A
Aditya Nandy
Researcher at Massachusetts Institute of Technology
Publications - 53
Citations - 1360
Aditya Nandy is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 13, co-authored 32 publications receiving 625 citations. Previous affiliations of Aditya Nandy include University of California, Berkeley & Genentech.
Papers
More filters
Journal ArticleDOI
Understanding the diversity of the metal-organic framework ecosystem
Seyed Mohamad Moosavi,Seyed Mohamad Moosavi,Aditya Nandy,Kevin Maik Jablonka,Daniele Ongari,Jon Paul Janet,Peter G. Boyd,Yong Jin Lee,Berend Smit,Heather J. Kulik +9 more
TL;DR: A machine learning method is developed to quantify similarities of MOFs to analyse their chemical diversity and identifies biases in the databases, and it is shown that such bias can lead to incorrect conclusions.
Journal ArticleDOI
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
TL;DR: In this paper, the authors introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry.
Posted ContentDOI
Understanding the Diversity of the Metal-Organic Framework Ecosystem
Seyed Mohamad Moosavi,Aditya Nandy,Kevin Maik Jablonka,Daniele Ongari,Jon Paul Janet,Peter G. Boyd,Yong Jin Lee,Berend Smit,Heather J. Kulik +8 more
TL;DR: This work shows how machine learning can be used to quantify similarities of MOFs, and shows that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions.
Journal ArticleDOI
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
TL;DR: In this paper, the authors compare the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors.
Journal ArticleDOI
Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations
Gokhan Barin,Gregory W. Peterson,Valentina Crocellà,Jun Xu,Kristen A. Colwell,Aditya Nandy,Jeffrey A. Reimer,Jeffrey A. Reimer,Silvia Bordiga,Jeffrey R. Long,Jeffrey R. Long +10 more
TL;DR: In this article, the performance of porous polymers functionalized with Bronsted acidic groups was investigated, which should possess inherent structural stability and enhanced reactivity towards ammonia in the presence of moisture.