OOIR: Observatory of International Research

Papers

(The H4-Index of npj Computational Materials is 57. 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 2020-04-01 to 2024-04-01.)

Article	Citations
Recent advances and applications of deep learning methods in materials science	217
Machine learning for perovskite materials design and discovery	192
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design	185
Atomistic Line Graph Neural Network for improved materials property predictions	171
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy	162
Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning	158
Inverse-designed spinodoid metamaterials	152
Discovery of high-entropy ceramics via machine learning	135
Completing density functional theory by machine learning hidden messages from molecules	122
Ab initio theory of the negatively charged boron vacancy qubit in hexagonal boron nitride	120
A critical examination of compound stability predictions from machine-learned formation energies	119
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials	115
Benchmarking graph neural networks for materials chemistry	114
Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy	113
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide	104
Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules	101
Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm	99
Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening	96
Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships	96
Quantum simulations of materials on near-term quantum computers	96
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images	93
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network	92
The electrode tortuosity factor: why the conventional tortuosity factor is not well suited for quantifying transport in porous Li-ion battery electrodes and what to use instead	90
Mechanism of keyhole pore formation in metal additive manufacturing	87
Two-step machine learning enables optimized nanoparticle synthesis	87

Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries	87
Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization	83
High-throughput discovery of high Curie point two-dimensional ferromagnetic materials	82
Biquadratic exchange interactions in two-dimensional magnets	82
Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys	81
Machine learning in concrete science: applications, challenges, and best practices	79
Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells	78
Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon	78
Frequency-dependent dielectric constant prediction of polymers using machine learning	77
Machine-learned interatomic potentials for alloys and alloy phase diagrams	76
Concepts of the half-valley-metal and quantum anomalous valley Hall effect	73
Accelerating materials discovery using artificial intelligence, high performance computing and robotics	73
Two-dimensional Stiefel-Whitney insulators in liganded Xenes	72
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods	71
Deep learning framework for material design space exploration using active transfer learning and data augmentation	71
Predicting aqueous stability of solid with computed Pourbaix diagram using SCAN functional	70
Symmetry-enforced Weyl phonons	69
Compositionally restricted attention-based network for materials property predictions	68
Predominance of non-adiabatic effects in zero-point renormalization of the electronic band gap	65
Band degeneracy enhanced thermoelectric performance in layered oxyselenides by first-principles calculations	65
Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture	65
In silico modelling of cancer nanomedicine, across scales and transport barriers	64
Computational screening study of double transition metal carbonitrides M′2M″CNO2-MXene as catalysts for hydrogen evolution reaction	63
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains	62
High-throughput computational screening for two-dimensional magnetic materials based on experimental databases of three-dimensional compounds	62
Small data machine learning in materials science	60
Explainable machine learning in materials science	60
Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys	58
High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses	58
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials	58
Understanding and design of metallic alloys guided by phase-field simulations	57
Machine learning property prediction for organic photovoltaic devices	57
Giant anomalous Hall and Nernst effect in magnetic cubic Heusler compounds	57
Sign-reversible valley-dependent Berry phase effects in 2D valley-half-semiconductors	57