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 2022-05-01 to 2026-05-01.)

Article	Citations
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene	642
Networking autonomous material exploration systems through transfer learning	325
Dynamical phase-field model of cavity electromagnonic systems	286
Strain and ligand effects in the 1-D limit: reactivity of steps	261
Sparse representation for machine learning the properties of defects in 2D materials	199
Active learning of effective Hamiltonian for super-large-scale atomic structures	186
Multiscale kinetic model of ethylene oligomerization in Ni-NU-1000 metal-organic framework	164
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings	162
FALCON: fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations	128
Structure and properties of graphullerene: a semiconducting two-dimensional C60 crystal	120
Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials	119
Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network	119
Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides	118
Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app	113
Probing multi-dimensional composition spaces in search of strong metallic alloys	113
cmtj: Simulation package for analysis of multilayer spintronic devices	109
SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction	108
AI-assisted rapid crystal structure generation towards a target local environment	107
Crosslinking degree variations enable programming and controlling soft fracture via sideways cracking	100
Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys	100
Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning	99
Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material	97
Ultra-fast interpretable machine-learning potentials	96
Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls	94
Dynamical mean field theory for real materials on a quantum computer	93

JARVIS-Leaderboard: a large scale benchmark of materials design methods	91
Identifying the ground state structures of point defects in solids	91
First principles methodology for studying magnetotransport in narrow gap semiconductors with ZrTe5 example	90
Vibrationally resolved optical excitations of the nitrogen-vacancy center in diamond	89
Machine learning-aided first-principles calculations of redox potentials	88
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints	87
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials	87
Author Correction: Active learning for accelerated design of layered materials	87
Quantum anomalous hall effect in collinear antiferromagnetism	87
A critical examination of robustness and generalizability of machine learning prediction of materials properties	86
Machine learning enhanced analysis of EBSD data for texture representation	86
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics	84
Ultrafast laser-driven topological spin textures on a 2D magnet	84
MatSciBERT: A materials domain language model for text mining and information extraction	84
Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching	80
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules	80
Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds	78
Combined study of phase transitions in the P2-type NaXNi1/3Mn2/3O2 cathode material: experimental, ab-initio and multiphase-field results	76
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows	75
Accelerating electron diffraction analysis using graph neural networks and attention mechanisms	74
Revealing the evolution of order in materials microstructures using multi-modal computer vision	73
Electro-chemo-mechanical modelling of structural battery composite full cells	72
Tunable sliding ferroelectricity and magnetoelectric coupling in two-dimensional multiferroic MnSe materials	72
High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling	72
High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy	70
Raman signatures of single point defects in hexagonal boron nitride quantum emitters	70
Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics	70
High-throughput discovery of fluoride-ion conductors via a decoupled, dynamic, and iterative (DDI) framework	70
Graph atomic cluster expansion for foundational machine learning interatomic potentials	70
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability	68
A machine learning approach to designing and understanding tough, degradable polyamides	68
Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data	67
Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis	66
From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models	66
Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene	66
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty	66
Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration	66
First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions	66
Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties	64
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning	64