Daniel B. K. Chu

Daniel B. K. Chu

Graduate Student

Massachusetts Institute of Technology

Daniel joined the group in December 2019 as a Ph.D. student in chemical engineering, having graduated from UC Santa Barbara with a B.S. in chemical engineering and a minor in physics. As an undergraduate, Daniel primarily worked on modeling LaMer burst nucleation in the Peters group. They became interested in electronic structure theory while using DFT for a computational study on CO2 reduction catalysts in Martin Head-Gordon’s group at UC Berkeley. Daniel’s current research in the Kulik group focuses on the development and application of mathematical models which relate the properties of transition metal complexes beyond simple additivity. They are interested in combining machine learning methods with mathematical modeling to achieve greater prediction accuracy at reduced data cost.

Interests
  • electronic structure theory
  • density functional theory
  • transition metal chemistry
Education
  • BS in Chemical Engineering, 2019

    UC Santa Barbara

Publications

  1. Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery (2024)
  2. Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes" (2024)
  3. Ligand Additivity Relationships Enable Efficient Exploration of Transition Metal Chemical Space (2022)
  4. Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost (2022)
  5. Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics (2020)