Zachary W. Ulissi
Orcid: 0000-0002-9401-4918Affiliations:
- Carnegie Mellon University, Pittsburgh, PA, USA
According to our database1,
Zachary W. Ulissi
authored at least 28 papers
between 2012 and 2024.
Collaborative distances:
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Bibliography
2024
Generalization of graph-based active learning relaxation strategies across materials.
Mach. Learn. Sci. Technol., 2024
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.
J. Chem. Inf. Model., December, 2023
Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters.
J. Chem. Inf. Model., October, 2023
<i>WhereWulff</i>: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions.
J. Chem. Inf. Model., April, 2023
AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification.
J. Open Source Softw., 2023
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.
CoRR, 2023
2022
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials.
Mach. Learn. Sci. Technol., December, 2022
Mach. Learn. Sci. Technol., September, 2022
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets.
Trans. Mach. Learn. Res., 2022
CoRR, 2022
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
2021
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy.
Mach. Learn. Sci. Technol., 2021
Enabling robust offline active learning for machine learning potentials using simple physics-based priors.
Mach. Learn. Sci. Technol., 2021
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021
2020
Mach. Learn. Sci. Technol., 2020
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage.
CoRR, 2020
2019
Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.
J. Chem. Inf. Model., 2019
2018
J. Chem. Inf. Model., 2018
2013
2012
Systems nanotechnology: Identification, estimation, and control of nanoscale systems.
Proceedings of the American Control Conference, 2012