npj Computational Materials

Article	Citations
Sparse representation for machine learning the properties of defects in 2D materials	743
Multiscale kinetic model of ethylene oligomerization in Ni-NU-1000 metal-organic framework	391
Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials	234
Author Correction: Active learning for accelerated design of layered materials	214
cmtj: Simulation package for analysis of multilayer spintronic devices	179
Crosslinking degree variations enable programming and controlling soft fracture via sideways cracking	138
Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app	136
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene	135
First principles methodology for studying magnetotransport in narrow gap semiconductors with ZrTe5 example	134
Strain and ligand effects in the 1-D limit: reactivity of steps	133
Unlocking 3D nanoparticle shapes from 2D high-resolution transmission electron microscopy images: a deep learning approach	133
Void suppression during vacancy aggregation in concentrated solid solution alloys using self-adaptive accelerated molecular dynamics	127
Networking autonomous material exploration systems through transfer learning	127
SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction	120
Machine learning-aided first-principles calculations of redox potentials	117
Structure and properties of graphullerene: a semiconducting two-dimensional C60 crystal	117
Dynamical mean field theory for real materials on a quantum computer	111
Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys	111
JARVIS-Leaderboard: a large scale benchmark of materials design methods	111
Dynamical phase-field model of cavity electromagnonic systems	108
Quantum anomalous hall effect in collinear antiferromagnetism	108
Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning	108
Machine learning enhanced analysis of EBSD data for texture representation	107
Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network	105
AI-assisted rapid crystal structure generation towards a target local environment	104

Identifying the ground state structures of point defects in solids	101
Probing multi-dimensional composition spaces in search of strong metallic alloys	100
FALCON: fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations	99
Robust electron counting for direct electron detectors with the Back-propagation counting method	99
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics	97
Vibrationally resolved optical excitations of the nitrogen-vacancy center in diamond	97
Accelerating sustainable glass discovery: integrating molecular dynamics, machine learning, and robotic synthesis	97
Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material	97
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings	94
A critical examination of robustness and generalizability of machine learning prediction of materials properties	94
Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls	92
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints	90
Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides	87
Ultra-fast interpretable machine-learning potentials	85
Active learning of effective Hamiltonian for super-large-scale atomic structures	85
Revealing the evolution of order in materials microstructures using multi-modal computer vision	84
DiffCrysGen: a generative diffusion model for accelerated design of inorganic crystalline materials	82
Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene	82
Accelerating electron diffraction analysis using graph neural networks and attention mechanisms	81
DFT insights into single-atom Fe-anchored N-doped multilayer graphene for ORR and OER bifunctional catalysis	81
High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy	81
Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds	80
Combined study of phase transitions in the P2-type NaXNi1/3Mn2/3O2 cathode material: experimental, ab-initio and multiphase-field results	80
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability	79
Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis	79
High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling	79
Electro-chemo-mechanical modelling of structural battery composite full cells	78
First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions	78
From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models	77
Raman signatures of single point defects in hexagonal boron nitride quantum emitters	77
MolAtlas: a visualization framework for molecular property distributions to guide functional molecule development	74
Tunable sliding ferroelectricity and magnetoelectric coupling in two-dimensional multiferroic MnSe materials	74
Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics	72
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules	72
Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching	72
Ultrafast laser-driven topological spin textures on a 2D magnet	72
A process-synergistic active learning framework for high-strength Al-Si alloys design	71
Graph atomic cluster expansion for foundational machine learning interatomic potentials	70
Scalable integrated framework for discovering high-performance MR-TADF emitters via combinatorial tree search	70
Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration	70
Agent-based multimodal information extraction for nanomaterials	69
Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties	69
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning	69
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty	69
Data-driven transfer learning across MOF-derived zirconia polymorphs	68
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows	68
A machine learning approach to designing and understanding tough, degradable polyamides	68
Comment on “Machine learning enhanced analysis of EBSD data for texture representation”	67
Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing	67
Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode	67

Element mapping-based Bayesian optimization framework enabling direct materials design: a case study on NASICON-type cathode materials	66
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function	66
Flat topological nodal lines in heavy-fermion compound CeCoGe3	66
MOFBuilder: automated end-to-end modeling of MOF dynamics for high-throughput screening	65
Theory of non-Hermitian topological whispering gallery	65
High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning	64
Author Correction: Characterization of domain distributions by second harmonic generation in ferroelectrics	64
Author Correction: High-throughput study of the anomalous Hall effect	63
Predicting temperature-dependent optoelectronic properties of semiconductor defects with equivariant neural networks	62
Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals	62
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction	62
High-entropy solid electrolytes discovery: a dual-stage machine learning framework bridging atomic configurations and ionic transport properties	62
Ab initio dynamical mean field theory with natural orbitals renormalization group impurity solver	62
Elucidation of molecular-level charge transport in an organic amorphous system	62
Pushing charge equilibration-based machine learning potentials to their limits	61
Magnetic wallpaper Dirac fermions and topological magnetic Dirac insulators	60
Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions	59
Transition state structure detection with machine learningś	59
High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery	59
Photoinduced ferroelectric phase transition triggering photocatalytic water splitting	58
Exploring superionic conduction in lithium oxyhalide solid electrolytes considering composition and structural factors	58
Optimizing casting process using a combination of small data machine learning and phase-field simulations	58
Prediction of ambient pressure conventional superconductivity above 80 K in hydride compounds	57
Deep convolutional neural networks to restore single-shot electron microscopy images	57
Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys	56
Electronic structure prediction of medium and high entropy alloys across composition space	55
Data-driven low-rank approximation for the electron-hole kernel and acceleration of time-dependent GW calculations	55
Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy	54
Promising ferroelectric metal EuAuBi with switchable giant shift current	54
Computational morphogenesis for liquid crystal elastomer metamaterial	54
Machine learning on multiple topological materials datasets	54
Lanthanide molecular nanomagnets as probabilistic bits	54
High-throughput discovery of perturbation-induced topological magnons	54
Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature	53
A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V	53
Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning	52
Prediction and tuning of altermagnetic magnon splitting in RFeO3: non-relativistic and relativistic perspectives	52
Origin of the insulating phase and metal-insulator transition in the organic molecular solid κ-(BEDT-TTF)2Cu2(CN)3	52
Unified generalized universal equation of states for magnetic Co, Cr, Fe, Mn and Ni: an approach for non-collinear atomistic modelling	52
Why is the strength of an elastomeric polymer network so low	52
Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning	52
Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints	52
Concurrent multi-peak Bragg coherent x-ray diffraction imaging of 3D nanocrystal lattice displacement via global optimization	52
GEMDAT: a Python toolkit for site-resolved diffusion analysis in solid-state molecular dynamics	52
Data-driven discovery of methane hydrate promoters	52
SLM-MATRIX: a multi-agent trajectory reasoning and verification framework for enhancing language models in materials data extraction	51
Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning	51
Modeling of ultrafast X-ray induced magnetization dynamics in magnetic multilayer systems	51
Machine learning-assisted high-throughput screening of superlattice-like O-PCM thin films	50
A computational framework for guiding the MOCVD-growth of wafer-scale 2D materials	50
AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity	50
Physically interpretable interatomic potentials via symbolic regression and reinforcement learning	49
An NV− center in magnesium oxide as a spin qubit for hybrid quantum technologies	48
Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling	48
Machine learning Hamiltonian enables scalable and accurate defect calculations: the case of oxygen vacancies in amorphous SiO2	48
Scalable foundation interatomic potentials via message-passing pruning and graph partitioning	48
First-principles approach to spin excitations in noncollinear magnetic systems	48
Superior printed parts using history and augmented machine learning	47
Unraveling dislocation-based strengthening in refractory multi-principal element alloys	47
Dynamics of lattice disorder in perovskite materials, polarization nanoclusters and ferroelectric domain wall structures	47
General invariance and equilibrium conditions for lattice dynamics in 1D, 2D, and 3D materials	46
Unraveling charge effects on interface reactions and dendrite growth in lithium metal anode	46
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments	46
nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems	46
Optimal pre-train/fine-tune strategies for accurate material property predictions	46
Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy	46
Automated phase mapping of high-throughput X-ray diffraction data encoded with domain-specific materials science knowledge	46
Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs	46
Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy	45
PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena	44
Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement	44
Tunable Schottky barriers and magnetoelectric coupling driven by ferroelectric polarization reversal of MnI3/In2Se3 multiferroic heterostructures	44
PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase	43
A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning	43
Fast prediction of anharmonic vibrational spectra for complex organic molecules	43
EMFF-2025: a general neural network potential for energetic materials with C, H, N, and O elements	43
Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations	43
Magnon shake-up: entanglement generation and sensing	43
Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport	42
Rational design of large anomalous Nernst effect in Dirac semimetals	42

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design	42
Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders	42
From ultrathin to bulk: decoding thickness-unrestricted ferroelectricity in Y:HfO2 via first-principles	42
Designing non-Van der Waals two-dimensional materials via a layer-intercalation strategy with tailorable ferroelectric, magnetic, and photocatalytic properties	42
Efficient first-principles electronic transport approach to complex band structure materials: the case of n-type Mg3Sb2	41
Cavity control of multiferroic order in single-layer NiI2	41
Spin textures and spin-charge interconversion in two-dimensional trigonal materials	41
Learning atomic forces from uncertainty-calibrated adversarial attacks	41
Generalized first-principles prediction of hydrogen para-equilibrium thermodynamics in metal hydrides	41
Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals	41
Ferroelectric order in hybrid organic-inorganic perovskite NH4PbI3 with non-polar molecules and small tolerance factor	40
Efficient simulations of charge density waves in the transition metal Dichalcogenide TiSe2	40
Self-driving manufacturing: accelerating materials discovery with adaptive closed-loop processing	40
Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys	40
The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding	39
Collecting diverse near-optimal samples via nested Thompson sampling	39
Inverse design of metal–organic frameworks for C2H4/C2H6 separation	39
Magnetic and moiré proximity effects in WSe2/WSe2/CrI3 trilayers	39
Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials	39
Crystal structure prediction at finite temperatures	38
Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids	38
The dual role of 90° domain walls in ferroelectric switching of Hf0.5Zr0.5O2 thin films: Insights from phase-field simulations	38
Explainable machine learning-enabled dual-objective design of γ' phase characteristic parameters in γ'-strengthened Co-based superalloys	37
Enhanced shift current in GeTe/SnSe heterostructures for bulk photovoltaic effect	37
Phonon density of states prediction from the phonon transformer	37
Infrared markers of topological phase transitions in quantum spin Hall insulators	37
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules	37
Obtaining auxetic and isotropic metamaterials in counterintuitive design spaces: an automated optimization approach and experimental characterization	37
No ground truth needed: unsupervised sinogram inpainting for nanoparticle electron tomography (UsiNet) to correct missing wedges	37
Targeted materials discovery using Bayesian algorithm execution	37
Molecular descriptors for high-throughput virtual screening of fluorescence emitters with inverted singlet-triplet energy gaps	36
Ab initio theory of the nonequilibrium adsorption energy	36
Computational screening of sodium solid electrolytes through unsupervised learning	36
Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling	36
Machine learning guided high-throughput search of non-oxide garnets	36
Finite-temperature screw dislocation core structures and dynamics in α-titanium	36
Kohn–Sham time-dependent density functional theory with Tamm–Dancoff approximation on massively parallel GPUs	35
Perturbative solution of fermionic sign problem in quantum Monte Carlo computations	34
‘Interaction annealing’ to determine effective quantized valence and orbital structure: an illustration with ferro-orbital order in WTe2	34
Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark	34
Single atom iron promotes CS hydrogenation on interstellar grain analogues	34
High-throughput exfoliation of multiferroic ternary oxide monolayers with high transition temperature and giant spin splitting	34
Solids that are also liquids: elastic tensors of superionic materials	34
Guided diffusion for the discovery of new superconductors	34
Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders	34
Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection	34
Reply to: An explanation for the Rule of Four in Inorganic Materials	34
Dielectric properties of disordered crystalline materials: a computational case study on hexagonal ice	34
Exploring parameter dependence of atomic minima with implicit differentiation	34
Angular relational knowledge distillation of machine learning interatomic potentials for scalable catalyst exploration	34
Advancing first-principles dielectric property prediction of complex microwave materials: an elemental-unit decomposition approach	34
Giant multiphononic effects in a perovskite oxide	33
Designing architected materials for mechanical compression via simulation, deep learning, and experimentation	33
Achieving optimal GaN/SiC interfacial thermal conductance via ultrathin alloy interlayers for high-power device cooling	33
Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning	33
Bayesian Optimization of Grain-Boundary Segregation in High-Entropy Alloys	32
Morphology prediction of small nanoparticles in any orientation from single electron micrographs	32
Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys	32
Enabling single-observation decomposition of multi-phase X-ray diffraction patterns via generative deep learning	32
Trajectory sampling and finite-size effects in first-principles stopping power calculations	32
The properties, thermodynamics and application prospects of diamanes	32
The impact of ionic anharmonicity on superconductivity in metal-stuffed B-C clathrates	32
Annealing-aware screening of Co–Ir–Ni–Rh–Ru alloys for ammonia decomposition	32
Large language models design sequence-defined macromolecules via evolutionary optimization	32
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling	32
Modeling the effects of salt concentration on aqueous and organic electrolytes	31
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials	31
Chemical bonding dictates alloying effect on inherent mechanical strength and plastic deformation mechanism in CoNiCr multicomponent alloy	31
A multi-fidelity machine learning approach to high throughput materials screening	31
Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction	31
Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone	31
Intrinsic hard magnetism and thermal stability of a ThMn12-type permanent magnet	31
Non-adiabatic approximations in time-dependent density functional theory: progress and prospects	31
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks	30
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors	30
Higher-order equivariant neural networks for charge density prediction in materials	30
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting	30
Author Correction: Polarization switching of HfO2 ferroelectric in bulk and electrode/ferroelectric/electrode heterostructure	30
Investigating contact-limited scaling in sub-15-nm TMD FETs from first-principles	30
The Bell-Evans-Polanyi relation for hydrogen evolution reaction from first-principles	30
AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures	30
Machine-learning structural reconstructions for accelerated point defect calculations	30
Chemical foundation model-guided design of high ionic conductivity electrolyte formulations	30
An autonomous robotic module for efficient surface tension measurements of formulations	30
A general optimization framework for mapping local transition-state networks	30
Technical review: Time-dependent density functional theory for attosecond physics ranging from gas-phase to solids	30
Towards understanding structure–property relations in materials with interpretable deep learning	30
Simple arithmetic operation in latent space can generate a novel three-dimensional graph metamaterials	30
Efficient equivariant model for machine learning interatomic potentials	30
Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings	30
Understanding and tuning negative longitudinal piezoelectricity in hafnia	30
The best thermoelectrics revisited in the quantum limit	29
Point-defect-driven flattened polar phonon bands in fluorite ferroelectrics	29
Electron-phonon physics at the exascale: a hybrid MPI-GPU-OpenMP framework for scalable Wannier interpolation	29
Electronic correlation in nearly free electron metals with beyond-DFT methods	29