| 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 | 40 | 2022 |
| Inverse Design of Nanoparticles Using Multi‐Target Machine Learning S Li, AS Barnard Advanced Theory and Simulations 5 (2), 2100414, 2022 | 25 | 2022 |
| Safety-by-design using forward and inverse multi-target machine learning S Li, AS Barnard Chemosphere 303, 135033, 2022 | 11 | 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 | 8 | 2024 |
| Variance tolerance factors for interpreting all neural networks S Li, A Barnard 2023 International Joint Conference on Neural Networks (IJCNN), 1-9, 2023 | 7* | 2023 |
| 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 | 6 | 2024 |
| 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 | 5 | 2022 |
| 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 | 4 | 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 | 4 | 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 | 4 | 2022 |
| Practical attribution guidance for rashomon sets S Li, AS Barnard, Q Deng arXiv preprint arXiv:2407.18482, 2024 | 3 | 2024 |
| Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences S Li, X Wang, AS Barnard Machine Learning: Science and Technology, 2024 | 1* | 2024 |
| EXAGREE: Towards Explanation Agreement in Explainable Machine Learning S Li, Q Deng, AS Barnard arXiv preprint arXiv:2411.01956, 2024 | | 2024 |
| Automated architectural space layout planning using a physics-inspired generative design framework Z Li, S Li, G Hinchcliffe, N Maitless, N Birbilis arXiv preprint arXiv:2406.14840, 2024 | | 2024 |
| Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning S Li, AS 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 UniversityVerified email at anu.edu.au