Autonomous Intelligent Machines & Systems

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Brief description of the context of the research including potential impact: ArtificialIntelligence (AI) offers many opportunities for accelerating scientific discovery in fields like drugdiscovery, materials science, climate science, and medical sciences. The use of AI in collaborationwith traditional scientific methods will be crucial as traditional methods often require significanttime and resources, limiting the pace of innovation. In order to solve various scientific challengesand enable scientific discovery, novel AI methods must be developed. One often cannot simply applystandard ML techniques to scientific problems, as it requires domain knowledge and understandingof the data, so innovative methods must be developed. The potential impact of this work includes reducing experimental costs, shortening the discovery lifecycle, and fostering transformative advancesin scientific and industrial applications.Aims and Objectives: My primary aim is to develop cutting-edge AI methodologies that driveforward scientific discovery. One example of this that I am currently working on relates to moleculardesign, where my research aims to improve the generation of new molecular structures, acceleratingprogress in areas such as drug design and material creation. This research has a few key objectives:Develop novel AI models capable of proposing innovative molecular structures with particular desired propertiesApply the developed methods to practical use cases in drug discovery and materials scienceNovelty of the research methodology: This research will focus on scientific challenges andthe development of novel AI methods to solve them. This novelty will come through exploring advanced AI methods and proposing new alternative methodologies, through exploring fundamentalmathematical and scientific ideas. In the case of molecular design, we are developing an innovativeapproach by combining generative models with optimization techniques. This general approach willcontribute to both the theoretical understanding and practical application of AI in scientific fields.Alignment to EPSRC's strategies and research areas (which EPSRC research area theproject relates to): This research aligns with several of the EPSRC's key strategic priorities,particularly in the areas of Artificial Intelligence, Computational and Theoretical Chemistry, andMathematical Sciences. It supports EPSRC's broader goals of fostering innovation in computationalmethodologies and applying them to solve real-world scientific problems. The project contributesto the advancement of computational methods for chemical discovery, promoting breakthroughs inHealthcare Technologies and Digital Economy, as well as aligning with the EPSRC's focus on theuse of AI for solving interdisciplinary challenges in science and engineering.Any companies or collaborators involved: In addition to my two supervisors, Stephen Robertsand Yee-Whye Teh, current collaborators include Leo Klarner and Tim Rudner