TBMaLT, a flexible toolkit for combining tight-binding and machine learning

Machine learning sparse tight-binding parameters for defects

(2022) Christoph Schattauer et al.   npj Computational Materials

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

(2022) Liwei Zhang et al.   npj Computational Materials

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

(2022) Guozheng Fan et al.   Journal of Physical Chemistry Letters

Differentiable quantum chemistry with PySCF for molecules and materials at the mean-field level and beyond

(2022) Xing Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning in materials science: From explainable predictions to autonomous design

(2021) Ghanshyam Pilania   COMPUTATIONAL MATERIALS SCIENCE

Machine learned Hückel theory: Interfacing physics and deep neural networks

(2021) Tetiana Zubatiuk et al.   JOURNAL OF CHEMICAL PHYSICS

Exploring the landscape of Buckingham potentials for silica by machine learning: Soft vs hard interatomic forcefields

(2020) Han Liu et al.   JOURNAL OF CHEMICAL PHYSICS

Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch

(2020) Guoqing Zhou et al.   Journal of Chemical Theory and Computation

Extended tight‐binding quantum chemistry methods

(2020) Christoph Bannwarth et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

(2020) Zhuoran Qiao et al.   JOURNAL OF CHEMICAL PHYSICS

Differentiable Programming Tensor Networks

(2019) Hai-Jun Liao et al.   Physical Review X

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

(2019) Jonathan Schmidt et al.   npj Computational Materials

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

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

(2018) Haichen Li et al.   Journal of Chemical Theory and Computation

A Robust and Accurate Tight-Binding Quantum Chemical Method for Structures, Vibrational Frequencies, and Noncovalent Interactions of Large Molecular Systems Parametrized for All spd-Block Elements (Z = 1–86)

(2017) Stefan Grimme et al.   Journal of Chemical Theory and Computation

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

(2017) J. S. Smith et al.   Chemical Science

Atomic Level Modeling of Extremely Thin Silicon-on-Insulator MOSFETs Including the Silicon Dioxide: Electronic Structure

(2015) Stanislav Markov et al.   IEEE TRANSACTIONS ON ELECTRON DEVICES

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

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

A General Quantum Mechanically Derived Force Field (QMDFF) for Molecules and Condensed Phase Simulations

(2014) Stefan Grimme   Journal of Chemical Theory and Computation

Density functional tight binding

(2014) M. Elstner et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

(2010) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

Ab initio molecular simulations with numeric atom-centered orbitals

(2009) Volker Blum et al.   COMPUTER PHYSICS COMMUNICATIONS