9 papers analyzed
These studies suggest that the behavior of atoms and molecules can be studied and modeled using machine learning models, quantum theory, and atomistic modeling, each providing valuable insights and accurate predictions of molecular properties.
The study and modeling of the behavior of atoms and molecules are fundamental to understanding chemical reactions, material properties, and biological processes. Various computational and theoretical methods have been developed to predict and analyze the properties and interactions of atoms and molecules, ranging from quantum mechanical approaches to machine learning models.
Machine Learning Models for Quantum Mechanical Properties:
Quantum Theory of Atoms in Molecules (QTAIM):
Graph-Based Neural Networks:
Independent and Modified Atom Models (IAM and MAM):
Perturbed Atoms in Molecules and Solids (PATMOS):
Molecular Mechanics and Integrated Software Systems:
Atomistic and Mesoscopic Modeling:
Game Theory in Atomic Interactions:
The study and modeling of atomic and molecular behavior leverage a variety of computational methods, from machine learning and neural networks to quantum mechanical theories and molecular mechanics. These approaches provide accurate predictions of molecular properties, enhance our understanding of chemical interactions, and bridge the gap between theoretical models and experimental observations.
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