Original Research
Published on 29 Aug 2025
Detection of wood grain defects based on edge prior aggregation
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1635222
- 268 views
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193 articles
Original Research
Accepted on 25 Aug 2025
Development of Defect Localization Method for Perforated Carbon-fiber-reinforced Plastic Specimens Using Finite Element Method and Graph Neural Network
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1652484
- 234 views
Mini Review
Accepted on 15 Aug 2025
Modeling and Design of Micro-structures: Focusing on Functionally Graded Materials and Future Prospects
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1659727
- 300 views
Review
Published on 08 Aug 2025
Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimization approaches
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1645227
- 2,001 views
Original Research
Published on 30 Jul 2025
TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1591955
- 1,332 views
Original Research
Published on 18 Jul 2025
Enhancing phase change thermal energy storage material properties prediction with digital technologies
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1616233
- 1,482 views
Original Research
Published on 02 Jul 2025
Rapid mold optimization based on ultraviolet curing 3D printing technology
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1605771
- 1,426 views
Original Research
Published on 06 Jun 2025
Digitized material design and performance prediction driven by high-throughput computing
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1599439
- 1,673 views
Original Research
Published on 14 Apr 2025
Classification, identification, and quantitative study of defects in aluminum plates using pulsed alternating current field measurement combined with principal component analysis
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1569055
- 1,218 views
Original Research
Published on 08 Apr 2025
Deep learning-driven medical image analysis for computational material science applications
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1583615
- 2,209 views
- 1 citation
Original Research
Published on 21 Jan 2025
Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2025.1542655
- 1,956 views
- 3 citations
Original Research
Published on 11 Dec 2024
Influence of microstructure on the wettability of tobacco leaves: a theoretical model and quantitative analysis
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2024.1485713
- 1,591 views
- 1 citation
Original Research
Published on 05 Dec 2024
Comparative flank wear and produced chips types analysis of CNMG 120408 indexable cutting inserts after machining hardened 1.6582 steel
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2024.1432039
- 1,036 views
Original Research
Published on 14 Nov 2024
Prediction and analysis of strength and economic feasibility of filling materials under the influence of mix proportion and curing age
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2024.1428859
- 859 views
Original Research
Published on 23 Oct 2024
Glass transition temperature of asphalt binder based on atomistic scale simulation
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2024.1485669
- 1,333 views
- 2 citations
Original Research
Published on 22 Oct 2024
Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm
in Computational Materials Science
Frontiers in Materials
doi 10.3389/fmats.2024.1481871
- 2,939 views
- 2 citations