OOIR: Observatory of International Research

Papers

(The H4-Index of npj Computational Materials is 64. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-06-01 to 2026-06-01.)

Article	Citations
Dynamical phase-field model of cavity electromagnonic systems	671
Strain and ligand effects in the 1-D limit: reactivity of steps	347
Sparse representation for machine learning the properties of defects in 2D materials	293
Multiscale kinetic model of ethylene oligomerization in Ni-NU-1000 metal-organic framework	208
Structure and properties of graphullerene: a semiconducting two-dimensional C60 crystal	191
Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials	170
Probing multi-dimensional composition spaces in search of strong metallic alloys	128
Machine learning-aided first-principles calculations of redox potentials	121
First principles methodology for studying magnetotransport in narrow gap semiconductors with ZrTe5 example	121
Author Correction: Active learning for accelerated design of layered materials	121
Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls	120
Dynamical mean field theory for real materials on a quantum computer	119
Vibrationally resolved optical excitations of the nitrogen-vacancy center in diamond	118
cmtj: Simulation package for analysis of multilayer spintronic devices	114
Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys	113
SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction	113
Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning	104
Crosslinking degree variations enable programming and controlling soft fracture via sideways cracking	104
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings	103
Networking autonomous material exploration systems through transfer learning	101
Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network	99
A critical examination of robustness and generalizability of machine learning prediction of materials properties	98
Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app	95
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene	94
Robust electron counting for direct electron detectors with the Back-propagation counting method	94

JARVIS-Leaderboard: a large scale benchmark of materials design methods	92
Unlocking 3D nanoparticle shapes from 2D high-resolution transmission electron microscopy images: a deep learning approach	92
AI-assisted rapid crystal structure generation towards a target local environment	92
Active learning of effective Hamiltonian for super-large-scale atomic structures	91
Ultra-fast interpretable machine-learning potentials	90
Machine learning enhanced analysis of EBSD data for texture representation	90
FALCON: fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations	89
Identifying the ground state structures of point defects in solids	89
Quantum anomalous hall effect in collinear antiferromagnetism	88
Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material	88
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints	87
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics	85
Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides	84
Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching	81
Accelerating electron diffraction analysis using graph neural networks and attention mechanisms	79
Revealing the evolution of order in materials microstructures using multi-modal computer vision	77
Raman signatures of single point defects in hexagonal boron nitride quantum emitters	76
High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling	75
Agent-based multimodal information extraction for nanomaterials	75
Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene	75
From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models	74
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning	74
High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy	74
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules	73
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows	72
Tunable sliding ferroelectricity and magnetoelectric coupling in two-dimensional multiferroic MnSe materials	72
A process-synergistic active learning framework for high-strength Al-Si alloys design	71
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty	71
First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions	70
DiffCrysGen: a generative diffusion model for accelerated design of inorganic crystalline materials	70
Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration	70
Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics	69
A machine learning approach to designing and understanding tough, degradable polyamides	67
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability	67
Electro-chemo-mechanical modelling of structural battery composite full cells	66
Combined study of phase transitions in the P2-type NaXNi1/3Mn2/3O2 cathode material: experimental, ab-initio and multiphase-field results	66
Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds	65
Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis	65
Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties	64
Graph atomic cluster expansion for foundational machine learning interatomic potentials	64
Ultrafast laser-driven topological spin textures on a 2D magnet	64
Comment on “Machine learning enhanced analysis of EBSD data for texture representation”	64