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Heather J. Kulik
Heather J. Kulik
Chemical Engineering and Chemistry, Massachusetts Institute of Technology
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Titel
Zitiert von
Zitiert von
Jahr
Density Functional Theory in Transition-Metal Chemistry: A Self-Consistent Hubbard Approach
HJ Kulik, M Cococcioni, DA Scherlis, N Marzari
Physical Review Letters 97 (10), 103001, 2006
7192006
Understanding the diversity of the metal-organic framework ecosystem
SM Moosavi, A Nandy, KM Jablonka, D Ongari, JP Janet, PG Boyd, Y Lee, ...
Nature communications 11 (1), 1-10, 2020
5142020
Protection of tissue physicochemical properties using polyfunctional crosslinkers
YG Park, CH Sohn, R Chen, M McCue, DH Yun, GT Drummond, T Ku, ...
Nature biotechnology 37 (1), 73-83, 2019
4022019
Critical knowledge gaps in mass transport through single-digit nanopores: a review and perspective
S Faucher, N Aluru, MZ Bazant, D Blankschtein, AH Brozena, J Cumings, ...
The Journal of Physical Chemistry C 123 (35), 21309-21326, 2019
3392019
Resolving transition metal chemical space: Feature selection for machine learning and structure–property relationships
JP Janet, HJ Kulik
The Journal of Physical Chemistry A 121 (46), 8939-8954, 2017
2792017
Mechanically triggered heterolytic unzipping of a low-ceiling-temperature polymer
CE Diesendruck, GI Peterson, HJ Kulik, JA Kaitz, BD Mar, PA May, ...
Nature chemistry 6 (7), 623-628, 2014
2602014
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
A Nandy, C Duan, MG Taylor, F Liu, AH Steeves, HJ Kulik
Chemical Reviews 121 (16), 9927-10000, 2021
2362021
Perspective: Treating electron over-delocalization with the DFT+ U method
HJ Kulik
The Journal of chemical physics 142 (24), 240901, 2015
2332015
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
JP Janet, C Duan, T Yang, A Nandy, HJ Kulik
Chemical science 10 (34), 7913-7922, 2019
2302019
Accelerating chemical discovery with machine learning: simulated evolution of spin crossover complexes with an artificial neural network
JP Janet, L Chan, HJ Kulik
The Journal of Physical Chemistry Letters 9 (5), 1064-1071, 2018
2232018
How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase
HJ Kulik, J Zhang, JP Klinman, TJ Martinez
The Journal of Physical Chemistry B 120 (44), 11381-11394, 2016
2202016
Predicting electronic structure properties of transition metal complexes with neural networks
JP Janet, HJ Kulik
Chemical Science 8 (7), 5137-5152, 2017
2152017
molSimplify: A toolkit for automating discovery in inorganic chemistry
EI Ioannidis, TZH Gani, HJ Kulik
Journal of computational chemistry 37 (22), 2106-2117, 2016
2092016
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
JP Janet, S Ramesh, C Duan, HJ Kulik
ACS Central Science 6 (4), 513-524, 2020
1742020
Strategies and software for machine learning accelerated discovery in transition metal chemistry
A Nandy, C Duan, JP Janet, S Gugler, HJ Kulik
Industrial & Engineering Chemistry Research 57 (42), 13973-13986, 2018
1652018
Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by FeIV=O
TZH Gani, HJ Kulik
ACS Catalysis 8 (2), 975-986, 2018
1622018
Roadmap on machine learning in electronic structure
HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ...
Electronic Structure 4 (2), 023004, 2022
1572022
Anion‐Selective Redox Electrodes: Electrochemically Mediated Separation with Heterogeneous Organometallic Interfaces
X Su, HJ Kulik, TF Jamison, TA Hatton
Advanced Functional Materials 26 (20), 3394-3404, 2016
1442016
Ab initio quantum chemistry for protein structures
HJ Kulik, N Luehr, IS Ufimtsev, TJ Martinez
The Journal of Physical Chemistry B 116 (41), 12501-12509, 2012
1352012
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
A Nandy, C Duan, HJ Kulik
Journal of the American Chemical Society, 2021
1342021
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