The digital lab manager: Automating research support.

Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph.

Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry.

In this study, we present a question answering (QA) system for chemistry, named Marie, with the use of a text-to-text pretrained language model to attain accurate data retrieval. The underlying data store is "The World Avatar" (TWA), a general world model consisting of a knowledge graph that evolves over time. TWA includes information about chemical species such as their chemical and physical properties, applications, and chemical classifications.

A dynamic knowledge graph approach to distributed self-driving laboratories.

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph.

Chemical Species Ontology for Data Integration and Knowledge Discovery.

Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, a semantic database for chemical species is indispensable for its ability to interrelate and infer relationships, enabling a more precise analysis and prediction of chemical behavior. This paper presents OntoSpecies, a web ontology designed to represent chemical species and their properties.

Marie and BERT-A Knowledge Graph Embedding Based Question Answering System for Chemistry.

This paper presents a novel knowledge graph question answering (KGQA) system for chemistry, which is implemented on hybrid knowledge graph embeddings, aiming to provide fact-oriented information retrieval for chemistry-related research and industrial applications. Unlike other existing designs, the system operates on multiple embedding spaces, which use various embedding methods and queries the embedding spaces in parallel. With the answers returned from multiple embedding spaces, the system leverages a score alignment model to adjust the answer scores and rerank the answers.

OntoPESScan: An Ontology for Potential Energy Surface Scans.

In this work, a new OntoPESScan ontology is developed for the semantic representation of one-dimensional potential energy surface (PES) scans, a central concept in computational chemistry. This ontology is developed in line with knowledge graph principles and The World Avatar (TWA) project. OntoPESScan is linked to other ontologies for chemistry in TWA, including OntoSpecies, which helps uniquely identify species along the PES and access their properties, and OntoCompChem, which allows the association of potential energy surfaces with quantum chemical calculations and the concepts used to derive them.

Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation.

Passing knowledge from human to human is a natural process that has continued since the beginning of humankind. Over the past few decades, we have witnessed that knowledge is no longer passed only between humans but also from humans to machines. The latter form of knowledge transfer represents a cornerstone in artificial intelligence (AI) and lays the foundation for knowledge engineering (KE).

Automated Rational Design of Metal-Organic Polyhedra.

Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs.

From Platform to Knowledge Graph: Evolution of Laboratory Automation.

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science.

Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis.

In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.

π-Diradical Aromatic Soot Precursors in Flames.

Soot emitted from incomplete combustion of hydrocarbon fuels contributes to global warming and causes human disease. The mechanism by which soot nanoparticles form within hydrocarbon flames is still an unsolved problem in combustion science. Mechanisms proposed to date involving purely chemical growth are limited by slow reaction rates, whereas mechanisms relying on solely physical interactions between molecules are limited by weak intermolecular interactions that are unstable at flame temperatures.

Question Answering System for Chemistry.

This paper describes the implementation and evaluation of a proof-of-concept Question Answering (QA) system for accessing chemical data from knowledge graphs (KGs) which offer data from chemical kinetics to the chemical and physical properties of species. We trained the question classification and named the entity recognition models that specialize in interpreting chemistry questions. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation to handle ontologies with different structures.

Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology.

In this paper, we develop a knowledge graph-based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene)dimethyl ether (PODE, where = 3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park simulator (JPS, theworldavatar.com), an existing cross-domain knowledge graph.

Multiscale Cross-Domain Thermochemical Knowledge-Graph.

In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software.

Reactivity of Polycyclic Aromatic Hydrocarbon Soot Precursors: Kinetics and Equilibria.

The thermodynamics and kinetics of cross-linking reactions between PAHs of various reactive edge types that are observed in soot precursors are explored using density functional theory. The forward rate constants confirm that reactions involving aryl σ-radicals are faster than others, but rate constants for reactions between aryl σ-radicals and localized π-radicals can be as large or even larger than for two aryl σ-radicals. However, rates for all cross-linking reactions between small PAHs are likely too slow to explain soot formation.

Knowledge Graph Approach to Combustion Chemistry and Interoperability.

In this paper, we demonstrate through examples how the concept of a Semantic Web based knowledge graph can be used to integrate combustion modeling into cross-disciplinary applications and in particular how inconsistency issues in chemical mechanisms can be addressed. We discuss the advantages of linked data that form the essence of a knowledge graph and how we implement this in a number of interconnected ontologies, specifically in the context of combustion chemistry. Central to this is OntoKin, an ontology we have developed for capturing both the content and the semantics of chemical kinetic reaction mechanisms.

OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms.

An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behavior of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process.

Optical band gap of cross-linked, curved, and radical polyaromatic hydrocarbons.

In this work, the optical band gaps of polycyclic aromatic hydrocarbons (PAHs) crosslinked via an aliphatic bond, curved via pentagon integration and with radical character were computed using density functional theory. A variety of different functionals were benchmarked against optical band gaps (OBGs) measured by ultraviolet-visible spectroscopy with HSE06 being most accurate with a percentage error of 6% for a moderate basis set. Pericondensed aromatics with different symmetries were calculated with this improved functional providing new scaling relationships for the OBG versus size.

An Ontology and Semantic Web Service for Quantum Chemistry Calculations.

The purpose of this article is to present an ontology, termed OntoCompChem, for quantum chemistry calculations as performed by the Gaussian quantum chemistry software, as well as a semantic web service named MolHub. The OntoCompChem ontology has been developed based on the semantics of concepts specified in the CompChem convention of Chemical Markup Language (CML) and by extending the Gainesville Core (GNVC) ontology. MolHub is developed in order to establish semantic interoperability between different tools used in quantum chemistry and thermochemistry calculations, and as such is integrated into the J-Park Simulator (JPS)-a multidomain interactive simulation platform and expert system.

Improved methodology for performing the inverse Abel transform of flame images for color ratio pyrometry.

A new method is presented for performing the Abel inversion by fitting the line-of-sight projection of a predefined intensity distribution (FLiPPID) to the recorded 2D projections. The aim is to develop a methodology that is less prone to experimental noise when analyzing the projection of axisymmetric objects-in this case, co-flow diffusion flame images for color ratio pyrometry. A regression model is chosen for the light emission intensity distribution of the flame cross section as a function of radial distance from the flame center line.

Polymorphism of nanocrystalline TiO prepared in a stagnation flame: formation of the TiO-II phase.

A metastable "high-pressure" phase known as α-PbO-type TiO or TiO-II is prepared a single-step synthesis using a laminar premixed stagnation flame. Three other TiO polymorphs, namely anatase, rutile and TiO-B phases, can also be obtained by tuning the oxygen/fuel ratio. TiO-II is observed as a mixture with rutile under oxygen-lean flame conditions.

First-Principles Thermochemistry for the Thermal Decomposition of Titanium Tetraisopropoxide.

The thermal decomposition of titanium tetraisopropoxide (TTIP) is investigated using quantum chemistry, statistical thermodynamics, and equilibrium composition analysis. A set of 981 Ti-containing candidate species are proposed systematically on the basis of the thermal breakage of bonds within a TTIP molecule. The ground state geometry, vibrational frequencies and hindrance potentials are calculated for each species at the B97-1/6-311+G(d,p) level of theory.

Comparative incidences of decompression illness in repetitive, staged, mixed-gas decompression diving: is 'dive fitness' an influencing factor?

Wreck diving at Bikini Atoll consists of a relatively standard series of decompression dives with maximum depths in the region of 45-55 metres' sea water (msw). In a typical week of diving at Bikini, divers can perform up to 12 decompression dives to these depths over seven days; on five of those days, divers can perform two decompression dives per day. All the dives employ multi-level, staged decompression schedules using air and surface-supplied nitrox containing 80% oxygen.

Proviral position effects: possible probes for genes that suppress transcription.

Rous sarcoma virus, an oncogenic avian retrovirus, readily causes morphological transformation of chick cells, but in infected rat cells transformation is rare because proviral transcription is inefficient. This constraint is not due to a lack of positive transcriptional factors, or an excess of negative ones, but reflects the site of proviral integration in rat cell DNA. In most sites the provirus is almost invariably silent, in others it is correspondingly active, whilst in a third category expression fluctuates in concert with transitions in chromatin structure.

Transcription of Rous sarcoma proviruses in rat cells is determined by chromosomal position effects that fluctuate and can operate over long distances.

Most Rous sarcoma proviruses integrated in rat DNA are inactive. The active minority are not necessarily a consequence of long terminal repeat mutations and their transcription can fluctuate in parallel with previously demonstrated transitions in chromatin configuration. Transcriptional alternations cannot be attributed to variations in positive or negative factors that operate independently of the site of provirus integration.