Aditya Nandy

发表

Mechanistic Insights into Substrate Positioning That Distinguish Non-heme Fe(II)/α-Ketoglutarate-Dependent Halogenases and Hydroxylases

H. Kulik, Rimsha Mehmood, D. Kastner, 2023, ACS Catalysis.

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

H. Kulik, J. Janet, A. Nandy, 2019, Chemical science.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Michael G. Taylor, F. Liu, H. Kulik, 2021, Chemical reviews.

Understanding the diversity of the metal-organic framework ecosystem

Peter G. Boyd, Yongjin Lee, S. M. Moosavi, 2020, Nature Communications.

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost.

H. Kulik, A. Nandy, Chenru Duan, 2020, The journal of physical chemistry letters.

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

F. Liu, H. Kulik, Chenru Duan, 2021, The journal of physical chemistry letters.

DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks.

H. Kulik, A. Nandy, Chenru Duan, 2022, Journal of chemical theory and computation.

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

H. Kulik, A. Nandy, Chenru Duan, 2022, Chemical science.

Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-off in Multireference Diagnostics.

Chenru Duan, Aditya Nandy, Heather J Kulik, 2020, Journal of chemical theory and computation.

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

H. Kulik, A. Nandy, Chenru Duan, 2020 .

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery pdf

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2021, Current Opinion in Chemical Engineering.

Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation

H. Kulik, A. Nandy, Vyshnavi Vennelakanti, 2022, ACS Catalysis.

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

H. Kulik, A. Nandy, Chenru Duan, 2022, SSRN Electronic Journal.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

H. Kulik, A. Nandy, Chenru Duan, 2022, JACS Au.

Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis

H. Kulik, Yuriy Román‐Leshkov, Chenru Duan, 2022, Journal of chemical theory and computation.

A transferable recommender approach for selecting the best density functional approximations in chemical discovery

H. Kulik, A. Nandy, Chenru Duan, 2022, Nature Computational Science.

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy pdf

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2022, ArXiv.

Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory.

H. Kulik, Akash Bajaj, Yael Cytter, 2022, The journal of physical chemistry letters.

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

H. Kulik, Vyshnavi Vennelakanti, Aditya Nandy, 2021, Topics in Catalysis.

Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics.

H. Kulik, A. Nandy, Chenru Duan, 2020, Physical chemistry chemical physics : PCCP.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Chenru Duan, Aditya Nandy, Heather J. Kulik, 2021, Journal of the American Chemical Society.

MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks pdf

N. Arunachalam, G. Terrones, A. Nandy, 2021, ArXiv.

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry.

Michael G. Taylor, H. Kulik, Akash Bajaj, 2021, The Journal of chemical physics.

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery pdf

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2021, The Journal of chemical physics.

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

H. Kulik, A. Nandy, Aditya Nandy, 2020, ACS Catalysis.

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Rachel B. Getman, H. Kulik, J. Janet, 2019, ACS Catalysis.

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

H. Kulik, J. Janet, A. Nandy, 2018, Industrial & Engineering Chemistry Research.

Effects of MOF linker rotation and functionalization on methane uptake and diffusion

H. Kulik, Shuwen Yue, Chang-Ho Oh, 2023, Molecular Systems Design & Engineering.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

H. Kulik, J. Janet, A. Nandy, 2019, Journal of chemical theory and computation.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael G. Taylor, H. Kulik, J. Janet, 2020, The journal of physical chemistry. A.

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

H. Kulik, J. Janet, A. Nandy, 2019 .

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

H. Kulik, A. Nandy, Chenru Duan, 2022, JACS Au.

Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores

H. Kulik, A. Nandy, Chenru Duan, 2022, ArXiv.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

F. Liu, H. Kulik, J. Janet, 2019, Inorganic chemistry.

Large-Scale Screening Reveals That Geometric Structure Matters More Than Electronic Structure in the Bioinspired Catalyst Design of Formate Dehydrogenase Mimics

Michael G. Taylor, H. Kulik, A. Steeves, 2021, ACS Catalysis.

Understanding the Role of Geometric and Electronic Structure in Bioinspired Catalyst Design: the Case of Formate Dehydrogenase

H. Kulik, A. Steeves, A. Nandy, 2021 .

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis.

H. Kulik, Yuriy Román‐Leshkov, Ilia Kevlishvili, 2023, Journal of the American Chemical Society.

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design.

H. Kulik, J. Janet, Chenru Duan, 2021, Accounts of chemical research.

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes.

Michael G. Taylor, H. Kulik, Chenru Duan, 2023, The Journal of chemical physics.

Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning

Aditya Nandy, Heather J. Kulik, Michael G. Taylor, 2021, The journal of physical chemistry letters.

Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts

H. Kulik, Mingjie Liu, Haojun Jia, 2022, Journal of Materials Chemistry A.

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

H. Kulik, A. Nandy, Chenru Duan, 2022, ArXiv.

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

H. Kulik, Chenru Duan, N. Arunachalam, 2022, Scientific Data.

Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion

Chenru Duan, Ilia Kevlishvili, Mingjie Liu, 2024, ACS Catalysis.

Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts

Ilia Kevlishvili, Husain Adamji, Aditya Nandy, 2024, Journal of Catalysis.

Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning.

Shuwen Yue, Aditya Nandy, Heather J. Kulik, 2023, The journal of physical chemistry. B.

Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify

Clorice R. Reinhardt, Aditya Nandy, Freya Edholm, 2023, J. Comput. Chem..