| Inverse design of MXenes for high-capacity energy storage materials using multi-target machine learning S Li, AS Barnard Chemistry of Materials 34 (11), 4964-4974, 2022 | 28* | 2022 |
| Inverse Design of Nanoparticles Using Multi‐Target Machine Learning S Li, AS Barnard Advanced Theory and Simulations 5 (2), 2100414, 2022 | 14 | 2022 |
| Safety-by-design using forward and inverse multi-target machine learning S Li, AS Barnard Chemosphere 303, 135033, 2022 | 7 | 2022 |
| The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts S Li, JYC Ting, AS Barnard Journal of Computational Science 65, 101896, 2022 | 4 | 2022 |
| Exploring the cloud of feature interaction scores in a Rashomon set S Li, R Wang, Q Deng, A Barnard 2024 International Conference on Learning Representations (ICLR), 2024 | 3 | 2024 |
| Variance tolerance factors for interpreting all neural networks S Li, A Barnard 2023 International Joint Conference on Neural Networks (IJCNN), 1-9, 2023 | 3* | 2023 |
| Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference JYC Ting, S Li, AS Barnard Advanced Theory and Simulations 5 (10), 2200330, 2022 | 2 | 2022 |
| Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning S Li, JYC Ting, AS Barnard International Conference on Computational Science, 307-318, 2022 | 2 | 2022 |
| A machine learning-driven framework for the property prediction and generative design of multiple principal element alloys Z Li, S Li, N Birbilis Materials Today Communications 38, 107940, 2024 | 1 | 2024 |
| Multi-target neural network predictions of MXenes as high-capacity energy storage materials in a Rashomon set S Li, AS Barnard Cell Reports Physical Science 4 (11), 2023 | 1 | 2023 |
| Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models S Li, A Barnard arXiv preprint arXiv:2402.00347, 2024 | | 2024 |
| Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning S Li, A Barnard 2021 AIChE Annual Meeting, 2021 | | 2021 |
| Comparing Three Data Representations for Music with a Sequence-to-Sequence Model S Li, CP Martin AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint …, 2020 | | 2020 |
Amanda S. BarnardSenior Professor of Computational Science, Australian National UniversityE-mail megerősítve itt: anu.edu.au
