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2020 – today
- 2024
[j2]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)- 2023
- [j1]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) - 2022
- [i12]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) - [i11]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) - [i10]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) - [i9]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) - [i8]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) - [i7]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) - [i6]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
- [i5]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) - [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)
Coauthor Index
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