Useful equations for modeling the relative stability of common nanoparticle morphologies. - Amanda S. Barnard (Comput. Phys. Commun., 2011)

Quantitative Structure-Property Relationship Modeling of Electronic Properties of Graphene Using Atomic Radial Distribution Function Scores. - Michael Fernández; Hongqing Shi; Amanda S. Barnard (J. Chem. Inf. Model., 2015)

On reverse Monte Carlo constraints and model reproduction. - George Opletal; Timothy C. Petersen; Amanda S. Barnard; Salvy P. Russo (J. Comput. Chem., 2017)

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction. - Baichuan Sun; Michael Fernández; Amanda S. Barnard (J. Chem. Inf. Model., 2017)

Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics. - Tommy Liu; Amanda S. Barnard (Mach. Learn. Sci. Technol., 2021)

Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning. - Sichao Li; Jonathan Y. C. Ting; Amanda S. Barnard (ICCS (1), 2022)

Variance Tolerance Factors For Interpreting Neural Networks. - Sichao Li; Amanda S. Barnard (CoRR, 2022)

The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts. - Sichao Li; Jonathan Y. C. Ting; Amanda S. Barnard (J. Comput. Sci., 2022)

Federated data processing and learning for collaboration in the physical sciences. - W. Huang; Amanda S. Barnard (Mach. Learn. Sci. Technol., 2022)

Shapley Based Residual Decomposition for Instance Analysis. - Tommy Liu; Amanda S. Barnard (ICML, 2023)

Variance Tolerance Factors For Interpreting All Neural Networks. - Sichao Li; Amanda S. Barnard (IJCNN, 2023)

A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication. - Yufan Xia; Marco De La Pierre; Amanda S. Barnard; Giuseppe M. J. Barca (IPDPS, 2023)

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis. - Weitong Huang; Hanna Suominen; Tommy Liu; Gregory Rice; Carlos Salomon; Amanda S. Barnard (J. Biomed. Informatics, 2023)

Exploring the cloud of feature interaction scores in a Rashomon set. - Sichao Li; Rong Wang; Quanling Deng; Amanda S. Barnard (ICLR, 2024)

Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models. - Sichao Li; Amanda S. Barnard (CoRR, 2024)

Practical Attribution Guidance for Rashomon Sets. - Sichao Li; Amanda S. Barnard; Quanling Deng (CoRR, 2024)

EXAGREE: Towards Explanation Agreement in Explainable Machine Learning. - Sichao Li; Quanling Deng; Amanda S. Barnard (CoRR, 2024)

Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning. - Zixin Zhuang; Amanda S. Barnard (J. Chem. Inf. Model., 2024)

Online meta-learned gradient norms for active learning in science and technology. - Haiqi Dong; Amanda S. Barnard; Amanda J. Parker (Mach. Learn. Sci. Technol., 2024)

Classification of battery compounds using structure-free Mendeleev encodings. - Zixin Zhuang; Amanda S. Barnard (J. Cheminformatics, 2024)

Diverse explanations from data-driven and domain-driven perspectives in the physical sciences. - Sichao Li; Xin Wang; Amanda S. Barnard (Mach. Learn. Sci. Technol., 2025)

BenchMake: Turn any scientific data set into a reproducible benchmark. - Amanda S. Barnard (CoRR, 2025)

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing. - Najmeh Samadiani; Amanda S. Barnard; Dayalan Gunasegaram; Najmeh Fayyazifar (J. Intell. Manuf., 2025)