Zachary W. Ulissi

Orcid: 0000-0002-9401-4918

Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA, USA


According to our database1, Zachary W. Ulissi authored at least 27 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Generalization of graph-based active learning relaxation strategies across materials.
Mach. Learn. Sci. Technol., 2024

Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials.
CoRR, 2024

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text.
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

FINETUNA: fine-tuning accelerated molecular simulations.
Mach. Learn. Sci. Technol., September, 2022

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets.
Trans. Mach. Learn. Res., 2022

AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning.
CoRR, 2022

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis.
CoRR, 2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?
CoRR, 2022

Spherical Channels for Modeling Atomic Interactions.
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

Rotation Invariant Graph Neural Networks using Spin Convolutions.
CoRR, 2021


2020
Methods for comparing uncertainty quantifications for material property predictions.
Mach. Learn. Sci. Technol., 2020

The Open Catalyst 2020 (OC20) Dataset and Community Challenges.
CoRR, 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
Dynamic Workflows for Routine Materials Discovery in Surface Science.
J. Chem. Inf. Model., 2018

2013
Control of nano and microchemical systems.
Comput. Chem. Eng., 2013

2012
Systems nanotechnology: Identification, estimation, and control of nanoscale systems.
Proceedings of the American Control Conference, 2012


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