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