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Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.
J Chem Inf Model
January 2025
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves.
View Article and Find Full Text PDFRelation between Electrostatic Charge Density and Spin Hamiltonian Models of Ligand Field in Lanthanide Complexes.
Inorg Chem
January 2025
Physikalisches Institut, Universität Freiburg, D-79104 Freiburg, Germany.
Understanding the ligand field interactions in lanthanide-containing magnetic molecular complexes is of paramount importance for understanding their magnetic properties, and simple models for rationalizing their effects are much desired. In this work, the equivalence between electrostatic models, which derive their results from calculating the electrostatic interaction energy of the charge density of the 4f electrons in an electrostatic potential representing the ligands, and the common quantum mechanical effective spin Hamiltonian in the space of the ground multiplet is formulated in detail. This enables the construction of an electrostatic potential for any given ligand field Hamiltonian and discusses the effects of the ligand field interactions in terms of an interaction of a generalized 4f charge density with the electrostatic potential.
View Article and Find Full Text PDFEvaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.
J Chem Theory Comput
January 2025
Exscientia, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level.
View Article and Find Full Text PDFNuclear Quantum Effects in Quantum Mechanical/Molecular Mechanical Free Energy Simulations of Ribonucleotide Reductase.
J Am Chem Soc
December 2024
Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States.
The enzyme ribonucleotide reductase plays a critical role in DNA synthesis and repair. Its mechanism requires long-range radical transfer through a series of proton-coupled electron transfer (PCET) steps. Nuclear quantum effects such as zero-point energy, proton delocalization, and hydrogen tunneling are known to be important in PCET.
View Article and Find Full Text PDFCalculation of divergenceless magnetically induced current density in molecules.
J Chem Phys
November 2024
Fachbereich für Chemie und Physik der Materialien, Paris-Lodron Universität Salzburg, Jakob-Harringerstr. 2a, A-5020 Salzburg, Austria.
A method for the calculation of divergenceless, magnetically induced quantum mechanical current densities in molecules that approximates the exact current is presented. This was achieved by adding to the calculated conventional current density, i.e.
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