| Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries |
166 |
| Recent advances and applications of machine learning in solid-state materials science |
133 |
| Plasmon-enhanced light-matter interactions and applications |
64 |
| A strategy to apply machine learning to small datasets in materials science |
63 |
| A review of oxygen reduction mechanisms for metal-free carbon-based electrocatalysts |
54 |
| Precision and efficiency in solid-state pseudopotential calculations |
52 |
| Machine learning modeling of superconducting critical temperature |
46 |
| Solving the electronic structure problem with machine learning |
45 |
| New frontiers for the materials genome initiative |
43 |
| Interplay between Kitaev interaction and single ion anisotropy in ferromagnetic CrI3 and CrGeTe3 monolayers |
42 |
| Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design |
41 |
| Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study |
38 |
| Ultra-low thermal conductivity of two-dimensional phononic crystals in the incoherent regime |
32 |
| Statistical variances of diffusional properties from ab initio molecular dynamics simulations |
32 |
| Active learning for accelerated design of layered materials |
29 |
| Efficient first-principles prediction of solid stability: Towards chemical accuracy |
29 |
| Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm |
28 |
| Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials |
26 |
| Machine learning hydrogen adsorption on nanoclusters through structural descriptors |
26 |
| De novo exploration and self-guided learning of potential-energy surfaces |
25 |
| Physics and applications of charged domain walls |
23 |
| An effective method to screen sodium-based layered materials for sodium ion batteries |
22 |
| Identifying Pb-free perovskites for solar cells by machine learning |
22 |
| Genetic algorithms for computational materials discovery accelerated by machine learning |
21 |
| Predicting accurate cathode properties of layered oxide materials using the SCAN meta-GGA density functional |
21 |
| Transparent conducting materials discovery using high-throughput computing |
21 |
| Bismuth and antimony-based oxyhalides and chalcohalides as potential optoelectronic materials |
21 |
| Nanotwinned and hierarchical nanotwinned metals: a review of experimental, computational and theoretical efforts |
20 |
| Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods |
20 |
| Insights into the design of thermoelectric Mg3Sb2 and its analogs by combining theory and experiment |
18 |
| Automated defect analysis in electron microscopic images |
18 |
| First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy |
18 |
| Bayesian inference of atomistic structure in functional materials |
18 |
| Orbitally driven giant thermal conductance associated with abnormal strain dependence in hydrogenated graphene-like borophene |
17 |
| Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks |
17 |
| Spatial correlation of elastic heterogeneity tunes the deformation behavior of metallic glasses |
17 |
| Prediction of Weyl semimetal and antiferromagnetic topological insulator phases in Bi2MnSe4 |
17 |
| Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials |
17 |
| A machine learning approach to model solute grain boundary segregation |
16 |
| Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning |
16 |
| Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2 |
16 |
| A new carbon phase with direct bandgap and high carrier mobility as electron transport material for perovskite solar cells |
16 |
| Analyzing machine learning models to accelerate generation of fundamental materials insights |
16 |
| First principles calculation of spin-related quantities for point defect qubit research |
15 |
| Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride |
15 |
| Designing interfaces in energy materials applications with first-principles calculations |
15 |
| Machine-learned multi-system surrogate models for materials prediction |
14 |
| Ductile deformation mechanism in semiconductor alpha-Ag2S |
13 |
| Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization |
13 |
| A property-oriented design strategy for high performance copper alloys via machine learning |
13 |
