Heather J. Kulik
Orcid: 0000-0001-9342-0191
According to our database1,
Heather J. Kulik
authored at least 21 papers
between 2016 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify.
J. Comput. Chem., 2024
2023
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets.
J. Cheminformatics, December, 2023
SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms.
J. Open Source Softw., July, 2023
A transferable recommender approach for selecting the best density functional approximations in chemical discovery.
Nat. Comput. Sci., 2023
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.
Nat. Comput. Sci., 2023
2022
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models.
CoRR, 2022
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties.
CoRR, 2022
Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores.
CoRR, 2022
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery.
CoRR, 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands.
CoRR, 2022
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis.
CoRR, 2022
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy.
CoRR, 2022
2021
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery.
CoRR, 2021
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks.
CoRR, 2021
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning.
CoRR, 2021
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks.
CoRR, 2021
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.
CoRR, 2021
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery.
CoRR, 2021
2019
Reply to "Comment on 'Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis'".
J. Chem. Inf. Model., 2019
Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis.
J. Chem. Inf. Model., 2019
2016
J. Comput. Chem., 2016