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Heather J. Kulik

Heather J. Kulik

D-Index & Metrics

Chemistry

D-Index
54
Citations
10968
World Ranking
12569
National Ranking
3344

Overview

Heather J. Kulik is affiliated with MIT in the United States and has a research focus primarily in materials science and chemistry. Their work encompasses significant contributions to materials chemistry, inorganic chemistry, molecular biology, computational theory and mathematics, and organic chemistry.

The main topics of the scientist's work include:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Metal-Organic Frameworks: Synthesis and Applications
  • Catalysis and Oxidation Reactions
  • X-ray Diffraction in Crystallography
  • Protein Structure and Dynamics
  • Catalytic Processes in Materials Science

Recent published papers by Heather J. Kulik include:

  • Understanding the diversity of the metal-organic framework ecosystem, 2020, Nature Communications
  • Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning, 2021, Chemical Reviews
  • Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization, 2020, ACS Central Science
  • Roadmap on Machine learning in electronic structure, 2022, Electronic Structure
  • Fluids and Electrolytes under Confinement in Single-Digit Nanopores, 2023, Chemical Reviews

Frequent coauthors with whom the scientist has collaborated extensively include:

  • Aditya Nandy
  • Chenru Duan
  • Ilia Kevlishvili
  • Gianmarco Terrones
  • David W. Kastner

Publication venues where the scientist has released multiple works include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Journal of the American Chemical Society
  • The Cambridge Structural Database
  • ACS Catalysis

Heather J. Kulik has also contributed to the literature through book publications, including a title published by the American Chemical Society:

  • Machine Learning in Chemistry, 2020

Best Publications

  • Density functional theory in transition-metal chemistry: A self-consistent Hubbard U approach

    Heather J. Kulik;Matteo Cococcioni;Damian A. Scherlis;Nicola N. Marzari

  • Understanding the diversity of the metal-organic framework ecosystem

    Seyed Mohamad Moosavi;Seyed Mohamad Moosavi;Aditya Nandy;Kevin Maik Jablonka;Daniele Ongari

  • Protection of tissue physicochemical properties using polyfunctional crosslinkers.

    Young Gyun Park;Young Gyun Park;Chang Ho Sohn;Chang Ho Sohn;Ritchie Chen;Ritchie Chen;Margaret McCue;Margaret McCue

  • Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective

    Samuel Faucher;Narayana R Aluru;Martin Z. Bazant;Daniel Blankschtein

  • Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

    Jon Paul Janet;Heather J. Kulik

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

    Aditya Nandy;Chenru Duan;Michael G Taylor;Fang Liu

  • molSimplify: A toolkit for automating discovery in inorganic chemistry

    Efthymios I. Ioannidis;Terry Z. H. Gani;Heather J. Kulik

  • Mechanically triggered heterolytic unzipping of a low-ceiling-temperature polymer.

    Charles E. Diesendruck;Gregory I. Peterson;Heather J. Kulik;Joshua A. Kaitz

  • Perspective: Treating electron over-delocalization with the DFT+U method

    Heather J. Kulik

  • How Large Should the QM Region Be in QM/MM Calculations? The Case of Catechol O-Methyltransferase

    Heather J. Kulik;Jianyu Zhang;Judith P. Klinman;Todd J. Martínez;Todd J. Martínez

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

    Jon Paul Janet;Chenru Duan;Tzuhsiung Yang;Aditya Nandy

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

    Aditya Nandy;Chenru Duan;Jon Paul Janet;Stefan Gugler;Stefan Gugler

  • Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by FeIV═O

    Terry Z. H. Gani;Heather J. Kulik

  • Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores

    Unknown

  • Anion-Selective Redox Electrodes: Electrochemically Mediated Separation with Heterogeneous Organometallic Interfaces

    Xiao Su;Heather J. Kulik;Timothy F. Jamison;T. Alan Hatton

  • Ab Initio Quantum Chemistry for Protein Structures

    Heather J. Kulik;Heather J. Kulik;Nathan Luehr;Nathan Luehr;Ivan S. Ufimtsev;Ivan S. Ufimtsev;Todd J. Martinez;Todd J. Martinez

  • Ionization behavior of nanoporous polyamide membranes.

    Cody L. Ritt;Jay R. Werber;Mengyi Wang;Zhongyue Yang

  • Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models

    Fang Liu;Fang Liu;Nathan Luehr;Nathan Luehr;Heather J. Kulik;Heather J. Kulik;Todd J. Martínez;Todd J. Martínez

  • Systematic study of first-row transition-metal diatomic molecules: a self-consistent DFT+U approach.

    Heather J. Kulik;Nicola Marzari

  • Towards quantifying the role of exact exchange in predictions of transition metal complex properties.

    Efthymios I. Ioannidis;Heather J. Kulik

  • Systematic Quantum Mechanical Region Determination in QM/MM Simulation

    Maria Karelina;Heather Janine Kulik

  • A self-consistent Hubbard U density-functional theory approach to the addition-elimination reactions of hydrocarbons on bare FeO+.

    Heather J. Kulik;Nicola N. Marzari

  • Spatially extended Kondo state in magnetic molecules induced by interfacial charge transfer.

    U. G. E. Perera;H. J. Kulik;V. Iancu;L. G. G. V. Dias da Silva;L. G. G. V. Dias da Silva

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

    Jon Paul Janet;Fang Liu;Aditya Nandy;Chenru Duan

  • Accurate potential energy surfaces with a DFT+U(R) approach.

    Heather J. Kulik;Nicola Marzari

Frequent Co-Authors

Nicola Marzari
Nicola Marzari École Polytechnique Fédérale de Lausanne
Todd J. Martínez
Todd J. Martínez Stanford University
Giulia Galli
Giulia Galli University of Chicago
Xin Jin
Xin Jin South China University of Technology
Byung Kook Lim
Byung Kook Lim University of California, San Diego
Berend Smit
Berend Smit University of California, Berkeley
David Prendergast
David Prendergast Lawrence Berkeley National Laboratory

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