A. Nandy

发表

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

Chenru Duan, Aditya Nandy, Jon Paul Janet, 2018, Industrial & Engineering Chemistry Research.

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

Fang Liu, Chenru Duan, Aditya Nandy, 2019, Journal of chemical theory and computation.

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

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

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.

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.

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.

Accelerating inorganic discovery with meta-calculation filtering via a decision classifier

Fang Liu, Chenru Duan, Aditya Nandy, 2019 .

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.

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.

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

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

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

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

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

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

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.

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 diversity of the metal-organic framework ecosystem

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

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.

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.

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

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

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.

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.

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

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

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

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

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

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

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

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

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

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

Ligand additivity relationships enable efficient exploration of transition metal chemical space.

Michael G. Taylor, H. Kulik, J. Janet, 2022, The Journal of chemical physics.

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.

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

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

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.