The structure of binary Lennard-Jones clusters: The effects of atomic size ratio

We introduce a global optimization approach for binary clusters that for a given cluster size is able to directly search for the structure and composition that has the greatest stability. We apply this approach to binary Lennard-Jones clusters, where the strength of the interactions between the two atom types is the same, but where the atoms have different sizes. We map out how the most stable structure depends on the cluster size and the atomic size ratio for clusters with up to 100 atoms and up to 30% difference in atom size. A substantial portion of this parameter space is occupied by structures that are polytetrahedral, both those that are polyicosahedral and those that involve disclination lines. Such structures involve substantial strains for one-component Lennard-Jones clusters, but can be stabilized by the different-sized atoms in the binary clusters. These structures often have a `core-shell' geometry, where the larger atoms are on the surface, and the smaller atoms are in the core.Comment: 13 pages, 9 figure

Hiking in the energy landscape in sequence space: a bumpy road to good folders

With the help of a simple 20 letters, lattice model of heteropolymers, we investigate the energy landscape in the space of designed good-folder sequences. Low-energy sequences form clusters, interconnected via neutral networks, in the space of sequences. Residues which play a key role in the foldability of the chain and in the stability of the native state are highly conserved, even among the chains belonging to different clusters. If, according to the interaction matrix, some strong attractive interactions are almost degenerate (i.e. they can be realized by more than one type of aminoacid contacts) sequence clusters group into a few super-clusters. Sequences belonging to different super-clusters are dissimilar, displaying very small ($\approx 10$) similarity, and residues in key-sites are, as a rule, not conserved. Similar behavior is observed in the analysis of real protein sequences.Comment: 17 pages 5 figures Corrected typos added auxiliary informatio

Structures, Energetics, and Reaction Barriers for CH_x Bound to the Nickel (111) Surface

To provide a basis for understanding and improving such reactive processes on nickel surfaces as the catalytic steam reforming of hydrocarbons, the decomposition of hydrocarbons at fuel cell anodes, and the growth of carbon nanotubes, we report quantum mechanics calculations (PBE flavor of density functional theory, DFT) of the structures, binding energies, and reaction barriers for all CH_x species on the Ni(111) surface using periodically infinite slabs. We find that all CH_x species prefer binding to μ3 (3-fold) sites leading to bond energies ranging from 42.7 kcal/mol for CH_3 to 148.9 kcal/mol for CH (the number of Ni-C bonds is not well-defined). We find reaction barriers of 18.3 kcal/mol for CH_(3,ad) → CH_(2,ad) + H_(ad) (with ΔE = +1.3 kcal/ mol), 8.2 kcal/mol for CH_(2,ad) → CH_(ad) + H_(ad) (with ΔE = -10.2 kcal/mol) and 32.3 kcal/mol for CH_(ad) → C_(ad) + H_(ad) (with ΔE = 11.6 kcal/mol). Thus, CH_(ad) is the stable form of CH_x on the surface. These results are in good agreement with the experimental data for the thermodynamic stability of small hydrocarbon species following dissociation of methane on Ni(111) and with the intermediates isolated during the reverse methanation process

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks.

Data-driven machine learning force fields (MLFs) are more and more popular in atomistic simulations and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models, and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence, a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high-dimensional neural network potential (HDNNP) on Pt clusters of sizes from 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, and then, a more accurate but expensive level, using a hybrid functional or nonlocal vdW functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error <10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functionals. The overall speedup can be as large as 900 for a 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of the delta potential energy surface, and accordingly, one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multilayer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive nonlocal vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles

Task automation through email data analysis

Currently, many companies do not use the information contained in their emails, yet it is a data set that is full of information and could be very useful. This thesis report focuses on email data analysis and task automation, particularly in the area of email-based process mining. The state of the art section reviews existing research on extracting information from email content using techniques such as lexical analysis, language detection, semantic analysis and machine learning methods. It explores different areas of process mining, including process pattern discovery, anomaly discovery, and process extraction from texts. The objectives of this research are to assess the feasibility of extracting candidate processes from emails, to develop human-understandable metrics to classify processes, to propose a system to identify automation opportunities in email templates and explore possibilities for automation in email interactions. To do this, we carried out different steps such as data preparation, chains detection, text representation, distance matrix calculation and grouping methods

Seismicity relocation and fault structure near the Leech River Fault Zone, southern Vancouver Island

Relatively low rates of seismicity and fault loading have made it challenging to correlate microseismicity to mapped surface faults on the forearc of southern Vancouver Island. Here we use precise relocations of microsciesmicity integrated with existing geologic data, to present the first identification of subsurface seismogenic structures associated with the Leech River fault zone (LRFZ) on southern Vancouver Island. We used HypoDD double difference relocation method to relocate 1253 earthquakes reported by the Canadian National Seismograph Network (CNSN) catalog from 1985 to 2015. Our results reveal an ~8-10 km wide, NNE-dipping zone of seismicity representing a subsurface structure along the eastern 30 km of the terrestrial LRFZ and extending 20 km farther eastward offshore, where the fault bifurcates beneath the Juan de Fuca Strait. Using a clustering analysis we identify secondary structures within the NNE-dipping fault zone, many of which are sub-vertical and exhibit right-lateral strike-slip focal mechanisms. We suggest that the arrangement of these near-vertical dextral secondary structures within a more general NE-dipping fault zone, located well beneath (10-15 km) the Leech River fault (LRF) as imaged by LITHOPROBE, may be a consequence of the reactivation of this fault system as a right-lateral structure in the crust with pre-existing NNE-dipping foliations. Our results provide the first confirmation of active terrestrial crustal faults on Vancouver Island using a relocation method. We suggest that slowly slipping active crustal faults, especially in regions with pre-existing foliations, may result in microseismicity along fracture arrays rather than along single planar structures

Bridge helix bending promotes RNA polymerase II backtracking through a critical and conserved threonine residue.

The dynamics of the RNA polymerase II (Pol II) backtracking process is poorly understood. We built a Markov State Model from extensive molecular dynamics simulations to identify metastable intermediate states and the dynamics of backtracking at atomistic detail. Our results reveal that Pol II backtracking occurs in a stepwise mode where two intermediate states are involved. We find that the continuous bending motion of the Bridge helix (BH) serves as a critical checkpoint, using the highly conserved BH residue T831 as a sensing probe for the 3'-terminal base paring of RNA:DNA hybrid. If the base pair is mismatched, BH bending can promote the RNA 3'-end nucleotide into a frayed state that further leads to the backtracked state. These computational observations are validated by site-directed mutagenesis and transcript cleavage assays, and provide insights into the key factors that regulate the preferences of the backward translocation

Formation mechanism of ultra porous framework materials

Understanding the formation mechanism of ultra porous framework materials may lead to insights into strategies for the design and synthesis of novel ultra porous materials or for the increased surface area of known materials. Several potential formation mechanisms have been proposed based on experimental evidence. Here, we assess, via simulation of the network generation process, these mechanisms and have identified key processes by which network interpenetration is minimised and hence surface area is maximised