Amanda Barnard

Senior Professor

Australian National University

Australia

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About

Professor Amanda Barnard is a Senior Professor at the Australian National University, Australia. Her research focuses on machine learning applications in biomarker screening, materials science, and cheminformatics, as evidenced by her recent publications. She explores diverse methodologies for predictive modeling and optimization of materials properties.

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Collaborators (2)

Mohannad Mayyas

AUSTRALIA

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Sergei Kalinin

Weston Fulton Chair Professor

University of Tennessee

UNITED STATES

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Amanda Barnard

Research Interests

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About

Articles (20)

A three-step machine learning strategy for reproducible biomarker screening in ovarian cancer

BenchMake: turn any scientific data set into a reproducible benchmark

Understanding interpretable patterns of Shapley behaviours in materials data

Artificial intelligence for advanced functional materials: exploring current and future directions

Diverse explanations from data-driven and domain-driven perspectives in the physical sciences

Materials “Economatics”: Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning

Predicting battery applications for complex materials based on chemical composition and machine learning

Regional Explanations and Diverse Molecular Representations in Cheminformatics

Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning

Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling

Importance of Structural Features and the Influence of Individual Structures of Graphene Oxide Using Shapley Value Analysis

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations

Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference

Liquid metal synthesis solvents for metallic crystals

Federated data processing and learning for collaboration in the physical sciences

The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts

Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference

Safety-by-design using forward and inverse multi-target machine learning

Charge-dependent Fermi level of graphene oxide nanoflakes from machine learning

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning

Collaborators (2)

Mohannad Mayyas

Sergei Kalinin

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