Events‎ > ‎AI3SD Event List‎ > ‎

18-19/11/2019 - AI3SD Network+ Conference - Holiday Inn Winchester & Winchester Science Centre

posted 26 Feb 2019, 08:31 by Samantha Kanza   [ updated 17 Sep 2019, 06:21 ]

We are the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery). The network+ is funded by EPSRC and hosted by the University of Southampton and aims to bring together researchers looking to show how cutting edge artificial and augmented intelligence technologies can be used to push the boundaries of scientific discovery. We launched in December 2018, and this conference marks the end of our first year. This is a two day event with a mixture of keynote talks from experts in the different areas of AI for Scientific Discovery, and discussions around different research areas. There will be dedicated time for networking and we will be implementing a smart badge system whereby attendees can mark their badges according to whether they are looking for a collaborator, employment, job candidates, PhD students etc. We will report on the activities of AI3SD over the last year, including the workhops and hackathons we have run and attended, and there will be an opportunity to hear from the projects we funded in AI3SD-FundingCall1. On the evening of the 18th there will be drinks and networking in the Winchester Science Centre followed by a pre dinner talk by famous Science Communicator Steve Mould! This will be followed by a formal conference dinner at the Holiday Inn. 

If you wish to submit a poster (for presentation in Session 2) or a talk (for presentation in Session 6) please fill in our AI3SD 2019 Poster Submission Form or our AI3SD 2019 Abstract Submission Form. The deadline for these submissions is the 1st October at Midnight and we will send out notification of the accepted posters and talks by the 7th October. 

Invited Keynote Speakers:
  • Professor Tim Albrecht - University of Birmingham: Tim joined the School of Chemistry at the University of Birmingham from Imperial College in 2017, as Chair in Physical Chemistry, and became the School's Director of Research in 2018. His research interests cover a broad range of topics with focus on charge transport at the nanoscale, single-molecule thermoelectrics, single-molecule biosensing using nanopores and nanopipettes, automation, data analysis and machine learning, in particular for unsupervised data classification and sensing.

  • Professor Nigel Clarke - University of Sheffield: Nigel joined the Department of Physics and Astronomy at The University of Sheffield as Professor of Soft Matter Theory in 2011, following over 10 years in The Department of Chemistry at Durham University. Nigel is also a University of Sheffield alumnus, having earned his first degree and PhD at the university in 1991 and 1994, respectively. Nigel's experience spans a number of disciplines, including physics, mathematics, material sciences and chemistry.  The range of his research contributions include topics as diverse as current/voltage characteristics in organic photovoltaics, dynamics and structure in polymer nanocomposites, instabilities and pattern formation in thin films, mechanical properties of organo-gels and the cytoskeleton, blood flow through vein valves, phase separation and microstructure evolution in polymer blends and the coupling between phase transitions and flow.  Nigel’s direct contributions encompass theoretical and computational science, simulations and experimental science. 

  • Dr Lucy Colwell - University of Cambridge

  • Dr Olga Egorova - University of Southampton

  • Professor Juan P. Garrahan: University of Nottingham: Juan P. Garrahan has held a Chair in Physics at the University of Nottingham since 2007. His research covers a broad area of theoretical statistical physics and its applications, with particular interests in the dynamics of complex and slow relaxing materials such as supercooled liquids and glasses, molecular self-assembly, quantum non-equilibrium systems, and the theory of large deviations. He obtained his PhD from the University of Buenos Aires in 1997, was Glasstone Fellow at the University of Oxford, an EPSRC Advanced Fellow, and a visiting professor at UC Berkeley in 2007. At the University of Nottingham he is currently the head of the Centre for Quantum Non-Equilibrium Systems (CQNE) and the director of the Machine Learning in Science Initiative (MLiS) of the Faculty of Science.

  • Professor Brian Hayden - University of Southampton: Brian Hayden (FRSC, FIOP) obtained his PhD in Bristol in 1979 was a postdoctoral fellow at the Fritz Haber Institute of the Max Planck Society, Berlin, and appointed lecturer at the University of Bath (1983) and Southampton (1988) where he was appointed to a Personal Chair in 1995. In 2000, he extended thin film MBE based methodologies to the combinatorial synthesis and high-throughput screening of materials. He is a founder (2004) and Chief Scientific Officer of Ilika plc involved in materials discovery and development for the electronics and energy sectors. He is author of over 150 refereed papers {h-index 39}, and 30 active patent families.

  • Professor Ross King - University of Manchester

  • Dr Reinhard Maurer - University of Warwick

  • Professor Matthew Todd - University College London: Mat Todd obtained his PhD in organic chemistry from Cambridge University in 1999, was a Wellcome Trust postdoc at The University of California, Berkeley, a college fellow back at Cambridge University, a lecturer at Queen Mary, University of London and between 2005 and 2018 was at the School of Chemistry, The University of Sydney. He is now Chair of Drug Discovery at University College London. His research interests include the development of new ways to make molecules, particularly how to make chiral molecules with new catalysts. He is also interested in making metal complexes that do unusual things when they meet biological molecules or metal ions. His lab motto is "To make the right molecule in the right place at the right time", and his students are currently trying to work out what this means. He has a significant interest in open science, and how it may be used to accelerate research, with particular emphasis on open source discovery of new medicines. He founded and currently leads the Open Source Malaria (OSM) and Open Source Mycetoma (MycetOS) consortia, and is a founder of a broader Open Source Pharma movement. In 2011 he was awarded a NSW Scientist of the Year award in the Emerging Research category for his work in open science and in 2012 the OSM consortium was awarded one of three Wellcome Trust/Google/PLoS Accelerating Science Awards. For his open source research, Mat was selected for the Medicine Maker's Power List in 2017 and 2018. He is on the Editorial Boards of PLoS One, ChemistryOpen and Nature Scientific Reports. 
Selected Submitted Speakers:
  • Dr Vitaliy Kurlin - University of Liverpool: Vitaliy is a Computer Scientist at the Materials Innovation Factory in Liverpool, where he facilitates the collaboration between Chemists and Computer Scientists. He was awarded the Marie Curie International Incoming Fellowship (2005-2007) and the EPSRC grant “Persistent Topological Structures in Noisy Images" (2011-2013). In 2014-2016 he has gained industrial experience through Knowledge Transfer Secondments in the Computer Vision group at Microsoft Research, Cambridge, UK. From 2018 he leads the Liverpool team on a £2.8M EPSRC 5-year grant “Application-Driven Topological Data Analysis” (with Oxford and Swansea). His research group includes one postdoc and five PhD students working on applications of topology and geometry to Materials Science, Computer Vision and Climate. 

  • Dr Benedict Irwin - Optibrium:

  • Miloslav Torda - Leverhulme Research Centre for Functional Materials Design:

Review of AI across UKRI – Dr Anna Angus-Smyth
Abstract coming soon.

Predicting the Activity of Drug Candidates where there is No Target  Professor Matthew Todd

Abstract coming soon.

'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets  Professor Tim Albrecht

In the project “'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets”, we combine quantum tunnelling-based biosensing with advanced Machine Learning methods. DNA sequencing based on quantum mechanical tunnelling in principle allows for the label-free identification of nucleotides, based on their intrinsic electronic properties, and thus in some ways constitutes the ultimate limit in single-molecule sensing and sequencing. While the sensor performance is affected by many factors, including the design of the tunnelling junction and the surface chemistry of the (metal) electrodes, in this project the main focus is on maximising the level of information that is extracted from the data. For example, we have been able to demonstrate a significantly improved error rate when employing Convolutional Neural networks for “base calling”, compared to Support Vector Machines, and are now exploring Capsule Nets for further improvements.[1,2]

[1] T. Albrecht, E. Alonso et al., “Deep learning for single-molecule science”, Nanotechnology 2017, (42), 423001. 
[2] A. Vladyka, T. Albrecht et al., manuscript in preparation

Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery  Dr Reinhard J. Maurer

Atomistic simulation based on quantum mechanics (QM) is currently being revolutionized by artificial intelligence and machine-learning (ML) methods. This involves approaches to efficiently predict materials and molecules with specific properties within the vast space of possible chemical compounds. It also involves efficient regression in high-dimensional parameter spaces to accelerate computationally demanding quantum chemical calculations of molecular properties such as the thermodynamic stability or spectroscopic signatures while retaining the predictive power of QM.Most previous approaches have used ML to predict measurable observables that arise from the QM wave function of molecules. However, all properties derive from the wave function, therefore an AI model able to predict the wave function, has the potential to predict all molecular properties. In this talk, I will explore ML approaches to directly represent wave functions for the purpose of developing methods that use AI and quantum chemistry in synergy. After presenting approaches to encode physical symmetries into deep learning infrastructures, I will present our recent efforts to use data-driven deep learning to develop a highly efficient Density-Functional Tight-binding simulation method to describe hybrid metal-organic materials.

Non-equilibrium Physics and Machine Learning  Professor Juan P. Garrahan
I will discuss work at the interface of current questions in non-equilibrium physics and machine learning methods. I will focus on the general statistical mechanics issue of accessing and characterising rare dynamical events in stochastic systems. I will describe the connection between trajectory ensemble methods - often based on the mathematics of large deviations - and reinforcement learning (RL) in Markov decision processes (MDPs). I will explain how the problem of "making rare events typical" in a stochastic system corresponds to finding the optimal dynamics in an MDP. The results I will present illustrate the many possible synergies between statistical physics and machine and deep learning.

Keynote - Dr Lucy Colwell
Title and Abstract coming soon.

Materials Development in the Energy and Electronics Sectors through Combinatorial Synthesis, High-Throughput Screening and Machine Learning – Professor Brian Hayden

The combinatorial synthesis of solid-state materials combined with high-throughput characterization and screening provides an opportunity to develop increasingly large materials data-bases. Machine learning approaches are crucial in several aspects of the building, interpretation and explitation of such data-bases: These can also be constructed to include physical and chemical descriptors from, for example, ab-initio calculation. The challenge is to ensure an audited content and consistent format of the data. Examples of how machine learning is being developed in the interpretation of raw data sets is presented using data from high throughput investigations of electrocatalysts mediating the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) for the development of reversible fuel cells and rechargeable metal-air batteries. The results provide an insight into the potential opportunities of machine learning in the future in the predictive development of functional materials.

Machine Learning for Modelling Microstructure Evolution – Professor Nigel Clarke

Our aim is to enhance our modelling capabilities for microstructure evolution with machine learning. In particular, we focus on Gaussian processes (GPs), a popular non-parametric class of models used extensively in ML and uncertainty quantification, which have well documented predictive abilities. In our preliminary studies, we apply existing GP methodology to microstructure evolution to determine the feasibility of generating an emulator to supplement more traditional, computationally intense, approaches. As an exemplar, we focus on the non-linear Cahn-Hilliard equation for describing phase separation in blends. Spatio-temporal problems are particularly challenging for ML due to their high dimensionaility, hence we use a method recently proposed for using machine learning to predict video images, based on the idea of light cones, in which the present is only dependent on the past in the immediate spatial neighbourhood, analogous to real-space time-stepping numerical schemes for PDEs. We will present results which highlight the both the strengths and challenges of using ML for modelling microstructure.

The Automation of Science: Robot Scientists for Chemistry and Biology – Professor Ross King
A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The motivation for developing Robot Scientists is to both to better understand the scientific method, and to make scientific research more efficient.  The Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge: both formed and experimentally confirmed novel hypotheses.  Adam worked in the domain of yeast functional genomics. The Robot Scientist ‘Eve’ was originally developed to automate early-stage drug development: active machine learning for Quantitative Structure Activity Relationship (QSAR) learning. More recently my colleagues and I have adapted Eve to work on yeast systems biology, and cancer. We argue that it is likely that advances in AI and lab automation will drive the development of ever-smarter Robot Scientists. The Physics Nobel Frank Wilczek is on record as saying that in 100 years’ time the best physicist will be a machine. If this comes to pass it will transform our understanding of science and the Universe.

Active Learning for Computational Polymorph Landscape Analysis - Dr Olga Egorova
Abstract coming soon. 

Isometric classifications of periodic crystals - Dr Vitaliy Kurlin
Solid crystalline materials (briefly, crystals) have numerous applications from high-temperature superconductors to gas capture. A periodic crystal is an infinite arrangement of atoms or molecules obtained by translating a unit cell (a non-rectangular box) in 3 independent directions. The Crystal Structure Prediction (CSP) aims to discover solid crystal that is based on a given chemical composition and has several target properties, most importantly the energy of its thermodynamic stability. Prof Sally Price (UCL) has summarised the state-of-the-art in CSP as "embarrassment of over-prediction", because modern software outputs 1000s of simulated crystals without identifying only few most promising candidates for synthesising in a lab. The underlying problem is the enormous ambiguity of crystal representation via conventional unit cells, because many different unit cells can define identical (up to a rigid motion) or nearly identical crystals. Reduced cells compare crystals only exactly (giving an answer yes/no) without quantifying a similarity between crystals in a continuous way. We propose a new classification of crystals by geometric invariants that are continuously changing under perturbations (atomic vibrations of atoms). These invariants will provide a well-defined distance between crystals that can be used for visualising large datasets of simulated crystals by continuously varying a threshold for similarity. This work is joint with several colleagues from the groups of Prof Andy Cooper FRS and Prof Matt Rosseinsky FRS at the Materials Innovation Factory, University of Liverpool.

Practical applications of deep learning to imputation of drug discovery data - Dr Benedict Irwin 
We describe a novel deep learning method for completing sparse data matrices that accepts both molecular descriptors and sparse experimental data as inputs to exploit the correlations between experimentally measured endpoints, as well as structure-activity relationships (SAR). The method can robustly estimate the confidence in each prediction and often greatly improves the accuracy of prediction over conventional quantitative SAR models. We describe practical applications to drug discovery, including pharma-scale collections comprised of up to one million compounds as well as smaller, project-specific data sets. We illustrate how the filling in of missing data, combined with the ability to focus on the most confident predictions, guides the selection of compounds and prioritisation of experimental resources in hit-to-lead and lead optimisation.

Dense periodic packings in the light of crystal structure prediction - Miloslav Torda
One of the methods in the design of new materials is to predict the crystal structure of a new compound from it’s molecular composition. This process involves generating many hypothetical structures based on lattice energy optimization. A different approach based only on the geometry of a molecule with it’s potential to speed up classical crystal structure prediction computations will be presented. Our preliminary results with regard to the periodic packing of a geometric representations of the pentacene molecule using Monte-Carlo molecular dynamic simulations will be also shown. Limitations and downsides of presented approach will be discussed, and future directions will be proposed.

Draft Programme: 

Day 1 [Holiday Inn then Winchester Science Centre]
- Optional Ethics & AI Workshop [Holiday Inn]
  • 09:00-09:30: Workshop Registration & Coffee
  • 09:30-11:30: Workshop

- Main Conference [Holiday Inn]
  • 11:00 – 11:30: Registration & Coffee

  • 11:30 – 12:00: Introduction & Welcome

  • 12:00 – 12:30: Session 1 - AI3SD Network Retrospective – What have we done in 2019 - Professor Jeremy Frey

  • 12:30 – 13:30: Lunch + Networking

  • 13:30 – 14:00: Review of AI across UKRI – Dr Anna Angus-Smyth

  • 14:00 - 15:00: Session 2: Flash Talks for Online Posters

  • 15:00 - 15:30: Tea & Coffee + Networking

  • 15:30 – 17:00: Session 3 - Network funded projects interim reports

    • 15:30 – 16:00: Predicting the Activity of Drug Candidates where there is No Target - Professor Matthew Todd

    • 16:00 – 16:30: 'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets - Professor Tim Albrecht

    • 16:30 – 17:00: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery - Dr Reinhard J. Maurer

- Evening Activities [Winchester Science Centre]
  • 17:15 – 19:00: Drinks & Networking in the Science Centre

  • 19:00 – 20:00: Illusions & How the brain works by Steve Mould

- Dinner [Holiday Inn]
  • 20:15 – 22:00: Dinner

  • 22:00: End of Day 1

Day 2 [Holiday Inn]
  • 08:30 – 09:00: Tea & Coffee

  • 09:00 – 09:30: Keynote [TBC] 
  • 09:30 – 10:00: Non-equilibrium Physics and Machine Learning - Professor Juan P. Garrahan
  • 10:00 – 11:30: Session 4 – AI , Molecules & Materials

    • 10:00 – 10:30: Keynote - Dr Lucy Colwell

    • 10:30 - 11:00: Materials Development in the Energy and Electronics Sectors through Combinatorial Synthesis, High-Throughput Screening and Machine Learning – Professor Brian Hayden

    • 11:00 – 11:30: Discussion Session

  • 11:30 – 12:00: Tea & Coffee Break + Networking

  • 12:00 – 13:15: Session 5 – Talks from EPSRC AI Feasibility studies

    • 12:00 – 12:25: Machine Learning for Modelling Microstructure Evolution – Professor Nigel Clarke

    • 12:25 – 12:50: The Automation of Science: Robot Scientists for Chemistry and Biology – Professor Ross King
    • 12:50 – 13:15: Active Learning for Computational Polymorph Landscape Analysis - Dr Olga Egorova

  • 13:15 - 14:00: Lunch & Networking

  • 14:00 – 16:00: Session 6 – Contributed Talks

    • 14:00 – 14:20: Isometric classifications of periodic crystals - Dr Vitaliy Kurlin
    • 14:20 – 14:40: Practical applications of deep learning to imputation of drug discovery data: Dr Benedict Irwin 
    • 14:40 – 15:00: Dense periodic packings in the light of crystal structure prediction - Miloslav Torda
  • 16:00 – 16:30: Tea & Coffee Break + Networking

  • 16:30 – 17:00: Summing up and end of conference

Evening Activities on the 18th:
Our evening activities will begin with drinks and networking in the Winchester Science Centre. There will be a free drink on arrival, and the top floor of the science centre has been reserved for conference attendees to view and interact with the displays. This will be followed by a fascinating pre dinner talk by famous Science Communicator Steve Mould who will be talking on Illusions and How the Brain works. This will be followed by a formal conference dinner back at the Holiday Inn. 

We have reserved a number of rooms at the Winchester Holiday Inn on the nights of the 17th and 18th November for conference attendees at a rate of £100. You will be able to ring up and book one of these rooms until the 7th October, upon which they will be released and anybody looking to book a room after this point will be quoted the current selling rate. The details for the Winchester Holiday Inn can be found here. Please quote the University of Southampton and the 18th November when you ring up to book.

  1. Who should attend?
    Anyone with an interest in Artificial Intelligence, Augmented Intelligence, Automated Investigations, Machine Learning, Scientific Discovery, Materials Discovery, and the Philosophical and Ethical Implications of Artificial Intelligence. AI3SD includes members from academia, industry and government and we welcome new members from each of these sectors to add to our growing Network+ so that we can form new interdiscplinary partnerships and work together to futher the field of scientific discovery using AI techniques.
  2.  What will I get out of it?
    You will be able to network with likeminded people who have research interests that complement yours. You will find out about the work our Network+ has done over the past year (including funded projects, conferences, workshops, and hackathons) and hear about the opportunities we have available for 2020. 
  3. What are the aims of the Conference?
    This conference is aiming to help the Network+ to drive progress in the areas of AI for Scientific Discovery and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims. This conference will also report on the progress the Network+ has made over the last year so that current and new members can see what has been achieved and contribute to discussions about the activities of the Network+ in 2020. 
  4. What are the themes of the Conference?
    The main themes addressed in this conference are AI, Molecules, & Materials, and the current state and recent advancements in AI and Machine Learning. Alongside these will be contributed talks on research relevant to AI3SD, and reports on pilot projects funded by or relevant to AI3SD.