Aditya Nandy

Aditya Nandy

Chemistry PhD

Massachusetts Institute of Technology

Aditya joined the Kulik group in November 2017 as a PhD student in Chemistry. He received his B.S. in Chemical Engineering from U.C. Berkeley in Spring 2017. At Cal, Aditya worked with Prof. Jeffrey A. Reimer, using solid-state NMR and other methods to study diffusion and adsorption of small molecules inside of MOF materials. While at MIT, Aditya held a NSF Graduate Research Fellowship. He is now a Schmidt AI + Science Fellow at UChicago and will start as an Assistant Professor at UCLA in 2025.

Interests
  • Computational Chemistry
  • Molecular Design
  • Metal-organic frameworks
Education

  • PhD in Chemistry, 2023

    Massachusetts Institute of Technology

  • BS in Chemical Engineering, 2017

    UC Berkeley

Publications

  1. Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify (2024)
  2. Large-scale Comparison of Fe and Ru Polyolefin C–H Activation Catalysts (2024)
  3. Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion (2024)
  4. Benchmarking Nitrous Oxide Adsorption and Activation in Metal-Organic Frameworks Bearing Coordinatively Unsaturated Metal Centers (2024)
  5. Learning Design Rules for Catalysts through Computational Chemistry and Machine Learning (2024)
  6. Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning (2023)
  7. Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis (2023)
  8. Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes (2023)
  9. A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models (2023)
  10. Mechanistic Insights Into Substrate Positioning Across Non-heme Fe(II)/Alpha-Ketoglutarate-Dependent Halogenases and Hydroxylases (2023)
  11. A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery (2023)
  12. Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models (2023)
  13. DFT-based Multireference Diagnostics in the Solid State: Application to Metal-organic Frameworks (2023)
  14. Low-cost machine learning prediction of excited state properties of iridium-centered phosphors (2023)
  15. Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores (2023)
  16. Effects of MOF Linker Rotation and Functionalization on Methane Uptake and Diffusion (2023)
  17. Ligand Additivity Relationships Enable Efficient Exploration of Transition Metal Chemical Space (2022)
  18. Using Computational Chemistry to Reveal Nature's Blueprints for Single-Site Catalysis of C–H Activation (2022)
  19. Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis (2022)
  20. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery (2022)
  21. Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost (2022)
  22. Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics (2022)
  23. Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory (2022)
  24. Machine Learning for the Discovery, Design, and Engineering of Materials (2022)
  25. Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts (2022)
  26. MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks (2022)
  27. Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry (2022)
  28. New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts (2022)
  29. Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery (2022)
  30. The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts (2022)
  31. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design (2021)
  32. Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning (2021)
  33. Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning (2021)
  34. Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery (2021)
  35. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks (2021)
  36. Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts (2020)
  37. Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost (2020)
  38. Understanding the diversity of the metal-organic framework ecosystem (2020)
  39. Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics (2020)
  40. Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions (2020)
  41. Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics (2020)
  42. Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation (2019)
  43. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry (2019)
  44. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models (2019)
  45. A quantitative uncertainty metric controls error in neural network-driven chemical discovery (2019)
  46. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry (2018)