|
The GW approximation: content, successes and limitations: The GW approximation
|
journal
|
December 2017 |
|
Approximation by superpositions of a sigmoidal function
|
journal
|
December 1989 |
|
A new determinant-based full configuration interaction method
|
journal
|
November 1984 |
|
A complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach
|
journal
|
May 1980 |
|
Approximation capabilities of multilayer feedforward networks
|
journal
|
January 1991 |
|
Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning
|
journal
|
May 2019 |
|
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
|
journal
|
October 2021 |
|
BerkeleyGW: A massively parallel computer package for the calculation of the quasiparticle and optical properties of materials and nanostructures
|
journal
|
June 2012 |
|
Large-scale GW calculations on pre-exascale HPC systems
|
journal
|
February 2019 |
|
DScribe: Library of descriptors for machine learning in materials science
|
journal
|
February 2020 |
|
Critical review of machine learning applications in perovskite solar research
|
journal
|
February 2021 |
|
Fundamentals of Condensed Matter Physics
|
book
|
January 2019 |
|
Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
|
journal
|
May 2018 |
|
Machine Learning Many-Body Green’s Functions for Molecular Excitation Spectra
|
journal
|
December 2023 |
|
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
|
journal
|
April 2019 |
|
Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction
|
journal
|
December 2020 |
|
Machine Learning Accelerated Study of Defect Energy Levels in Perovskites
|
journal
|
June 2023 |
|
Predicting the Band Gaps of Inorganic Solids by Machine Learning
|
journal
|
March 2018 |
|
Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning
|
journal
|
February 2024 |
|
Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach
|
journal
|
May 2022 |
|
Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing
|
journal
|
September 2020 |
|
Spin-Stabilization by Coulomb Blockade in a Vanadium Dimer in WSe2
|
journal
|
November 2023 |
|
Challenges for Density Functional Theory
|
journal
|
December 2011 |
|
Large Scale GW Calculations
|
journal
|
May 2015 |
|
Unconventional superconductivity in magic-angle graphene superlattices
|
journal
|
March 2018 |
|
Machine learning phases of matter
|
journal
|
February 2017 |
|
Learning phase transitions by confusion
|
journal
|
February 2017 |
|
Quantum machine learning for electronic structure calculations
|
journal
|
October 2018 |
|
Representing individual electronic states for machine learning GW band structures of 2D materials
|
journal
|
February 2022 |
|
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
|
journal
|
May 2022 |
|
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
|
journal
|
May 2023 |
|
Machine learning the microscopic form of nematic order in twisted double-bilayer graphene
|
journal
|
August 2023 |
|
Towards fully automated GW band structure calculations: What we can learn from 60.000 self-energy evaluations
|
journal
|
January 2021 |
|
Machine learning for perovskite materials design and discovery
|
journal
|
January 2021 |
|
Evolutionary computing and machine learning for discovering of low-energy defect configurations
|
journal
|
May 2021 |
|
Machine learning sparse tight-binding parameters for defects
|
journal
|
May 2022 |
|
Predicting electronic structures at any length scale with machine learning
|
journal
|
June 2023 |
|
Accelerating GW calculations through machine-learned dielectric matrices
|
journal
|
October 2023 |
|
Learning hard quantum distributions with variational autoencoders
|
journal
|
June 2018 |
|
Discovering and understanding materials through computation
|
journal
|
May 2021 |
|
The marvels of moiré materials
|
journal
|
March 2021 |
|
Machine learning quantum phases of matter beyond the fermion sign problem
|
journal
|
August 2017 |
|
Reconstructing quantum states with generative models
|
journal
|
March 2019 |
|
Neural network-based order parameter for phase transitions and its applications in high-entropy alloys
|
journal
|
October 2021 |
|
Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
|
journal
|
June 2022 |
|
Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions
|
journal
|
November 2016 |
|
Understanding, discovery, and synthesis of 2D materials enabled by machine learning
|
journal
|
January 2022 |
|
Machine learning dielectric screening for the simulation of excited state properties of molecules and materials
|
journal
|
January 2021 |
|
A full coupled‐cluster singles and doubles model: The inclusion of disconnected triples
|
journal
|
February 1982 |
|
Perspective on density functional theory
|
journal
|
April 2012 |
|
Perspective: Fifty years of density-functional theory in chemical physics
|
journal
|
May 2014 |
|
Modeling the dielectric constants of crystals using machine learning
|
journal
|
July 2020 |
|
Computational design of moiré assemblies aided by artificial intelligence
|
journal
|
September 2021 |
|
Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
|
journal
|
July 2022 |
|
The GW method
|
journal
|
March 1998 |
|
The atomic simulation environment—a Python library for working with atoms
|
journal
|
June 2017 |
|
The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals
|
journal
|
September 2018 |
|
Recent progress of the Computational 2D Materials Database (C2DB)
|
journal
|
July 2021 |
|
From DFT to machine learning: recent approaches to materials science–a review
|
journal
|
May 2019 |
|
Machine learning assisted quantum state estimation
|
journal
|
July 2020 |
|
New Method for Calculating the One-Particle Green's Function with Application to the Electron-Gas Problem
|
journal
|
August 1965 |
|
Self-Consistent Equations Including Exchange and Correlation Effects
|
journal
|
November 1965 |
|
Unsupervised detection of decoupled subspaces: Many-body scars and beyond
|
journal
|
June 2022 |
|
Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies
|
journal
|
October 1986 |
|
Accurate G W self-energies in a plane-wave basis using only a few empty states: Towards large systems
|
journal
|
August 2008 |
|
GW quasiparticle spectra from occupied states only
|
journal
|
March 2010 |
|
GW method with the self-consistent Sternheimer equation
|
journal
|
March 2010 |
|
Ab initio calculations of electronic excitations: Collapsing spectral sums
|
journal
|
July 2010 |
|
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
|
journal
|
May 2014 |
|
Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques
|
journal
|
March 2016 |
|
Principal component analysis for fermionic critical points
|
journal
|
November 2017 |
|
Kernel methods for interpretable machine learning of order parameters
|
journal
|
November 2017 |
|
Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders
|
journal
|
August 2017 |
|
Quasiparticle Band Gap of ZnO: High Accuracy from the Conventional G 0 W 0 Approach
|
journal
|
September 2010 |
|
Simple Approximate Physical Orbitals for G W Quasiparticle Calculations
|
journal
|
October 2011 |
|
Optical Spectrum of MoS 2 : Many-Body Effects and Diversity of Exciton States
|
journal
|
November 2013 |
|
Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
|
journal
|
March 2015 |
|
Quantum Loop Topography for Machine Learning
|
journal
|
May 2017 |
|
Machine Learning Topological Invariants with Neural Networks
|
journal
|
February 2018 |
|
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
|
journal
|
April 2018 |
|
Unsupervised Machine Learning and Band Topology
|
journal
|
June 2020 |
|
Mixed Stochastic-Deterministic Approach for Many-Body Perturbation Theory Calculations
|
journal
|
February 2024 |
|
Space-Time Method for Ab Initio Calculations of Self-Energies and Dielectric Response Functions of Solids
|
journal
|
March 1995 |
|
Seeing moiré: Convolutional network learning applied to twistronics
|
journal
|
December 2022 |
|
Quantum Monte Carlo simulations of solids
|
journal
|
January 2001 |
|
Coupled-cluster theory in quantum chemistry
|
journal
|
February 2007 |
|
Machine learning and the physical sciences
|
journal
|
December 2019 |
|
Gradient-based learning applied to document recognition
|
journal
|
January 1998 |
|
Circular Convolutional Neural Networks for Panoramic Images and Laser Data
|
conference
|
June 2019 |
|
Representation Learning: A Review and New Perspectives
|
journal
|
August 2013 |
|
On the Experimental Feasibility of Quantum State Reconstruction via Machine Learning
|
journal
|
January 2021 |
|
Electronic structure at coarse-grained resolutions from supervised machine learning
|
journal
|
March 2019 |
|
Shedding light on moiré excitons: A first-principles perspective
|
journal
|
October 2020 |
|
ImageNet classification with deep convolutional neural networks
|
journal
|
May 2017 |
|
Disentangled Representation Learning and Generation With Manifold Optimization
|
journal
|
September 2022 |
|
A geometric viewpoint of manifold learning
|
journal
|
March 2015 |
|
From Real Materials to Model Hamiltonians With Density Matrix Downfolding
|
journal
|
May 2018 |
|
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
|
preprint
|
January 2014 |
|
Going Deeper with Convolutions
|
preprint
|
January 2014 |
|
Spatial Transformer Networks
|
preprint
|
January 2015 |
|
Spectral Operator Representations
|
preprint
|
January 2024 |
|
Unsupervised Learning of Individual Kohn-Sham States: Interpretable Representations and Consequences for Downstream Predictions of Many-Body Effects
|
preprint
|
January 2024 |