The presentations given at AI3SD2019 can be found below:
Ethics Workshop
- Ethical Frameworks, Ethical Judgements – Dr Will McNeill
- Ethics for AI for Scientific Discovery – Dr Samantha Kanza
Session 1: AI3SD
- AI3SD Network Retrospective – Professor Jeremy Frey
Session 2: Data, AI, Molecules & Materials
- Learning with Complex Priors and Interactions – Professor John Shawe-Taylor
- Data driven models that predict protein function from sequence – Dr Lucy Colwell
- Materials Development in the Energy and Electronics Sectors through Combinatorial Synthesis, High–Throughput Screening and Machine Learning – Professor Brian Hayden [Slides cannot be shared]
Session 3: Network Funded Projects Interim Reports
- Predicting the Activity of Drug Candidates where there is No Target – Professor Matthew Todd
- ‘Next-next’ Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets – Professor Tim Albrecht
- Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery – Dr Reinhard J. Maurer
Session 4: Machine Learning & Deep Learning for Scientific Discovery
- Non-equilibrium Physics and Machine Learning – Professor Juan P. Garrahan
- Deep Machine Learning of Quantum Chemical Hamiltonians – Professor David Yaron
- AlphaFold: Improved protein structure prediction using potentials from Deep Learning – Dr Andrew Senior
Session 5: AI & Scientific Discovery
- The UKRI Review of Support for AI – Dr Renée Van de Locht
- Explainable AI and Scientific Discovery – Dr Richard Tomsett
Session 6: Flash Talks for Online Posters
- See Poster Page
Session 7: Talks from EPSRC AI Feasibility Studies
- Machine Learning for Modelling Microstructure Evolution – Professor Nigel Clarke
- The Automation of Science: Robot Scientists for Chemistry and Biology – Professor Ross King
- Multi-fidelity Statistical Learning Approach for Organic Molecular Crystal Structure Prediction – Dr Roohoolah Hafizi & Dr Olga Egorova
Session 8: Contributed Talks
- Isometric classifications of periodic crystals – Dr Vitaliy Kurlin
- Practical applications of deep learning to imputation of drug discovery data – Dr Benedict Irwin
- Dense periodic packings in the light of crystal structure prediction – Miloslav Torda
- Data Science and the Physical Sciences Data-Science Service – Dr Nicola Knight
- Ellipsoids as a new descriptor for materials – Dr James Cumby