OOIR: Observatory of International Research

Papers

(The H4-Index of npj Computational Materials is 51. 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 2020-11-01 to 2024-11-01.)

Article	Citations
Recent advances and applications of deep learning methods in materials science	359
Atomistic Line Graph Neural Network for improved materials property predictions	245
Machine learning for perovskite materials design and discovery	231
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design	224
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy	172
Small data machine learning in materials science	145
Benchmarking graph neural networks for materials chemistry	144
Accelerating materials discovery using artificial intelligence, high performance computing and robotics	119
Machine learning in concrete science: applications, challenges, and best practices	119
Mechanism of keyhole pore formation in metal additive manufacturing	118
Phase-field modeling of grain evolutions in additive manufacturing from nucleation, growth, to coarsening	117
Two-step machine learning enables optimized nanoparticle synthesis	105
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods	102
Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon	101
Two-dimensional Stiefel-Whitney insulators in liganded Xenes	99
Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization	96
Explainable machine learning in materials science	94
Understanding and design of metallic alloys guided by phase-field simulations	92
Deep learning framework for material design space exploration using active transfer learning and data augmentation	92
MatSciBERT: A materials domain language model for text mining and information extraction	86
Compositionally restricted attention-based network for materials property predictions	86
Machine-learned interatomic potentials for alloys and alloy phase diagrams	86
A review of the recent progress in battery informatics	84
Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture	81
Band degeneracy enhanced thermoelectric performance in layered oxyselenides by first-principles calculations	81

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials	79
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains	77
Sign-reversible valley-dependent Berry phase effects in 2D valley-half-semiconductors	75
Predominance of non-adiabatic effects in zero-point renormalization of the electronic band gap	75
Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials	74
Computational screening study of double transition metal carbonitrides M′2M″CNO2-MXene as catalysts for hydrogen evolution reaction	72
Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms	72
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning	71
Restructured single parabolic band model for quick analysis in thermoelectricity	71
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration	70
Application of phase-field method in rechargeable batteries	68
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures	68
Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe	65
Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials	65
Machine learning property prediction for organic photovoltaic devices	63
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing	62
Computational high-throughput screening of alloy nanoclusters for electrocatalytic hydrogen evolution	61
High performance Wannier interpolation of Berry curvature and related quantities with WannierBerri code	61
Nanodevices engineering and spin transport properties of MnBi2Te4 monolayer	60
Learning two-phase microstructure evolution using neural operators and autoencoder architectures	60
Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts	56
Electrically and magnetically switchable nonlinear photocurrent in РТ-symmetric magnetic topological quantum materials	56
Design high-entropy carbide ceramics from machine learning	55
The origin of the lattice thermal conductivity enhancement at the ferroelectric phase transition in GeTe	53
Damage mechanism identification in composites via machine learning and acoustic emission	53
Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses	52
Predicting stable crystalline compounds using chemical similarity	51
Ab initio molecular dynamics and materials design for embedded phase-change memory	51
Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet	51
2D spontaneous valley polarization from inversion symmetric single-layer lattices	51