OOIR: Observatory of International Research

Papers

(The H4-Index of npj Computational Materials is 69. 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 2022-08-01 to 2026-08-01.)

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