Fast and accurate modeling of molecular atomization energies with machine learning.
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Summary
A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.
- Type
- article
- Published
- 2011-09-12
- Cited by
- 1,781
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W2104489082
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:321566
Keywords
Computer science, Statistical physics, Physics
References
- The strengths of chemical bonds
- Statistical Mechanics: Theory and Molecular Simulation
- The Elements of Statistical Learning
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- (IEEE Transactions on Neural Networks,12(6):1498-1504)A Remote Password Authentication Scheme for Multi-Server Architecture Using Neural Network
- “Model”
- Learning with Kernels
Cited by
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- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- Machine learning for quantum mechanics in a nutshell
- Data mining approaches to high-throughput crystal structure and compound prediction.
- Electronic spectra from TDDFT and machine learning in chemical space.
- Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.
- Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
- Permutation-invariant distance between atomic configurations.
- Understanding Machine-learned Density Functionals
- Towards the Quantum Machine: Using Scalable Machine Learning Methods to Predict Photovoltaic Efficacy of Organic Molecules
- Alternative approach to chemical accuracy: a neural networks-based first-principles method for heat of formation of molecules made of H, C, N, O, F, S, and Cl.
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
- A QSPR approach for the fast estimation of DFT/NBO partial atomic charges☆
- Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
- Gaussian approximation potentials: A brief tutorial introduction
- Representing potential energy surfaces by high-dimensional neural network potentials
- Scaling laws for van der Waals interactions in nanostructured materials
- Multiscale approach for simulations of Kelvin Probe force microscopy with atomic resolution
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