sensor networks. Therefore, in the last decade, a number of routing protocols for wireless sensor networks have been developed according to the principles of swarm intelligence, and, in particular, taking inspiration from the foraging behaviors of ant and bee colonies. In this paper, we provide an extensive survey of these protocols. We discuss the general principles of swarm intelligence and of its application to routing. We also introduce a novel taxonomy for routing protocols in wireless sensor networks and use it to classify the surveyed protocols. We conclude the paper with a critical analysis of the status of the field, pointing out a number of fundamental issues related to the (mis) use of scientific methodology and evaluation procedures, and we identify some future research directions.
Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary ...to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp2 motifs with markedly different reactivities. We therefore draw a comprehensive, both qualitative and quantitative, picture of the surface chemistry of a-C and its reactivity toward −H, −O, −OH, and −COOH. While this paper focuses on a-C surfaces, the presented methodology opens up a new systematic and general way to study the surface chemistry of amorphous and disordered materials.
Carbonaceous materials, especially tetrahedral amorphous carbon (ta-C), can form complex functionalized surface structures and are thus promising candidates for applications in biomedical devices and ...electrochemistry. Functional groups at ta-C surfaces have been widely studied by spectroscopic techniques; however, interpretation of the experimental data is extremely difficult, especially in the case of X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The assignments of experimental XPS and XAS signals are normally based on references obtained from molecular or crystalline samples, which are simplified approximations for the far more complex amorphous structures. Here, we use extensive density functional theory (DFT) simulations to predict XAS and XPS signatures for carbon-based materials in more realistic environments, building on large data sets of structural models generated by a machine-learning (ML) interatomic potential. The results indicate clear signatures: individual fingerprint XAS spectra and distinctive XPS binding energy distributions, both in terms of center and broadness of the signal, for chemically different groups. The results point out what kind of structural information can and cannot be extracted with X-ray spectroscopy. This study will enable a deeper physicochemical understanding of experimental data and ultimately theory-based identification and quantification of functional groups in carbonaceous materials.
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