Uncertainty Quantification and Reduction of Molecular Dynamics Models

Molecular dynamics (MD) is an important method underlying the modern field of Computational Materials Science. Without requiring prior knowledge as inputs, MD simulations have been used to study a variety of material problems. However, results of molecular dynamics simulations are often associated with errors as compared with experimental observations. These errors come from a variety of sources, including inaccuracy of interatomic potentials, short length and time scales, idealized problem description and statistical uncertainties of MD simulations themselves. This chapter specifically devotes to the statistical uncertainties of MD simulations. In particular, methods to quantify and reduce such statistical uncertainties are demonstrated using a variety of exemplar cases, including calculations of finite temperature static properties such as lattice constants, cohesive energies, elastic constants, dislocation energies, thermal conductivities, surface segregation and calculations of kinetic properties such as diffusion parameters. We also demonstrate that when the statistical uncertainties are reduced to near zero, MD can be used to validate and improve widely used theories

RORS: Enhanced Rule-based OWL Reasoning on Spark

The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. The performance of the rule-based OWL reasoning is often sensitive to the rule execution order. In this paper, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. Firstly, we divide all rules (27 in total) into four main classes, namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and schema rules (8 rules) since, as we investigated, those triples corresponding to the first three classes of rules are overwhelming (e.g., over 99% in the LUBM dataset) in our practical world. Secondly, based on the interdependence among those entailment rules in each class, we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules. Finally, we implement the new rule execution order on Spark in a prototype called RORS. The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).Comment: 12 page

An Approach to Generating Arguments over DL-Lite Ontologies

Argumentation frameworks for ontology reasoning and management have received extensive interests in the field of artificial intelligence in recent years. As one of the most popular argumentation frameworks, Besnard and Hunter's framework is built on arguments in form of where Phi is consistent and minimal for entailing phi. However, the problem about generating arguments over ontologies is still open. This paper presents an approach to generating arguments over DL-Lite ontologies by searching support paths in focal graphs. Moreover, theoretical results and examples are provided to ensure the correctness of this approach. Finally, we show this approach has the same complexity as propositional revision

Apparatus and method for intra-layer modulation of the material deposition and assist beam and the multilayer structure produced therefrom

A method of producing a multilayer structure that has reduced interfacial roughness and interlayer mixing by using a physical-vapor deposition apparatus. In general the method includes forming a bottom layer having a first material wherein a first plurality of monolayers of the first material is deposited on an underlayer using a low incident adatom energy. Next, a second plurality of monolayers of the first material is deposited on top of the first plurality of monolayers of the first material using a high incident adatom energy. Thereafter, the method further includes forming a second layer having a second material wherein a first plurality of monolayers of the second material is deposited on the second plurality of monolayers of the first material using a low incident adatom energy. Next, a second plurality of monolayers of the second material is deposited on the first plurality of monolayers of the second material using a high incident adatom energy

A Triangular Personalized Recommendation Algorithm for Improving Diversity

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods