Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-ai-for-materials-discovery-workshop-tickets-56112484953

Description
Materials play a key role in modern society and the growing demands for functionality, selectivity, re-usability, efficiency and environmentally sustainable which all place huge demands on the chemistry. The use of Computational Chemistry and more generally Artificial and Augmented Intelligence (AI) to predict structure and function provides new possibilities in using theory to drive innovation and discovery.

Agenda

• 13:00-13:30 – Registration & Coffee
• 13:30-13:45 – Welcome from Professor Jeremy Frey
• 13:45-14:30 – Theoretical Studies of CO and CO2 Hydrogenation to Methanol and Conversion of Methanol to Olefins – Professor Felix Studt
• 14:30-15:15 – LAISER: Putting the AI in Laser – Dr Ben Mills
• 15:15-15:30 – Coffee Break
• 15:30-16:15 – Machine learning opportunities in prediction-led discovery of molecular materials – Professor Grame Day
• 16:15-17:00 – Potential Solutions to Mathematical Challenges for Solid Crystalline Materials – Dr Vitaliy Kurlin
• 17:00-17:45 – One million crystal structures: what can we learn? – Dr Angeles Pulido
• 17:45-18:00 – Wrap up & Conclusions

Keynote Speakers

• Professor Felix Studt – Felix is a Professor at the Institute of Chemical Technology and Polymer Chemistry (ITCP) and the Institute of Catalysis Research and Technology (IKFT) at the Karlsruhe Institute of Technology (KIT). His research interests are in theory guided materials discovery, electrochemical processes, synthesis gas conversion, CO2 reduction, and routes from biomass to chemicals. He has published over 70 papers which have had over 2900 citations, and has been invited to speak at many international conferences. He has also published a textbook on the Fundamental Concepts in Heterogeneous Catalysis.
• Dr Ben Mills – Ben is a Senior Research Fellow and EPSRC Early Career Fellow at the Optoelectronics Research Centre at the University of Southampton, where he leads a research group focussed at the interface of laser machining and machine learning. Ben completed his PhD in high harmonic generation via ultrafast lasers at the University of Southampton in 2009, and then became the manager of the “FAST lab”, a femtosecond laser facility at the University. His background covers 10 years in ultrafast lasers and their application for high-precision materials processing. Current research interests also include laboratory automation and machine learning, in particular convolutional neural networks.
• Professor Graeme Day – Graeme is a Professor of Chemical Modelling at the University of Southampton. His research interests centre on the development and application of computational methods for understanding and predicting the structures and properties of molecular materials. An area of particular interest is crystal structure prediction, and its applications in structure determination, polymorph discovery and the design of materials with targeted properties. These research areas all stem from a fundamental interest in understanding and modelling intermolecular interactions. Graeme is the author or co-author of over 115 publications, including 5 book chapters. He serves on the advisory board for the Royal Society of Chemistry’s journal Molecular Systems Design and Engineering, is on the steering committee of the UK Materials Chemistry High End Computing Consortium and is a member of the EPSRC peer review college.
• Dr Vitaliy Kurlin – 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 Angeles Pulido – Angeles is a Research and Application Scientist at the Crystallographic Data Centre (CCDC) the Cambridge CDC, she is part of the Pfizer Design Centre within the Materials Science team who apply computational techniques to study organic molecular crystals relevant to pharmaceutical industry, with especial interest in crystal structure prediction, materials stability and polymorphism. Angeles’ main research interest is in silico modelling of solids and the use of computational techniques to provide an atomistic view and a better understanding of thermodynamic, kinetic and spectroscopic features of crystalline organic and inorganic materials.

Keynote Abstracts

• Theoretical Studies of CO and CO2 Hydrogenation to Methanol and Conversion of Methanol to Olefins – Professor Felix Studt: The catalytic conversion of CO2 to fuels and chemicals is experiencing renewed interest and growth as it is seen as one of the cornerstone reactions in a future sustainable energy scenario. Methanol, which can also be produced from CO2, is also an important chemical building block as it can be converted to olefins, hydrocarbon and gasoline. Theoretical studies of the processes at the catalytic surfaces help to understand how these catalyst function on the atomic-scale. Here insight gained on the active site of methanol synthesis[1] as well as the selectivity for CO and CO2 hydrogenation[2] is used for the computational screening of new CO2 hydrogenation catalysts.[3] We also investigated the conversion of methanol to olefins in zeolite catalysts using a combination of ab initio/density functional theory and microkinetic/reactor modeling.[4,5] In addition we will show how theory can help establishing trends across different acid sites and various frameworks,[6-8] a finding that might serve as a guidance for the discovery of improved catalysts for the production of fuels and chemicals from methanol.
• LAISER: Putting the AI in Laser – Dr Ben Mills: Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now becoming heavily focussed on making existing processing techniques more precise and efficient. A research area of particular current importance is therefore the development of real-time monitoring and feedback systems for laser machining, via visual inspection of the sample during machining. Convolutional neural networks (CNNs) offer the capability for image processing without the need for understanding the underlying physical processes, and hence offer an ideal solution for the monitoring of laser machining, which itself is not fully understood. In this talk, the application of CNNs for real-time monitoring and process control for laser machining will be discussed, along with the capability of CNNs for predicting the outcome of laser machining before the experiment occurs. In addition, an application of combining laser light with CNNs for real-time sensing of pollution particulates will be demonstrated.
• Machine learning opportunities in prediction-led discovery of molecular materials – Professor Graeme Day: Predictive computational approaches have developed rapidly as tools to accelerate the discovery of molecular materials with targeted properties. A challenge in developing the use of these approaches is the expense of both crystal structure prediction and property prediction, and the difficulty of interpreting the resulting energy-structure-function landscapes, which normally contain huge numbers of possible structures. The talk will discuss opportunities for developing machine learning approaches to improve the speed and reliability of computational predictions.
• Potential Solutions to Mathematical Challenges for Solid Crystalline Materials – Dr Vitaliy Kurlin: Abstract. Solid crystalline materials (briefly, crystals) can be modelled as periodic structures based on a geometric pattern that represents any chemical composition. One of the challenges in crystal structure prediction is to encode any crystal in a unique numerical form that is convenient to compare crystals and to search for new crystals with better properties. The talk will discuss continuous geometric invariants that will enable a more efficient search in the huge configuration space of all possible crystals.
• One million crystal structures: what can we learn? – Dr Angeles Pulido: The Cambridge Structural Database (CSD) is fast approaching the astonishing milestone of 1 million crystal structures. The CSD captures not just crystallographic structural data, but it intrinsically also contains an enormous amount of experimental information on molecular conformations and interactions, as well as physico-chemical properties. This chemistry and property information is key to underpinning the challenge of computer-led materials design and development. This talk will focus on how AI strategies have been used to transform the vast amount of scientific information in the CSD into actionable knowledge: from approaches to improve data curation and quality; to the development of methodologies to assist in drug development. Some of the challenges faced by AI approaches will be discussed, as well as the potential for empowering and further enriching the information in the CSD.

FAQ

1. Who should attend?
Anyone with an interest in Materials discovery, Artificial Intelligence, Machine Learning, Deep Learning, and particularly those looking to apply AI technologies to materials discovery. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery.

2. What will I get out of it?
You will be able to network with likeminded people who have research interests that complement yours. There will be several keynotes around the topics of AI for Materials Discovery to spark discussion and ideas. You will hear a range of thought-provoking talks about different aspects of using AI technologies in the area of materials discovery, and have the opportunity to both discuss this subject area with other members of the workshop and address questions to the speakers. Members of the Network Executive Group will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons).

3. What are the aims of the workshop?
This workshop is aiming to help the Network+ to drive progress in this area 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.

4. What are the main themes of the workshop?
Materials, Computational chemistry, Novel Mathematics, AI.