4.8 Article

Obtaining Electronic Properties of Molecules through Combining Density Functional Tight Binding with Machine Learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Chemistry, Physical

Curvature Constrained Splines for DFTB Repulsive Potential Parametrization

Akshay Krishna Ammothum Kandy et al.

Summary: The CCS methodology uses quadratic programming to fit repulsive potentials, ensuring a unique and optimal two-body repulsive potential in a single shot, making the parametrization process robust with minimal human effort. The method allows users to tune the shape of the repulsive potential based on prior knowledge and has been further developed with new constraints to handle sparse data.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

Review Chemistry, Multidisciplinary

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith et al.

Summary: The article discusses the potential impact of machine learning models on chemical sciences and emphasizes the importance of collaboration between expertise in computer science and physical sciences. It provides concise tutorials of computational chemistry and machine learning methods, and demonstrates how they can be used together to provide insightful predictions.

CHEMICAL REVIEWS (2021)

Article Chemistry, Physical

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Nir Goldman et al.

Summary: A rapid-screening approach has been developed for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. The method leverages a recent reactive molecular dynamics force field and linear combinations of Chebyshev polynomials, allowing for efficient creation of multi-center representations with minimal initial DFT calculations. The workflow has been focused on TiH2 as a model system, demonstrating its ability to produce reliable DFTB models over a broad range of thermodynamic conditions.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

Article Chemistry, Physical

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

Chiara Panosetti et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

Article Chemistry, Physical

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks

Martin Stoehr et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Chemistry, Multidisciplinary

Deep-neural-network solution of the electronic Schrodinger equation

Jan Hermann et al.

NATURE CHEMISTRY (2020)

Article Materials Science, Multidisciplinary

Artificial neural network correction for density-functional tight-binding molecular dynamics simulations

Junmian Zhu et al.

MRS COMMUNICATIONS (2019)

Review Chemistry, Physical

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Multidisciplinary Sciences

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K. T. Schuett et al.

NATURE COMMUNICATIONS (2019)

Review Physics, Multidisciplinary

Machine learning and the physical sciences

Giuseppe Carleo et al.

REVIEWS OF MODERN PHYSICS (2019)

Article Chemistry, Physical

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

Julian J. Kranz et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Chemistry, Physical

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians

Haichen Li et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Multidisciplinary

Description of Non-Covalent Interactions in SCC-DFTB Methods

Vijay Madhav Miriyala et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)

Article Chemistry, Multidisciplinary

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

Article Chemistry, Physical

Communication: Charge-population based dispersion interactions for molecules and materials

Martin Stoehr et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Chemistry, Physical

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Article Materials Science, Multidisciplinary

Density-functional tight-binding for beginners

Pekka Koskinen et al.

COMPUTATIONAL MATERIALS SCIENCE (2009)

Review Computer Science, Interdisciplinary Applications

Ab initio molecular simulations with numeric atom-centered orbitals

Volker Blum et al.

COMPUTER PHYSICS COMMUNICATIONS (2009)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)