Zachary W. Ulissi
Materials Chemistry · 2017–2022
A researcher in the Science corpus: 7 papers; trusts 2 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 124 citations into it; #6,180 of 11,513 by trust here, #4,355 by citations
- Catalysis — 24 citations into it
Whom Zachary W. Ulissi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Zachary W. Ulissi overall
Papers from Zachary W. Ulissi's perspective
Ordered by how strongly the researchers Zachary W. Ulissi trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- GradientBased Learning Applied to Document Recognition
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
- Gated Graph Sequence Neural Networks
- A new model for learning in graph domains
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- Self-Consistent Equations Including Exchange and Correlation Effects
- Quantum-chemical insights from deep tensor neural networks
Their papers
- The Open Catalyst 2020 (OC20) Dataset and Community Challenges (2020) — 822 citations
- An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (2020) — 83 citations
- Accelerated discovery of CO2 electrocatalysts using active machine learning (2020) — 1,213 citations
- Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution (2018) — 758 citations
- Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction (2017) — 325 citations
- Methods for comparing uncertainty quantifications for material property predictions (2019) — 208 citations
- The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts (2022) — 346 citations