AI3SD Series: Winter Seminar Series 2020/21

This is a record of all of the talks from the AI3SD Winter Seminar Series 2020/21. All of our speakers were invited to be interviewed and where that interview has taken place, the records of the talks listed below also contain links to the related interviews. The following talks took place in this series:1. Topology: From shapes to numbers - Professor Jacek Brodzki (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P00892. New theoretical and data-driven approaches to the study of molecular conformational spaces and energy landscapes – Dr Ingrid Membrillo Solis (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P00883. The Shape of Data in Chemistry – Insights Gleaned from Complex Solutions and Their Interfaces – Professor Aurora Clark (Washington State University) - http://dx.doi.org/10.5258/SOTON/P00874. Natural Language Processing in AI-driven Drug Discovery: What it is, why it matters and how (not) to do it – Dr Sia Togia (Benevolent AI) - Video not available5. New Trends in Drug Discovery – Robotics & AI – Dr Martin-Immanuel Bittner (Arctoris) - http://dx.doi.org/10.5258/SOTON/P00866. An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery – Professor Matthew Todd (University College London) - http://dx.doi.org/10.5258/SOTON/P00857. Interpretable machine learning for materials design and characterization – Dr Keith Butler (STFC) - http://dx.doi.org/10.5258/SOTON/P00848. When charge transport data are a worm – a transfer learning approach for unsupervised data classification – Professor Tim Albrecht (University of Birmingham) - http://dx.doi.org/10.5258/SOTON/P00839. Prediction in organometallic catalysis – a challenge for computational chemistry – Dr Natalie Fey (University of Bristol) - http://dx.doi.org/10.5258/SOTON/P009310. Data-driven materials discovery for functional applications – Professor Jacqui Cole (University of Cambridge) - http://dx.doi.org/10.5258/SOTON/P0082 11. Outlier Detection in Scientific Discovery – Dr Jo Grundy (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P008112. Machine learning for electronically excited states of molecules – Dr Julia Westermayr (University of Warwick) - http://dx.doi.org/10.5258/SOTON/P008013. Machines Learning Chemistry – Professor Jonathan Hirst (University of Nottingham) - http://dx.doi.org/10.5258/SOTON/P009214. Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters – Dr Jenna A. Bilbrey (Pacific Northwest National Laboratories) - http://dx.doi.org/10.5258/SOTON/P007915. Semantic Web in Scientific Research – Possibilities & Practices – Dr Samantha Kanza (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P007816. Ontologies, Natural Language, Annotation and Chemistry – Dr Colin Batchelor (Royal Society of Chemistry) - http://dx.doi.org/10.5258/SOTON/P007717. H2020 Project Onto Trans – Dr Alexandra Simperler (Goldbeck Consulting) - http://dx.doi.org/10.5258/SOTON/P007618. Unifying Machine Learning and Quantum Chemistry: From Deep Learning of Wave Functions to ML/QM-hybrid methods – Dr Reinhard Maurer (University of Warwick) - http://dx.doi.org/10.5258/SOTON/P007519. Accelerating structure prediction methods for materials discovery: Professor Graeme Day (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P007420. High-Throughput Approaches for the Discovery of Supramolecular Organic Materials: Fusing Computational Screening with Automated Synthesis – Dr Becky Greenaway (Imperial College London) - Video not available 21. Generating a Machine-Learned Equation of State for Fluid Properties – Professor Erich Müller (Imperial College London) - http://dx.doi.org/10.5258/SOTON/P007322. Machine Learning with Causality: Solubility Prediction in Organic Solvents and Water – Dr Bao Nguyen (University of Leeds) - http://dx.doi.org/10.5258/SOTON/P007223. Machine learning for biological sequence design – Dr Lucy Colwell (University of Cambridge) - http://dx.doi.org/10.5258/SOTON/P009024. Machine learning applications for macro-molecular X-ray crystallography at Diamond – Dr Melanie Vollmar (Diamond) - http://dx.doi.org/10.5258/SOTON/P009125. Using convolutional neural networks to enable neoantigen load as a biomarker of cancer immunotherapy – Dr Felicia Ng (AstraZeneca) - Video not available26. Can Lattice Theory Help Find a Cure for Paralysis? – Dr Nicola Richmond (GlaxoSmithKline) - Video not availabl