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Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning.

, , , , , и . J. Chem. Inf. Model., 61 (5): 2147-2158 (2021)

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Donald Carlson

Donald Carlson

Donald Merckling

Donald Preuss

Donald Doenges

 

Другие публикации лиц с тем же именем

On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods., , , , , и . CoRR, (2022)On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks., , , , , и . LoG, том 231 из Proceedings of Machine Learning Research, стр. 6. PMLR, (2023)Reliable Graph Neural Network Explanations Through Adversarial Training., , , , и . CoRR, (2021)How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications., , , , и . KDD, стр. 2637-2647. ACM, (2022)Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance., и . COMPLEX NETWORKS (1), том 1141 из Studies in Computational Intelligence, стр. 3-15. Springer, (2023)Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design., , , , , , и . CoRR, (2020)Actionable Attribution Maps for Scientific Machine Learning., , , , , , и . CoRR, (2020)Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning., , , , , , и . CoRR, (2022)Generative Counterfactual Introspection for Explainable Deep Learning., , , и . GlobalSIP, стр. 1-5. IEEE, (2019)FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing., , , и . CoRR, (2022)