npj Computational Materials

Article	Citations
Recent advances and applications of deep learning methods in materials science	359
Atomistic Line Graph Neural Network for improved materials property predictions	245
Machine learning for perovskite materials design and discovery	231
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design	224
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy	172
Small data machine learning in materials science	145
Benchmarking graph neural networks for materials chemistry	144
Accelerating materials discovery using artificial intelligence, high performance computing and robotics	119
Machine learning in concrete science: applications, challenges, and best practices	119
Mechanism of keyhole pore formation in metal additive manufacturing	118
Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening	117
Two-step machine learning enables optimized nanoparticle synthesis	105
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods	102
Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon	101
Two-dimensional Stiefel-Whitney insulators in liganded Xenes	99
Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization	96
Explainable machine learning in materials science	94
Understanding and design of metallic alloys guided by phase-field simulations	92
Deep learning framework for material design space exploration using active transfer learning and data augmentation	92
MatSciBERT: A materials domain language model for text mining and information extraction	86
Compositionally restricted attention-based network for materials property predictions	86
Machine-learned interatomic potentials for alloys and alloy phase diagrams	86
A review of the recent progress in battery informatics	84
Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture	81
Band degeneracy enhanced thermoelectric performance in layered oxyselenides by first-principles calculations	81

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials	79
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains	77
Sign-reversible valley-dependent Berry phase effects in 2D valley-half-semiconductors	75
Predominance of non-adiabatic effects in zero-point renormalization of the electronic band gap	75
Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials	74
Computational screening study of double transition metal carbonitrides M′2M″CNO2-MXene as catalysts for hydrogen evolution reaction	72
Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms	72
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning	71
Restructured single parabolic band model for quick analysis in thermoelectricity	71
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration	70
Application of phase-field method in rechargeable batteries	68
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures	68
Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe	65
Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials	65
Machine learning property prediction for organic photovoltaic devices	63
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing	62
Computational high-throughput screening of alloy nanoclusters for electrocatalytic hydrogen evolution	61
High performance Wannier interpolation of Berry curvature and related quantities with WannierBerri code	61
Nanodevices engineering and spin transport properties of MnBi2Te4 monolayer	60
Learning two-phase microstructure evolution using neural operators and autoencoder architectures	60
Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts	56
Electrically and magnetically switchable nonlinear photocurrent in РТ-symmetric magnetic topological quantum materials	56
Design high-entropy carbide ceramics from machine learning	55
The origin of the lattice thermal conductivity enhancement at the ferroelectric phase transition in GeTe	53
Damage mechanism identification in composites via machine learning and acoustic emission	53
Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses	52
Predicting stable crystalline compounds using chemical similarity	51
Ab initio molecular dynamics and materials design for embedded phase-change memory	51
Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet	51
2D spontaneous valley polarization from inversion symmetric single-layer lattices	51
A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts	50
Electron correlation effects on exchange interactions and spin excitations in 2D van der Waals materials	49
Machine learning potentials for metal-organic frameworks using an incremental learning approach	49
Data-driven discovery of 2D materials by deep generative models	48
Low-dimensional non-metal catalysts: principles for regulating p-orbital-dominated reactivity	48
Designing polymer nanocomposites with high energy density using machine learning	47
A machine-learned interatomic potential for silica and its relation to empirical models	47
Neural network reactive force field for C, H, N, and O systems	47
Identifying the ground state structures of point defects in solids	47
Active learning for the power factor prediction in diamond-like thermoelectric materials	47
A general and transferable deep learning framework for predicting phase formation in materials	46
Nanometer-size Na cluster formation in micropore of hard carbon as origin of higher-capacity Na-ion battery	46
Pure bulk orbital and spin photocurrent in two-dimensional ferroelectric materials	46
Bayesian optimization with adaptive surrogate models for automated experimental design	45
Diverse electronic and magnetic properties of CrS2 enabling strain-controlled 2D lateral heterostructure spintronic devices	45
Electron–phonon physics from first principles using the EPW code	44
Discovering plasticity models without stress data	44
Rapid and flexible segmentation of electron microscopy data using few-shot machine learning	44
Machine learning and evolutionary prediction of superhard B-C-N compounds	43
Computational design for 4D printing of topology optimized multi-material active composites	43

Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials	43
Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity	43
TransPolymer: a Transformer-based language model for polymer property predictions	43
High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions	42
Quantum point defects in 2D materials - the QPOD database	42
Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning	42
Tunable sliding ferroelectricity and magnetoelectric coupling in two-dimensional multiferroic MnSe materials	41
Uncertainty quantification and reduction in metal additive manufacturing	41
Topological representations of crystalline compounds for the machine-learning prediction of materials properties	41
Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset	40
Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning	40
PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys	40
Machine learning of superconducting critical temperature from Eliashberg theory	39
Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure	39
Coalescence of Al0.3CoCrFeNi polycrystalline high-entropy alloy in hot-pressed sintering: a molecular dynamics and phase-field study	39
Bimeron clusters in chiral antiferromagnets	38
Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters	38
AtomSets as a hierarchical transfer learning framework for small and large materials datasets	38
Specialising neural network potentials for accurate properties and application to the mechanical response of titanium	38
Phase classification of multi-principal element alloys via interpretable machine learning	38
Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials	37
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene	37
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning	37
Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy	37
Strong quartic anharmonicity, ultralow thermal conductivity, high band degeneracy and good thermoelectric performance in Na2TlSb	37
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data	36
In-silico synthesis of lowest-pressure high-Tc ternary superhydrides	36
Automation of diffusion database development in multicomponent alloys from large number of experimental composition profiles	36
Deep learning for visualization and novelty detection in large X-ray diffraction datasets	36
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics	36
Hybrid magnetorheological elastomers enable versatile soft actuators	35
An atomistic approach for the structural and electronic properties of twisted bilayer graphene-boron nitride heterostructures	35
Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach	35
Colossal switchable photocurrents in topological Janus transition metal dichalcogenides	35
Machine learning assisted design of shape-programmable 3D kirigami metamaterials	35
Predicting lattice thermal conductivity via machine learning: a mini review	34
A scheme for simulating multi-level phase change photonics materials	34
Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality	34
A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing	34
Phase field modeling for the morphological and microstructural evolution of metallic materials under environmental attack	34
Optimal band structure for thermoelectrics with realistic scattering and bands	34
High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity	34
Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis	34
A nonlinear magnonic nano-ring resonator	34
Automated pipeline for superalloy data by text mining	33
AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes	33
Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines	33
Viscosity in water from first-principles and deep-neural-network simulations	32
Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics	32
Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength	32
Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects	32
Prediction of high thermoelectric performance in the low-dimensional metal halide Cs3Cu2I5	32
Stone–Wales defects in hexagonal boron nitride as ultraviolet emitters	32
Van der Waals force-induced intralayer ferroelectric-to-antiferroelectric transition via interlayer sliding in bilayer group-IV monochalcogenides	32
Microscopic mechanism of unusual lattice thermal transport in TlInTe2	32
Dynamic observation of dendrite growth on lithium metal anode during battery charging/discharging cycles	31
Radiative properties of quantum emitters in boron nitride from excited state calculations and Bayesian analysis	31
Inverse design of truss lattice materials with superior buckling resistance	31
Topology-optimized thermal metamaterials traversing full-parameter anisotropic space	31
Bayesian optimization with active learning of design constraints using an entropy-based approach	31
First-principles search of hot superconductivity in La-X-H ternary hydrides	31
Machine-learning atomic simulation for heterogeneous catalysis	30
Composition design of high-entropy alloys with deep sets learning	30
Segregation-assisted spinodal and transient spinodal phase separation at grain boundaries	30
A critical examination of robustness and generalizability of machine learning prediction of materials properties	30
Perovskite synthesizability using graph neural networks	30
How coherence is governing diffuson heat transfer in amorphous solids	29
Evolutionary computing and machine learning for discovering of low-energy defect configurations	29
Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks	29
Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning	29
Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning	29
Ferroelectricity coexisted with p-orbital ferromagnetism and metallicity in two-dimensional metal oxynitrides	29
Compressing local atomic neighbourhood descriptors	29
Intersystem crossing and exciton–defect coupling of spin defects in hexagonal boron nitride	28
High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory	28
The ferroelectric field-effect transistor with negative capacitance	28
Modelling charge transport and electro-optical characteristics of quantum dot light-emitting diodes	28
Comparing crystal structures with symmetry and geometry	28
AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging	28
Switchable Rashba anisotropy in layered hybrid organic–inorganic perovskite by hybrid improper ferroelectricity	28

Prediction of protected band edge states and dielectric tunable quasiparticle and excitonic properties of monolayer MoSi2N4	28
A systematic approach to generating accurate neural network potentials: the case of carbon	28
Switchable half-metallicity in A-type antiferromagnetic NiI2 bilayer coupled with ferroelectric In2Se3	27
Crystal twins: self-supervised learning for crystalline material property prediction	27
Physics guided deep learning for generative design of crystal materials with symmetry constraints	27
Multifunctional two-dimensional van der Waals Janus magnet Cr-based dichalcogenide halides	27
Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images	27
A multiscale polymerization framework towards network structure and fracture of double-network hydrogels	27
Topological feature engineering for machine learning based halide perovskite materials design	27
Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning	27
Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium	27
Artificial generation of representative single Li-ion electrode particle architectures from microscopy data	27
Multifunctional antiperovskites driven by strong magnetostructural coupling	26
Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning	26
Emergence of local scaling relations in adsorption energies on high-entropy alloys	26
Hole-doping induced ferromagnetism in 2D materials	26
Low-overhead distribution strategy for simulation and optimization of large-area metasurfaces	26
Antiferromagnetic second-order topological insulator with fractional mass-kink	26
Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework	26
Hyperactive learning for data-driven interatomic potentials	26
Effect of exchange-correlation functionals on the estimation of migration barriers in battery materials	26
Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning	26
MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art	26
Towards fully automated GW band structure calculations: What we can learn from 60.000 self-energy evaluations	25
Physics-embedded graph network for accelerating phase-field simulation of microstructure evolution in additive manufacturing	25
Coexisting charge density wave and ferromagnetic instabilities in monolayer InSe	25
Accurate large-scale simulations of siliceous zeolites by neural network potentials	25
On the possibility that PbZrO3 not be antiferroelectric	25
Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls	24
Phase diagram of a distorted kagome antiferromagnet and application to Y-kapellasite	24
The kinetics of static recovery by dislocation climb	24
Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC	24
Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization	24
Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models	24
Suppressing the ferroelectric switching barrier in hybrid improper ferroelectrics	24
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials	24
Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys	24
Thermodynamics and dielectric response of BaTiO3 by data-driven modeling	23
Calibration after bootstrap for accurate uncertainty quantification in regression models	23
Prediction of the Curie temperature considering the dependence of the phonon free energy on magnetic states	23
Temperature and composition dependent screw dislocation mobility in austenitic stainless steels from large-scale molecular dynamics	23
In-plane ferroelectric tunnel junctions based on 2D α-In2Se3/semiconductor heterostructures	23
Range-separated hybrid functionals for accurate prediction of band gaps of extended systems	23
Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity	23
Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes	23
Intriguing magnetoelectric effect in two-dimensional ferromagnetic/perovskite oxide ferroelectric heterostructure	23
The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations	23
INDEEDopt: a deep learning-based ReaxFF parameterization framework	23
Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature	23
Inverse design of two-dimensional materials with invertible neural networks	23
Materials information and mechanical response of TRIP/TWIP Ti alloys	23
Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy	23
The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding	23
High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts	23
Structural and chemical mechanisms governing stability of inorganic Janus nanotubes	23
Common microscopic origin of the phase transitions in Ta2NiS5 and the excitonic insulator candidate Ta2NiSe5	23
Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing	22
High-throughput reaction engineering to assess the oxidation stability of MAX phases	22
Training data selection for accuracy and transferability of interatomic potentials	22
The complementary graphene growth and etching revealed by large-scale kinetic Monte Carlo simulation	22
Strong electron–phonon coupling influences carrier transport and thermoelectric performances in group-IV/V elemental monolayers	22
Chemically induced local lattice distortions versus structural phase transformations in compositionally complex alloys	22
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning	22
Degenerate topological line surface phonons in quasi-1D double helix crystal SnIP	22
High-throughput computation and structure prototype analysis for two-dimensional ferromagnetic materials	22
How dopants limit the ultrahigh thermal conductivity of boron arsenide: a first principles study	22
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks	22
Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloys	22
Achieving a high dielectric tunability in strain-engineered tetragonal K0.5Na0.5NbO3 films	22
Automated stopping criterion for spectral measurements with active learning	22
Dzyaloshinskii–Moriya interaction in noncentrosymmetric superlattices	22
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment	21
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models	21
Nonvolatile electrical control of spin polarization in the 2D bipolar magnetic semiconductor VSeF	21
Thermal conductivity of glasses: first-principles theory and applications	21
Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass	21
Correcting the corrections for charged defects in crystals	21
Anisotropic Dzyaloshinskii-Moriya interaction protected by D2d crystal symmetry in two-dimensional ternary compounds	21
Superior printed parts using history and augmented machine learning	21
Chiral logic computing with twisted antiferromagnetic magnon modes	21
Quantum anomalous hall effect in collinear antiferromagnetism	21
Relativistic domain-wall dynamics in van der Waals antiferromagnet MnPS3	21
An infrastructure with user-centered presentation data model for integrated management of materials data and services	21
Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations	21
Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning	21
High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials	21
Environmental screening and ligand-field effects to magnetism in CrI3 monolayer	21
Predicting elastic properties of hard-coating alloys using ab-initio and machine learning methods	20
Screening transition metal-based polar pentagonal monolayers with large piezoelectricity and shift current	20
Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys	20
High-throughput design of functional-engineered MXene transistors with low-resistive contacts	20
Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications	20
Different types of spin currents in the comprehensive materials database of nonmagnetic spin Hall effect	20
Visualizing temperature-dependent phase stability in high entropy alloys	20
Toward room-temperature nanoscale skyrmions in ultrathin films	20