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ORCIDHeather J. Kulik
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2020 – today
- 2024
[j8]Freya Edholm, Aditya Nandy
, Clorice R. Reinhardt, David W. Kastner, Heather J. Kulik:
Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify. J. Comput. Chem. 45(6): 352-361 (2024)- [i16]Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik:
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions. CoRR abs/2404.13430 (2024) - 2023
- [j7]Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan H. Jensen:
Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets. J. Cheminformatics 15(1): 121 (2023) - [j6]Gianmarco G. Terrones, Yu Chen, Archit Datar, Li-Chiang Lin, Heather J. Kulik, Yongchul G. Chung:
SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms. J. Open Source Softw. 8(87): 5429 (2023) - [j5]Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik:
A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Nat. Comput. Sci. 3(1): 38-47 (2023) - [j4]Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik:
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nat. Comput. Sci. 3(12): 1045-1055 (2023) - [i15]Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik:
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. CoRR abs/2304.06174 (2023) - 2022
- [i14]Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik:
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy. CoRR abs/2201.04243 (2022) - [i13]Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Román-Leshkov, Heather J. Kulik:
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis. CoRR abs/2203.01276 (2022) - [i12]Chenru Duan, Adriana J. Ladera, Julian C.-L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik:
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands. CoRR abs/2205.02879 (2022) - [i11]Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik:
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. CoRR abs/2205.02967 (2022) - [i10]Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik:
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery. CoRR abs/2207.10747 (2022) - [i9]Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik:
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 abs/2208.05444 (2022) - [i8]Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik:
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties. CoRR abs/2209.08595 (2022) - [i7]Aditya Nandy, Shuwen Yue, Changhwan Oh, Chenru Duan, Gianmarco G. Terrones, Yongchul G. Chung, Heather J. Kulik:
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models. CoRR abs/2210.14191 (2022) - 2021
- [i6]Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik:
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery. CoRR abs/2106.10768 (2021) - [i5]Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik:
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. CoRR abs/2106.13109 (2021) - [i4]Aditya Nandy, Chenru Duan, Heather J. Kulik:
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks. CoRR abs/2106.13327 (2021) - [i3]Michael G. Taylor, Aditya Nandy, Connie C. Lu, Heather J. Kulik:
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning. CoRR abs/2107.14280 (2021) - [i2]Aditya Nandy, Gianmarco Terrones, Naveen Arunachalam, Chenru Duan, David W. Kastner, Heather J. Kulik:
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks. CoRR abs/2109.08098 (2021) - [i1]Aditya Nandy, Chenru Duan, Heather J. Kulik:
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. CoRR abs/2111.01905 (2021)
2010 – 2019
- 2019
- [j3]Helena W. Qi, Heather J. Kulik:
Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis. J. Chem. Inf. Model. 59(5): 2199-2211 (2019) - [j2]Helena W. Qi, Heather J. Kulik:
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. 59(9): 3609-3610 (2019) - 2016
- [j1]Efthymios I. Ioannidis, Terry Z. H. Gani, Heather J. Kulik:
molSimplify: A toolkit for automating discovery in inorganic chemistry. J. Comput. Chem. 37(22): 2106-2117 (2016)
Coauthor Index
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