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RESEARCH PAPER · Physical Review Materials · 2022

Deep dive into machine learning density functional theory for materials science and chemistry

Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi

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Deep dive into machine learning density functional theory for materials science and chemistryOriginalEffect of Molecular and Electronic Geometries on the Electronic Density in FLO-SIC3CENT2: Improved charge equilibration via neural network technique28Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty7Accelerating equilibration in first-principles molecular dynamics with orbital-free density functional theory26The fourth-order expansion of the exchange hole and neural networks to construct exchange–correlation functionals4Accessible and Efficient Modeling of Chromophores with Time-Independent Excited-State Density Functional Tight-Binding: Concepts and Applications1Classical and quantum machine learning applications in spintronics9Edge Based Architecture for Total Energy Regression Models for Computational Materials Science0Machine-Learning for Static and Dynamic Electronic Structure Theory1The atom sampling method for mesoscale molecular dynamics and its application to graphene assemblies2Prediction-accuracy improvement of neural network to ferromagnetic multilayers by Gaussian data augmentation and ensemble learning11Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons26Electronic density response of warm dense matter94Recent advances in density functional theory approach for optoelectronics properties of graphene33ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training29Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data2Predicting electronic structures at any length scale with machine learning70Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter11Equation of state predictions for ScF3 and CaZrF6 with neural network-driven molecular dynamics4The rise of the machines: A state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture36Machine learning the electronic structure of matter across temperatures6Orbital-Free Density Functional Theory: An Attractive Electronic Structure Method for Large-Scale First-Principles Simulations115Graph neural networks for predicting structural stability of Cd- and Zn-doped γ -CsPbI38Physics-enhanced neural networks for equation-of-state calculations1Advancements in cathode materials for potassium-ion batteries: current landscape, obstacles, and prospects44A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning3Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter16Investigating the electronic properties of novel titanium oxonitridophosphate, Ti 5 P 12 N 24 O 2 , through structural distortions at the titanium sites4Rational Design of Battery Materials Through Spectroscopic Characterization and Computational Modeling of Redox Orbitals0Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0Development of a machine learning finite-range nonlocal density functional12Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures74Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units6Inverting the Kohn–Sham equations with physics-informed machine learning6X-ray-induced atomic transitions via machine learning: A computational investigation0Discovery of magnesium-aluminum alloys by generative model and automatic differentiation approach2Reproducibility of real-time time-dependent density functional theory calculations of electronic stopping power in warm dense matter13A review of the application of Density Functional Theory and machine learning for oxidative coupling of methane reaction for ethylene production3Two-Dimensional Carbon-Based Nanoporous Membranes for High-Performance Sustainable Desalination: A Simulation Calculation Investigation0Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning3Multi-objective Optimization-Oriented Generative Adversarial Design for Multi-principal Element Alloys13Phonon Transport in Defect-Laden Bilayer Janus PtSTe Studied Using Neural-Network Force Fields3AI for science in electrochemical energy storage: A multiscale systems perspective on transportation electrification18Untangling high-temperature thermal expansion and lattice thermal conductivity behavior of vanadium using machine-learned molecular dynamics17Quantum chemical package Jaguar: A survey of recent developments and unique features62Ensemble Generalization of the Perdew–Zunger Self-Interaction Correction: A Way Out of Multiple Minima and Symmetry Breaking4Ab Initio Calculation of Coupling-Constant Averaged Exchange–Correlation Holes for Spherically Symmetric Atoms2Generalizing deep learning electronic structure calculation to the plane-wave basis32Bridging the gap in electronic structure calculations via machine learning2Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments6Electronic structure simulations in the cloud computing environment10Toward first principles-based simulations of dense hydrogen92A Systematic Review of Adversarial Machine Learning and Deep Learning Applications2A Database of Experiments for the Development of Sulfide-Type Solid Electrolytes1Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study3On machine learnability of local contributions to interatomic potentials from density functional theory calculations4Machine learning approach for predicting optical and photothermal properties of gold nanoparticle/polymer hybrid films: Effect of synthetic data3Revisiting Optical Properties of BaLiX3(X = Cl, Br, I) Using First-Principles Calculations with Full-Potential DFT Code WIEN2k0Phenylalanine and Tyrosine Interactions with the WSe2 Nanosheet: A Comparative Analysis Based on DFT Calculation and Experimental Confirmation17Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Lone Pair and Unique N‐Bridging of Novel Titanium Nitridophosphate2Topological Hall Effect of Skyrmions from first Principles4Regressing the self-consistent Hamiltonian and density from the non-self-consistent Hamiltonian0PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals4Applications of density functional theory and machine learning in nanomaterials: A review51Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations8Machine Learning in nanoarchitectonics24Recent advances in silver oxide-based superconductors: characterization, challenges and theoretical progress0Predicting Optical Bandgaps in C60 and Functionalized Derivatives from Limited Data for Renewable Energy Applications0Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications7Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques24Graph neural networks to predict atomic transition charges and exciton couplings in organic semiconductors2Density-functional Green function theory: dynamical exchange-correlation field in lieu of self-energy0Artificial intelligence for science in quantum, atomistic, and continuum systems71Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials1AI-Driven Computational Materials Science for Advanced Energy Materials Development2Review of Artificial Intelligence Applications in Performance Prediction of Advanced Energy Materials2Deep learning for simulating the evolution of condensed matter systems at the continuum scale: methods and applications1Self-learning quantum Monte Carlo: Application to the classical Holstein spin-fermion model1Transformer-based prediction of dispersion relation and transmittance in phononic crystals8Atomistic Modeling of Valence Change Memory Devices: What Can We Learn from Simulations?2A Computational Renaissance in High-Energy Density Materials (HEDMs) Research38Electronic structure calculation methods for surface catalysis0Real-space machine learning of correlation density functionals13Scalable machine learning model for energy decomposition analysis in aqueous systems0Electronic origin and interfacial stability of the newly identified Al5Sc2Si phase in Sc micro-alloyed Al–Mg–Si–Cu alloys: A first-principles and experimental study1An FFT-oriented performance analysis and optimization of Quantum ESPRESSO on top-tier EuroHPC systems0From first principles to machine learning: accelerating the design and discovery of hybrid organic–inorganic perovskites0Informatics for thermodynamics: Stability to synthesizability0Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces5Recent advances in carbon-based materials for supercapacitors: synthesis, electrochemical properties, and impact of machine learning12The CP2K Program Package Made Simple27AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Beyond Algorithms0Quantum Science Beyond the Hype: Facts, Myths, and Realistic Progress in Physics, Chemistry, and Computing0Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules6Synthesis, antimicrobial activity, and DFT study of 5-benzoyl-2-(2,5-dimethylphenyl)-4-hydroxy-6-phenylpyridazin-3(2 H )-one0Machine learning for functional materials: insights from compositional, electronic, and dynamic processes3Machine learning time propagators for time-dependent density functional theory simulations1Recent advances in electronic structure learning0Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS1Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design0Sustainable Electrochemical Synthesis of High‐Quality MXenes: Mechanistic Insights, Applications, Challenges, and Technological Prospects1Thermal transport and photothermal conversion in perovskite quantum dot-based nanofluids: Insights from materials design to computational modeling0Essay: Pushing the Frontiers of High Energy Density Science toward Unexplored States of Matter0Screening foundational machine learning models using the adsorption of thiophene on transition metal surfaces0Machine Learning in Molecular Dynamics from Reactive Chemistry to Astrophysical Systems0Enhancing Machine-Learned Electrostatic Models with Linear Response Theory for Multiscale Simulations in Enzymes0Machine-learning-driven investigation of dopant-induced modulation of electronic properties in two-dimensional SnO0Machine learning-guided first-principles screening of halide double perovskites for optoelectronic applications0Machine-learned exchange–correlation functionals via random polynomial kernels: a reduced orbital-free variational theory0

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