Amanda Susan Barnard is an Australian theoretical physicist working in predicting the real world behavior of nanoparticles using analytical models and supercomputer simulations and applied machine learning. Barnard is a pioneer in the thermodynamic cartography of nanomaterials, creating nanoscale phase diagrams relevant to different environmental conditions, and relating these to structure/property maps. Her current research involves developing and applying statistical methods and machine/deep learning in nanoscience and nanotechnology, and materials and molecular informatics. In 2014 she became the first person in the southern hemisphere, and the first woman, to win the Feynman Prize in Nanotechnology, which she won for her work on diamond nanoparticles. Barnard is currently based in Australia as Professor of Computational Science in the Research School of Computer Science at the Australian National University.
Biography
In 2001, she graduated with a first-class honours science degree from the Royal Melbourne Institute of Technology, majoring in applied physics. Barnard received a PhD in 2003 from RMIT for her computer modelling work predicting and explaining various forms of nanocarbon at different sizes. Following her PhD, Barnard served as a Distinguished Postdoctoral Fellow in the Center for Nanoscale Materials at Argonne National Laboratory. She also held a senior research position as Violette & Samuel Glasstone Fellow at the University of Oxford with an Extraordinary Research Fellowship at The Queen's College. Professor Barnard then moved to CSIRO as an ARC Queen Elizabeth II Fellow, an Office of the Chief Executive Science Leader, and finally as a Chief Research Scientist spanning 2009 to 2020.
2003–2005 Distinguished Postdoctoral Fellowship, Center for Nanoscale Materials, Argonne National Laboratory, USA
Research highlights
Identified the link between nanomorphology and the environmental stability of nanomaterials, and how it influences reactivity and potential "nano-hazards"
Developed a new technique for investigating the shape of nanomaterials as a function of size, temperature or chemical potential, able to include experimentally realistic structures and chemical environments
First researcher to report investigations into the effect of shape on size-dependent phase transitions in nanomaterials
Discovered the first example of anisotropic surface electrostatic potential in a homoelemental nanomaterial, resulting in dipolar or multipolar interactions in a non-polar material
Leader in statistical nanoscience and the use of statistical analysis and machine learning to predict the properties of diverse and complex ensembles of nanoscale materials.
