Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Machine Learning Assisted Discovery of Shape Memory Polymers and Their Thermomechanical Modeling

As a new class of smart materials, shape memory polymer (SMP) is gaining great attention in both academia and industry. One challenge is that the chemical space is huge, while the human intelligence is limited, so that discovery of new SMPs becomes more and more difficult. In this dissertation, by adopting a series of machine learning (ML) methods, two frameworks are established for discovering new thermoset shape memory polymers (TSMPs). Specifically, one of them is performed by a combination of four methods, i.e., the most recently proposed linear notation BigSMILES, supplementing existing dataset by reasonable approximation, a mixed dimension (1D and 2D) input model and a type of dual-convolutional-neural-network model. The framework is able to predict the recovery stress and glass transition temperature for TSMP and screen 14 new TSMPs from a large chemical space. The other leverages transfer learning, variational autoencoder and weighted vector combination method, and the developed ML framework can design ultraviolet (UV) curable TSMPs with desired properties. With new SMPs discovered by ML, as well as other new SMPs continuously developed in the labs, there is an urgent need to develop thermomechanical models so that the new SMPs can be used in structural design. Through the framework of solid mechanics, three different constitutive models are presented for classical one-way thermoset shape memory polymer (TSMP), two-way semi-crystalline SMP and enthalpy-driven four-chain SMP with large recovery stress, respectively. Among them, a new two-phase sphere model based on the physical growth process of the frozen phase from nuclei is proposed, which tends to bring more underly physical mechanism for the classical storage strain-based phase transition model. By introducing Gibbs energy and a transition of the molecule deformation mechanism, a enthalpy-driven thermomechanical model with new representative unit cell is developed, which could reasonably elucidates the large recovery stress for a new branch of TSMPs. Multiple mechanisms, involving phase transition law, damage evolution, and relaxation are introduced into the model for two-way semi-crystalline SMP, which is able to reveal the mechanisms of three different 2W-SMEs

Tax policy in the european union, A review of issues and options

As economic integration within the European Union (EU) progresses, the interactions between the tax systems of the Member States are of growing importance. Member State tax policies can have spillover effects on other Member States and differing abilities to provide net fiscal benefits to residents may impair the efficient allocation of productive factors across the EU. These considerations have important implications for the design and coordination of tax systems in the EU. Following a survey of tax developments and a review of the criteria that should govern the tax relationships between the Member States, this paper analyzes the issues and options that Member States face when levying and coordinating their taxes on consumption, labor and capital.Economics ;