ACS Emerging and Deep Tech Webinar Series: Big data Challenges for Nanotechnology
Join us to hear from Dr. Amanda Barnard who will discuss the impact of domain-specific constraints on data-driven nanomaterials design, and explore the differences between computational simulation and nanomaterials informatics that can be leveraged for greater impact.
Wed 10 Mar 2021 12:00 PM AEDT
Duration: 1.0 hours
Register by Fri 05 Mar 2021 05:00 PM AEDT
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Virtual
CPD Hours: 1
Skills Level: Research (RSCH) -> Level 5
About this event
Nanotechnology, the development and use of materials on the order of millionths of a millimetre in size, is an important enabling technology with applications in energy, environment, electronics and health. A fundamental aim of nanotechnology research is to identify features of nanomaterials that can be tuned to control how they perform under specific application conditions. The combination of computational modeling with machine learning provides a powerful way of relating structural features with functional properties, but combining these fundamentally different scientific approaches is not as straightforward as it seems. Machine learning methods were developed for large data sets with small numbers of consistent features. Typically, nanomaterials data sets are small, with high dimensionality and high variance in the feature space, and suffer from numerous destructive biases. None of the established data science or machine learning methods in widespread use today were devised with materials data sets in mind, but there are ways to overcome these issues and use them reliably.
In this presentation we will discuss the impact of domain-specific constraints on data-driven nanomaterials design, and explore the differences between computational simulation and nanomaterials informatics that can be leveraged for greater impact. We will review a case study using feature engineering, dimension reduction, machine learning and visualisation to predict the charge transfer properties of a set of carbon nanoparticle used in biomedical applications, based on their surface characteristics. Once the key nanoparticle features have been identified, we will use simple statistical methods to predict ensemble properties and investigate the impact of tuning these features on the properties of the sample as a whole, and show how manufacturers can separate particles capable of delivering chemotherapeutics to different parts of the body.
About the speaker
Senior Prof Amanda Barnard FAIP FRSC
Deputy Director, School of Computing, ANU
Prof. Dr. Amanda Barnard is a Senior Professor at the School of Computing in the Australian National University, the Leader of the Computational Science cluster and the Deputy Director. She received her PhD in theoretical condensed matter physics in 2003 from RMIT University, and is a Fellow of the Australian Institute of Physics and the Royal Society of Chemistry (UK). She currently leads research at the interface of computational modelling, high performance supercomputing, and applied machine learning and artificial intelligence (AI), in target domains of materials science, chemistry and nanotechnology.
Her research has been awarded in 12 national and international awards in 5 scientific disciplines, including the Feynman Prize (Theory) in 2014. She is a leader in the Australasian high performance computing community, and currently serves on the Pawsey Supercomputing Exascale Readiness (PaCER) programme committee, is Chair of the Australasian Leadership Computing Grants Scheme at the National Computational Infrastructure (NCI) and the independent director on the Board of Directors for New Zealand eScience Infrastructure (NeSI).
FYI - this event will not be recorded.
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Speakers
Speaker
Amanda Barnard
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