The use of simulations and videos in order to improve the learning of REDOX reactions in Engineering Degrees

Many students from secondary schools to universities in many countries struggle to learn chemistry and many do not succeed. Many high school and university students experience difficulties with fundamental ideas in chemistry [1]. Despite the importance of the foundation of chemistry, most students emerge from introductory courses with very limited understanding of the subject [2]. Chemistry had been regarded as a difficult subject for students by many researchers, teachers and science educators [3-4] because of the abstract nature of many chemical concepts, teaching styles applied in class, lack of teaching aids and the difficulty of the language of chemistry. Information and communication technologies (ICT) have fundamentally changed the practices and procedures of teaching Chemistry at University Degrees. In general, the use of ICT in education lends itself to more student-centred learning settings. Furthermore, and due to the fact that the globalization is becoming more and more important, the role of ICT in education is becoming more and more essential. The presence of ICT in the interactive educational environment can help to develop thinking skills and make classrooms an environment for educational growth. ICT also helps students to develop new thinking skills which may transfer to different situations which may require analysis and comprehension skills, and consequently critical skill development. ICT has become an increasingly popular technological tool within an educational context. Even though, the potential of ITC use in increasing student interactivity and collaboration has been explored by many educators, the research conducted on the effectiveness of these tools use in an educational context is still quite limited. In this work a study to investigate the use of ICT in the teaching and learning of Chemistry at Malaga University was conducted between 2015/16. The study participants were two classrooms of the 1º level of Mechanical Engineering Degree. In the present work, a positive attitude towards learning has been accompanied by a motivated behaviour. This could be seen as the use of the simulation and some videos (ICT). The present study investigated whether computer assisted instruction, simulation and videos were more effective than face-to-face instruction in increasing student success in chemistry. This study aims to investigate the effectiveness ICT as an educational tool in an undergraduate course for students. The results of the Mechanical Engineering Degree study is based on surveys purpose after the use of an interactive application and videos in order to know, if they think the use of these ITC have improved their learning process. On average, the students find the use and application useful, overall because they are able to transfer from macroscopic level to microscopic or/ and symbolic level. Several concepts and conceptual relations covered in the chemistry or science courses were provided in a concrete way, the help of computer simulations improved the student success significantly. [1] Carson, J. , & Watson, E. M (2002). Undergraduate students’ understandings of entropy and gibbs free energy. University Chemistry Education, 4, 4-12. [2] Ochs, R.S. (1996) Thermodynamics and Spontaneity, Journal of Chemical Education, 73(10), pp 952-954. [3] Carter, C.S. and Brickhouse, M.W. (1989) What Makes Chemistry Difficult? Alternate Perceptions, Journal of Chemical Education, 66(3), 223-225 [4] Nakhleh, M.B. (1992) Why Some Students Don’t Learn Chemistry, Chemical Misconceptions, Journal of Chemical Education, 69(3), 191-196.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis

Back Matter including Contributors and Index to The role of the library in an electronic society: Papers presented at the 1979 Clinic on Library Applications of Data Processing

published or submitted for publicatio

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE

Development of a Comprehensive Network for Scientific and Technical Information in Japan

published or submitted for publicatio