OOIR: Observatory of International Research

Papers

(The H4-Index of npj Computational Materials is 61. 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 2022-01-01 to 2026-01-01.)

Article	Citations
Author Correction: Active learning for accelerated design of layered materials	820
Structure and properties of graphullerene: a semiconducting two-dimensional C60 crystal	513
cmtj: Simulation package for analysis of multilayer spintronic devices	275
Dynamical mean field theory for real materials on a quantum computer	258
Machine learning-aided first-principles calculations of redox potentials	250
Strain and ligand effects in the 1-D limit: reactivity of steps	237
First principles methodology for studying magnetotransport in narrow gap semiconductors with ZrTe5 example	209
Vibrationally resolved optical excitations of the nitrogen-vacancy center in diamond	165
Dynamical phase-field model of cavity electromagnonic systems	152
Crosslinking degree variations enable programming and controlling soft fracture via sideways cracking	151
Multiscale modeling of ultrafast melting phenomena	146
Networking autonomous material exploration systems through transfer learning	144
Sparse representation for machine learning the properties of defects in 2D materials	129
Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys	117
Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning	114
Electron-mediated anharmonicity and its role in the Raman spectrum of graphene	113
Active learning of effective Hamiltonian for super-large-scale atomic structures	108
FALCON: fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations	108
Accurate piezoelectric tensor prediction with equivariant attention tensor graph neural network	101
SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction	96
JARVIS-Leaderboard: a large scale benchmark of materials design methods	94
Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials	93
A critical examination of robustness and generalizability of machine learning prediction of materials properties	92
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials	91
Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides	86

Machine learning enhanced analysis of EBSD data for texture representation	84
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints	84
Prediction of intrinsic multiferroicity and large valley polarization in a layered Janus material	82
RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics	82
MatSciBERT: A materials domain language model for text mining and information extraction	80
Quantum anomalous hall effect in collinear antiferromagnetism	80
Identifying the ground state structures of point defects in solids	80
Ultra-fast interpretable machine-learning potentials	80
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings	79
Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis	78
Emergence of local scaling relations in adsorption energies on high-entropy alloys	78
High-throughput discovery of fluoride-ion conductors via a decoupled, dynamic, and iterative (DDI) framework	77
Tunable sliding ferroelectricity and magnetoelectric coupling in two-dimensional multiferroic MnSe materials	77
Phase-field framework with constraints and its applications to ductile fracture in polycrystals and fatigue	76
Raman signatures of single point defects in hexagonal boron nitride quantum emitters	76
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning	75
A machine learning approach to designing and understanding tough, degradable polyamides	74
Conversion of twisted light to twisted excitons using carbon nanotubes	74
Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics	72
Revealing the evolution of order in materials microstructures using multi-modal computer vision	71
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules	71
Electro-chemo-mechanical modelling of structural battery composite full cells	67
Imaging atomic-scale chemistry from fused multi-modal electron microscopy	67
From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models	67
Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching	66
Persistent half-metallic ferromagnetism in a (111)-oriented manganite superlattice	66
High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy	65
Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene	65
Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data	64
A process-synergistic active learning framework for high-strength Al-Si alloys design	63
Agent-based multimodal information extraction for nanomaterials	63
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows	63
Ultrafast laser-driven topological spin textures on a 2D magnet	63
First-principles search of hot superconductivity in La-X-H ternary hydrides	62
First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions	62
A graph based approach to model charge transport in semiconducting polymers	61
Combined study of phase transitions in the P2-type NaXNi1/3Mn2/3O2 cathode material: experimental, ab-initio and multiphase-field results	61
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty	61