60 Works

AI3SD Video: Why you should take up PhD Opportunities in the Physical Sciences

Jeremy G. Frey
This talk forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD...

AI3SD Video: One does not simply \"digitise scientific research\": The challenges and opportunities of technology in the 21st century

Samantha Kanza & Nicola Knight
We live in a technology driven era where emails, electronic systems and smart assistants are commonplace, and yet despite this there is an abnormally large amount of scientific research that is still recorded on paper. Additionally, even when data and research is captured electronically, it is of limited use unless it is adequately stored, labelled and made available in a machine-readable format. This talk explores some of the challenges and opportunities of digitising scientific research...

AI3SD Video: Towards Biological Plausibility Using Linked Open Data

Egon Willighagen
Behind risk assessment is experimental evidence. Behind biological knowledge is primary literature. However, because the amount of knowledge keeps growing, our experimental technologies are advancing and getting increasingly complex, even experts can no longer keep up with the progress in mechanistic understanding, outside their increasingly specialistic domain. At the same time, the number of biological questions with a simple answer keeps dropping and many modern questions have complex answers. Access to the right facts at...

AI3SD Video: Automated Chemical Ontology Expansion

Janna Hastings
Ontologies provide a shared vocabulary and semantic resource for a domain. Manual construction enables them to achieve high quality and capture subtle semantic nuances, essential for wide acceptance and applicability across a community. However, the manual curation process does not scale for large domains. I will present a methodology for automatic ontology extension based on deep learning using ontology annotations, and show how we apply this methodology to the ChEBI ontology, a prominent reference ontology...

AI3SD Video: Data-Driven Molecular Design in Computational Toxicology

Barbara Zdrazil
Timely drug discovery and toxicology approaches have seen a rise in strategies which use data as a basis for decisions at various stages. Such approaches include (automated) data integration and curation efforts, predictive machine learning approaches, as well as structure-based molecular design strategies that make use of the wealth of publicly available data sources and data types. In this talk, various computational workflows which have been developed in my lab for addressing research questions related...

AI3SD Video: Organising your Networks & Projects

Samantha Kanza & Nicola Knight
This talk will cover tips and tricks for how to organise your networks and projects, including how to set up your communication methods, how to structure and organise all of the different types of data that you collect and best practices for project management.

AI3SD Video: Event detection in single-molecule data - how to find molecular signatures without (too many) prior assumptions

Tim Albrecht
Data from single-molecule experiments, such as from current-time or conductance-distance spectroscopy or sensors, are often "noisy" and characterised by complex molecular behaviour. In some cases, extracting the physically relevant information may be based on supervised approaches, i.e. where labelled data are available for training. In other cases, such data are either not available or it may simply be undesirable to make a priori assumptions about the molecular characteristics, for example to prevent loss of information...

AI3SD Video: Interpretable Machine Learning for Materials' Design and Characterisation

Keith Tobias Butler
"Where is the knowledge we have lost in information?" T.S. Eliot, The Rock
Machine learning (ML) and artificial intelligence (AI) are the subjects of wildly differing opinions on utility and potential impact. Depending who we talk to ML is the solution to almost every human challenge, from open boarders to pandemic control, or presents an existential crises for the species. In materials science the polarisation is perhaps less extreme, but nonetheless pervasive, while the numbers of...

AI3SD Video: Inference from Medical Images: Subspaces for Low Data Regimes

Mahesan Niranjan
Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a...

AI3SD Video: Equality, Diversity & Inclusion in Networks: Developing your inclusive approach

Debra Fearnshaw & Jeremy G. Frey
This session will give delegates a framework in which to consider how they can develop an inclusive approach to their work, team and research. This talk will provide some examples of how to do this and inspire delegates to develop their own approaches.

AI3SD video: AI standardization to enable digital development

Emelie Bratt
Standardisation is often viewed hand in hand with legislation but there are significant differences in the purpose each serves. While both look to deliver a common level of safety and trust for industry as well as consumers standards are voluntary in nature. The role of standards is to respond to common problems identified across industry, through collaborative consensus base processes. It looks to produce guidance to bridge gaps in understanding and practices across sectors and...

AI3SD video: curated large inorganic datasets of reconnected InChI, InChI and IUPAC name

Thomas Allam
Speaking about my experience with the Skills4Scientists Seminar series on e-chemistry, networking and careers and the AI4SD internship itself.

AI3SD Video: Combining robotics and Machine Learning for accelerated drug discovery

Thomas Fleming
Artificial intelligence has an increasing impact on drug discovery and development, offering opportunities to identify novel targets, hit, and lead-like compounds in accelerated timeframes. However, the success of any AI/ ML model depends on the quality of the input data, and the speed with which in silico predictions can be validated in vitro.

The talk will cover laboratory automation and robotics and the benefits they offer in terms of quality and speed of data generation...

AI3SD Video: Learning to Control Quantum Systems Robustly

Frank Langbein
Quantum control provides methods to steer the dynamics of quantum systems. The robustness of such controls, in addition to high fidelity, is important for practical applications due to the presence of uncertainties arising from limited knowledge about system and control Hamiltonians, initial state preparation errors, and interactions with the environment leading to decoherence. We introduce a novel robustness measure based on the Wasserstein distance, and discuss structured singular value analysis and log-sensitivity approaches from classical...

AI3SD Video: Machine Learning and AI for Drug Design

Ola Engkvist
Artificial Intelligence has become impactful during the last few years in chemistry and the life sciences, pushing the scientific boundaries forward as exemplified by the recent success of AlphaFold2.

In this presentation I will provide an overview of how AI have impacted drug design in the last few years, where we are now and what progress we can reasonably expect in the coming years. The presentation will have a focus on deep learning based molecular...

AI3SD Video: Hyperparameter Optimisation for Graph Neural Networks

YINGFANG YUAN
Traditional deep learning has made significant progress on various problems, from computer vision to natural language processing. For graph problems, there are still many challenges. Graph neural networks (GNNs) have been proposed for a wide range of learning tasks in the graph domain. In particular, in recent years, an increasing number of GNN models were applied to model molecular graphs and predict the properties of the corresponding molecules. However, a direct impediment to achieve good...

Humans of AI3SD: Dr Nicholas Watson

Michelle Pauli & nicholas watson
Dr Nicholas Watson is an associate professor of chemical engineering at the University of Nottingham and his research is focused on data-driven in-process sensing to deliver sustainable, safe and productive food manufacturing systems.

In this Humans of AI3SD interview he discusses the benefits of low-cost sensors for SMEs, why the real world is a lot more complicated than the controlled world of the lab, and the surprising value in getting your problems tackled by people who've...

AI3SD Video: Reinforcement Learning Methods

Stephen Gow
Reinforcement learning is a machine learning paradigm in which an agent learns to make decisions to achieve a long-term goal. In the past five years, the previously somewhat niche method has seen substantially increased interest from within the chemistry community, driven by the need for a machine learning approach to problems of planning and sequential decision making and recent developments in harnessing the power of neural networks to make reinforcement learning achievable for large problems....

AI3SD Video: Cross-architecture tuning of quantum devices faster than human experts

Natalia Ares
A concerning consequence of quantum device variability is that the tuning of each qubit in a quantum circuit constitutes a time-consuming non-trivial process that has to be independently performed for each device, requiring a deep understanding of the particular device to be tuned and "muscle memory". I will show a machine-learning based approach that can tune quantum devices completely automatically, regard less of the device architecture and being agnostic to the material realisation. Our algorithm...

AI3SD Video: Automated Rational Design of Metal-Organic Polyhedra

Aleksandar Kondinkski
Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have: i) curated relevant MOP and CBU data; ii) developed an assembly model concept that embeds rules in the MOP construction; iii)...

AI3SD Video: AI and optimisation in Computational Chemistry

Grant Hill
Numerical methods of optimisation are vital in chemistry, ranging from finding the lowest energy of a molecule through to reactor design. This talk will discuss two examples of how we are using optimisation techniques to work towards automating the discovery of new materials and developing computational chemistry methodology. The first of these is an AI3SD funded project where we set out to develop new membranes for water desalination using artificial intelligence techniques. In the second,...

AI3SD video: harnessing advanced algorithms to enable the automated optimisation of telescoped chemical reactions; performance directed self-optimisation of bimetallic nanoparticle catalysts

Thomas Chamberlain
The catalytic performance of nanoparticles is dependent on an extensive number of properties, reactions conditions and combinations thereof; however, very few methods employing multivariate closed-loop optimisation of nanoparticle catalysts have been reported to date. Here we demonstrate a machine learning-driven reactor platform for the performance directed synthesis of nanoparticle catalysts. Our experimental strategy uses an automated two-stage continuous flow reactor with decoupled residence times, allowing the precise synthesis of gold-silver nanoparticles (AuAgNP) with variable metal...

AI3SD video: physical sciences data infrastructure: shaping the physical sciences roadmap

Simon J. Coles
Digital technologies and computational resources are being utilised in scientific research and an increasing rate, however, the vast potential of these resources has yet to be realised. The physical sciences data infrastructure (PSDI) project aims to accelerate research in the physical sciences by providing a data infrastructure that brings together and builds upon the various data systems researchers currently use. This project is currently funded through the EPSRC digital research infrastructure funding to undertake a...

AI3SD video: development of a full stack for digital R&D in chemistry and chemical process development

Alexei A. Lapkin
In order to enable seamless access to AI tools in research, it is necessary to transform how our laboratories are equipped. AI requires access to data, and it takes too long to gain access and to clean up datasets. Our experimental hardware is not wired and is not accessible to algorithms. What is required is a development of data architecture that enables access to experimental and literature data both to a uman in the middle...

AI3SD Video: ML2: Estimation with Machine Learning

Mahesan Niranjan
This video is the second video of Mahesan Niranjan's 5 part lecture series on Machine Learning for our Summer School.

Registration Year

  • 2022
    48
  • 2021
    12

Resource Types

  • Audiovisual
    60

Affiliations

  • University of Southampton
    60
  • University of Nottingham
    5
  • University of Cambridge
    4
  • University College London
    4
  • University of Leeds
    2
  • University of Oxford
    2
  • Swansea University
    2
  • University of Sheffield
    2
  • University of Strathclyde
    1
  • University of Kent
    1