Machine learning sparse tight-binding parameters for defects

DFTB Modeling of Lithium-Intercalated Graphite with Machine-Learned Repulsive Potential

(2021) Chiara Panosetti et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure

(2021) Zifeng Wang et al.   npj Computational Materials

Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory

(2021) Kevin F. Garrity et al.   Scientific Data

Grain-boundary-rich polycrystalline monolayer WS2 film for attomolar-level Hg2+ sensors

(2021) Lixuan Liu et al.   Nature Communications

The critical role of composition-dependent intragrain planar defects in the performance of MA1–xFAxPbI3 perovskite solar cells

(2021) Wei Li et al.   Nature Energy

Advanced modeling of materials with PAOFLOW 2.0: New features and software design

(2021) Frank T. Cerasoli et al.   COMPUTATIONAL MATERIALS SCIENCE

Ab initio electron-defect interactions using Wannier functions

(2020) I-Te Lu et al.   npj Computational Materials

Tight-Binding Studio: A technical software package to find the parameters of tight-binding Hamiltonian

(2020) Mohammad Nakhaee et al.   COMPUTER PHYSICS COMMUNICATIONS

Machine learning approach to constructing tight binding models for solids with application to BiTeCl

(2020) M. Nakhaee et al.   JOURNAL OF APPLIED PHYSICS

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Localized Intervalley Defect Excitons as Single-Photon Emitters in WSe2

(2019) Lukas Linhart et al.   PHYSICAL REVIEW LETTERS

Data‐Driven Materials Science: Status, Challenges, and Perspectives

(2019) Lauri Himanen et al.   Advanced Science

BOPcat software package for the construction and testing of tight-binding models and bond-order potentials

(2019) A.N. Ladines et al.   COMPUTATIONAL MATERIALS SCIENCE

PAOFLOW : A utility to construct and operate on ab initio Hamiltonians from the projections of electronic wavefunctions on atomic orbital bases, including characterization of topological materials

(2018) Marco Buongiorno Nardelli et al.   COMPUTATIONAL MATERIALS SCIENCE

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

(2018) Julian J. Kranz et al.   Journal of Chemical Theory and Computation

Disentanglement via Entanglement: A Unified Method for Wannier Localization

(2018) Anil Damle et al.   MULTISCALE MODELING & SIMULATION

Automated construction of symmetrized Wannier-like tight-binding models from ab initio calculations

(2018) Dominik Gresch et al.   PHYSICAL REVIEW MATERIALS

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Random Gradient-Free Minimization of Convex Functions

(2015) Yurii Nesterov et al.   FOUNDATIONS OF COMPUTATIONAL MATHEMATICS

Bond-order potentials: derivation and parameterization for refractory elements

(2015) Ralf Drautz et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Interlayer interaction in general incommensurate atomic layers

(2015) Mikito Koshino   NEW JOURNAL OF PHYSICS

An updated version of wannier90: A tool for obtaining maximally-localised Wannier functions

(2014) Arash A. Mostofi et al.   COMPUTER PHYSICS COMMUNICATIONS

Effect of defects on the intrinsic strength and stiffness of graphene

(2014) Ardavan Zandiatashbar et al.   Nature Communications

Wave-Function Mapping of Graphene Quantum Dots with Soft Confinement

(2012) D. Subramaniam et al.   PHYSICAL REVIEW LETTERS

Maximally localized Wannier functions: Theory and applications

(2012) Nicola Marzari et al.   REVIEWS OF MODERN PHYSICS

Identification of active atomic defects in a monolayered tungsten disulphide nanoribbon

(2011) Zheng Liu et al.   Nature Communications

Transition to Landau levels in graphene quantum dots

(2010) F. Libisch et al.   PHYSICAL REVIEW B

Atomistic modelling of materials with bond-order potentials

(2009) Thomas Hammerschmidt et al.   International Journal of Materials Research