Machine learning prediction of the electronic property of binary transition metal alloys

Machine learning methods have garnered much attention and use in computational catalysis. Previous studies have demonstrated rapid and accurate prediction of a variety of catalytic properties as well as the underlying potential energy landscapes. In particular, d-band center, defined as the first moment of the d-projected density of states, has been widely used as the key descriptor of activity trends for reactions catalyzed by metal surfaces. In this work, we construct a gradient boosting regression (GBR) model for prediction of the d-band center of bulk binary transition metal alloys. An accurate model is obtained using a dataset of over 1200 alloys from the Materials Project database spanning the entire d-block of the periodic table. The d-band centers, periodic groups, and relative compositions of the constituent metals are determined to have the highest feature importance scores, consistent with the underlying physics of the alloy. The regression model presented here offer a promising strategy of rapid property prediction with physical interpretability to aid the optimization and discovery of efficient heterogeneous catalysts.

Hydrodynamic manipulation of nano-objects by optically induced thermo-osmotic flows

The manipulation of nano-objects at the microscale is of great technological significance to construct new functional materials, to manipulate tiny amounts of liquids, to reconfigure sensorial systems or to detect minute concentrations of analytes in medical screening. It is commonly approached by the generation of potential energy landscapes, for example, with optical fields or by using pressure driven microfluidics. Here we show that strong hydrodynamic boundary flows enable the trapping and manipulation of nano-objects near surfaces. These thermo-osmotic flows are induced by modulating the van der Waals interaction at a solid-liquid interface with optically generated temperature fields. We use a thin gold film on a glass substrate to provide localized but reconfigurable point-like optical heating. Convergent boundary flows with velocities of tens of micrometres per second are observed and substantiated by a quantitative physical model. The hydrodynamic forces acting on suspended nanoparticles and attractive van der Waals or depletion induced forces enable precise positioning and guiding of the nanoparticles. Fast multiplexing of flow fields further provides the means for parallel manipulation of many nano-objects and the generation of complex flow fields. Our findings have direct consequences for the field of plasmonic nano-tweezers as well as other thermo-plasmonic trapping schemes and pave the way for a general scheme of nanoscopic manipulation with boundary flows.

Anisotropy in mechanical unfolding of protein upon partner-assisted pulling and handle-assisted pulling

Proteins as force-sensors respond to mechanical cues and regulate signaling in physiology. Proteins commonly connect the source and response points of mechanical cues in two conformations, independent proteins in end-to-end geometry and protein complexes in handshake geometry. The force-responsive property of independent proteins in end-to-end geometry is studied extensively using single-molecule force spectroscopy (SMFS). The physiological significance of the complex conformations in force-sensing is often disregarded as mere surge protectors. However, with the potential of force-steering, protein complexes possess a distinct mechano-responsive property over individual force-sensors. To decipher, we choose a force-sensing protein, cadherin-23, from tip-link complex and perform SMFS using end-to-end geometry and handshake complex geometry. We measure higher force-resilience of cadherin-23 with preferential shorter extensions in handshake mode of pulling over the direct mode. The handshake geometry drives the force-response of cadherin-23 through different potential-energy landscapes than direct pulling. Analysis of the dynamic network structure of cadherin-23 under tension indicates narrow force-distributions among residues in cadherin-23 in direct pulling, resulting in low force-dissipation paths and low resilience to force. Overall, the distinct and superior mechanical responses of cadherin-23 in handshake geometry than single protein geometry highlight a probable evolutionary drive of protein-protein complexes as force-conveyors over independent ones.

Long-Range Atom–Ion Rydberg Molecule: A Novel Molecular Binding Mechanism

We present a novel binding mechanism where a neutral Rydberg atom and an atomic ion form a molecular bound state at a large internuclear distance. The binding mechanism is based on Stark shifts and level crossings that are induced in the Rydberg atom due to the electric field of the ion. At particular internuclear distances between the Rydberg atom and the ion, potential wells occur that can hold atom–ion molecular bound states. Apart from the binding mechanism, we describe important properties of the long-range atom–ion Rydberg molecule, such as its lifetime and decay paths, its vibrational and rotational structure, and its large dipole moment. Furthermore, we discuss methods of how to produce and detect it. The unusual properties of the long-range atom–ion Rydberg molecule give rise to interesting prospects for studies of wave packet dynamics in engineered potential energy landscapes.

Locomotor transitions in the potential energy landscape-dominated regime

To traverse complex three-dimensional terrain with large obstacles, animals and robots must transition across different modes. However, the most mechanistic understanding of terrestrial locomotion concerns how to generate and stabilize near-steady-state, single-mode locomotion (e.g. walk, run). We know little about how to use physical interaction to make robust locomotor transitions. Here, we review our progress towards filling this gap by discovering terradynamic principles of multi-legged locomotor transitions, using simplified model systems representing distinct challenges in complex three-dimensional terrain. Remarkably, general physical principles emerge across diverse model systems, by modelling locomotor–terrain interaction using a potential energy landscape approach. The animal and robots' stereotyped locomotor modes are constrained by physical interaction. Locomotor transitions are stochastic, destabilizing, barrier-crossing transitions on the landscape. They can be induced by feed-forward self-propulsion and are facilitated by feedback-controlled active adjustment. General physical principles and strategies from our systematic studies already advanced robot performance in simple model systems. Efforts remain to better understand the intelligence aspect of locomotor transitions and how to compose larger-scale potential energy landscapes of complex three-dimensional terrains from simple landscapes of abstracted challenges. This will elucidate how the neuromechanical control system mediates physical interaction to generate multi-pathway locomotor transitions and lead to advancements in biology, physics, robotics and dynamical systems theory.

Estimating DNA methylation potential energy landscapes from nanopore sequencing data

High-throughput third-generation sequencing devices, such as the Oxford Nanopore Technologies (ONT) MinION sequencer, can generate long reads that span thousands of bases. This new technology opens the possibility of considering a wide range of epigenetic modifications and provides the capability of interrogating previously inaccessible regions of the genome, such as highly repetitive regions, as well as of performing comprehensive allele-specific methylation analysis, among other applications. It is well-known, however, that detection of DNA methylation from nanopore data results in a substantially reduced per-read accuracy when comparing to WGBS, due to noise introduced by the sequencer and its underlying chemistry. It is therefore imperative that methods are developed for the reliable modeling and analysis of the DNA methylation landscape using nanopore data. Here we introduce such method that takes into account the presence of noise introduced by the ONT sequencer and, by using simulations, we provide evidence of its potential. The proposed approach establishes a solid foundation for the development of a comprehensive framework for the statistical analysis of DNA methylation, and possibly of other epigenetic marks, using third-generation sequencing.

Structural Aspects of the Ortho Chloro- and Fluoro- Substituted Benzoic Acids: Implications on Chemical Properties

This article presents a detailed comprehensive investigation of the ortho fluoro- and chloro- substituted benzoic acids both, as isolated molecules and in the crystalline phase. Quantum chemical calculations performed within the density functional theory (DFT) formalism are used to investigate the potential energy landscapes of the molecules, taking into special consideration the effects of the interactions between the carboxylic group and the ortho halogen substituents, as well as the nature of these later on the structure and properties of the investigated systems. The structures of the relevant conformers of the molecules are discussed in comparative terms, and used to rationalize experimental data obtained for the compounds in the gas phase and isolated in low-temperature inert matrices. The UV-induced photofragmentation reactions of two of the compounds isolated in cryogenic inert matrices were studied as illustrative cases. The structures of the crystals reported previously in the literature are revisited and discussed also in a comparative basis. Particular emphasis is given to the analysis of the intermolecular interactions in the different crystals, using Hirshfeld surface analysis, the CE-B3LYP energy decomposition model and the HOMA index, and to their correlation with thermodynamic data.