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Geometry Optimization Prediction

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One of the most crucial objectives in quantum chemistry is to predict the most stable configuration of atoms in a molecule. Essentially, it is an optimization issue where the goal is to reduce the total energy of the molecule in relation to where the nucleus is located. By reducing the model energy, geometric optimization can forecast the three-dimensional arrangement of atoms in a molecule. At least in theory, binding phenomena - the propensity of atoms and molecules to condense into more stable bigger structures - and the emergence of certain forms based on the component elements - can be understood as the outcome of geometric optimization. Alfa Chemistry offers its customers a molecular geometry conformation prediction service to assist their applied research.

Our Methods

Machine learning techniques have been widely used for molecular geometry optimization. Among them, the kriging model, which is a method akin to Gaussian process regression, is an exact interpolation method representing a multidimensional function. The best results for molecular geometry optimization come from the combination of energy calculation and gradient analysis. Gradient-enhanced kriging (GEK), a specific type of kriging, has been created to fully utilize the information the gradient provides.

Structures of the butadiene ethylene complex.Fig 1. Structures of the butadiene + ethylene complex. (Raggi G, et al. 2020)

GEK-based geometric optimization offers many advantages over traditional step-constrained second-order truncated extended molecular optimization methods. In particular, the agent model given by GEK can have multiple smooth points, converges smoothly to the exact model as the number of sample points increases, and contains explicit expressions for the expected error of the model function at any point.

For further efficient geometric optimization, Alfa Chemistry shows how the GEK procedure can be used in a way that, in the presence of a small number of data points, the substitution surface will guide the optimization to the minimum of the potential surface in a robust manner. In this sense, the GEK method will be utilized to simulate the behavior of a conventional second-order scheme while retaining the adaptability of a cutting-edge machine learning strategy. Additionally, constrained variance optimization will be made possible by using the expected error.

Our Service

Project NameMolecular Geometry Optimization Prediction Service
DeliverablesWe provide all raw data and analysis services to our customers.
Samples RequirementOur services require specific requirements from you.
Timeline DecideAccording to customers' needs
PricePlease contact us for an inquiry

Application:

The basic thermodynamics of chemical processes are established with the aid of molecular structure optimization. The optimization of equilibrium structures, transition states, reaction pathways, and limitations on the potential energy surface of the ground state forms the foundation of zero-level understanding of chemical reaction kinetics. Usually, unconstrained or constrained optimization on the ground and excited state potential energy surfaces is essential for researchers to extract qualitative understanding and quantitative predictions of the nature of chemical processes.

Geometry Optimization Prediction

Alfa Chemistry provides global customers with fast, professional and high-quality molecular geometry optimization prediction services at competitive prices, which can reduce the cost of late-stage experiments. Geometry optimization prediction service is a customized innovative scientific research service. We need to evaluate each project before we can determine the corresponding analysis plan and price. If you are interested in our services, please contact us for more details.

Reference

  • Raggi G, et al. (2020). "Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging." J. Chem. Theory Comput. 16(6): 3989-4001.

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