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Updated: Nov 2, 2025

Covalent Fragment Screening Using the Quantitative Irreversible Tethering Assay
Published on: February 28, 2025
Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds.
John Mark P Martirez1, Emily A Carter1
1Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, United States.
We developed a new quantum mechanics method using capped density functional embedding theory (DFET) to accurately simulate large molecules and materials. This approach reduces computational cost for correlated wavefunction (CW) theories.
Area of Science:
- Computational Chemistry
- Quantum Mechanics
- Materials Science
Background:
- Quantum-mechanics-(QM)-based simulations are crucial for understanding molecular and material properties.
- Correlated wavefunction (CW) theories offer high accuracy but are computationally expensive for large systems.
- Divide-and-conquer methods partition large systems to manage computational cost.
Purpose of the Study:
- To develop an efficient QM simulation method for large systems incorporating CW theories.
- To introduce a partitioning protocol using capping atoms and density functional embedding theory (DFET).
- To reduce the computational expense of simulating complex chemical systems.
Main Methods:
- Utilized capping atoms to saturate covalent bonds at fragment interfaces.
- Employed density functional embedding theory (DFET) to describe fragment interactions.
- Incorporated an auxiliary fragment to correct for capping group electron density contributions.
- Developed a purely electron-density-dependent embedding potential.
Main Results:
- Demonstrated the utility of the capped-DFET and capped embedded CW method.
- Successfully simulated contrasting systems: an organic molecule and an ionic metal oxide cluster.
- Achieved accurate simulations with reduced computational cost.
Conclusions:
- The capped-DFET method provides a computationally efficient way to apply CW theories to large systems.
- This approach simplifies implementation and reduces the cost of QM simulations.
- The method shows promise for diverse applications in chemistry and materials science.
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