Aditya Nandy
Aditya Nandy
Schmidt AI in Science Fellow at UChicago and Incoming Assistant Professor at UCLA (2025)
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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, 4068, 2020
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), 2913-2922, 2019
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
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
Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations
G Barin, GW Peterson, V Crocella, J Xu, KA Colwell, A Nandy, JA Reimer, ...
Chemical Science 8 (6), 4399-4409, 2017
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 143 (42), 17535-17547, 2021
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
JP Janet, F Liu, A Nandy, C Duan, T Yang, S Lin, HJ Kulik
Inorganic Chemistry, 2019
Machine learning accelerates the discovery of design rules and exceptions in stable metal–oxo intermediate formation
A Nandy, J Zhu, JP Janet, C Duan, RB Getman, HJ Kulik
ACS Catalysis 9 (9), 8243-8255, 2019
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
C Duan, JP Janet, F Liu, A Nandy, HJ Kulik
Journal of Chemical Theory and Computation 15 (4), 2331-2345, 2019
Effect of hydration on healthy intervertebral disk mechanical stiffness
SE Bezci, A Nandy, GD O'Connell
Journal of Biomechanical Engineering 137 (10), 101007, 2015
Seeing is believing: Experimental spin states from machine learning model structure predictions
MG Taylor, T Yang, S Lin, A Nandy, JP Janet, C Duan, HJ Kulik
The Journal of Physical Chemistry A 124 (16), 3286-3299, 2020
MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
A Nandy, G Terrones, N Arunachalam, C Duan, DW Kastner, HJ Kulik
Scientific Data 9 (1), 74, 2022
Navigating transition-metal chemical space: artificial intelligence for first-principles design
JP Janet, C Duan, A Nandy, F Liu, HJ Kulik
Accounts of Chemical Research 54 (3), 532-545, 2021
NMR spectroscopy reveals adsorbate binding sites in the metal–organic framework UiO-66 (Zr)
A Nandy, AC Forse, VJ Witherspoon, JA Reimer
The Journal of Physical Chemistry C 122 (15), 8295-8305, 2018
New strategies for direct methane-to-methanol conversion from active learning exploration of 16 million catalysts
A Nandy, C Duan, C Goffinet, HJ Kulik
Jacs Au 2 (5), 1200-1213, 2022
Data-driven approaches can overcome the cost–accuracy trade-off in multireference diagnostics
C Duan, F Liu, A Nandy, HJ Kulik
Journal of Chemical Theory and Computation 16 (7), 4373-4387, 2020
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
A Nandy, C Duan, HJ Kulik
Current Opinion in Chemical Engineering 36, 100778, 2022
Putting density functional theory to the test in machine-learning-accelerated materials discovery
C Duan, F Liu, A Nandy, HJ Kulik
The Journal of Physical Chemistry Letters 12 (19), 4628-4637, 2021
Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts
A Nandy, H Kulik
ACS Catalysis, 2020
Semi-supervised machine learning enables the robust detection of multireference character at low cost
C Duan, F Liu, A Nandy, HJ Kulik
The Journal of Physical Chemistry Letters 11 (16), 6640-6648, 2020
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