The Journal of Computational and Data-Driven Materials Engineering (JCDME), ISSN: 3149-9368, published by Institute for Advanced Materials Research Press, is an international, peer-reviewed, open-access journal dedicated to computational, data-driven, and digital approaches in materials engineering and materials science.
The journal provides a multidisciplinary platform for materials scientists, engineers, computational researchers, physicists, chemists, data scientists, manufacturing specialists, and industry professionals to publish original and evidence-based research concerning the modeling, simulation, design, optimization, characterization, processing, and performance evaluation of engineering materials.
JCDME publishes Original Research Articles, Review Articles, Systematic Reviews, and Materials Engineering Case Studies,
The journal’s scope includes:
Computational Materials Engineering, including computational approaches to materials selection, engineering design, processing, manufacturing, structural performance, service behavior, and process–structure–property–performance relationships
Materials Informatics, including materials databases, data infrastructure, metadata, knowledge representation, materials ontologies, knowledge graphs, data integration, data provenance, and informatics-supported research workflows
Data-Driven Materials Design, including inverse design, data-guided candidate selection, generative methods, surrogate modeling, Bayesian optimization, active learning, and the development of scientifically defensible materials-design rules
Computational Materials Science, including computational investigation of material structures, thermodynamics, kinetics, phase stability, defects, interfaces, properties, processes, performance, and behavior
Materials Modeling and Simulation, including first-principles calculations, molecular dynamics, Monte Carlo methods, thermodynamic modeling, phase-field modeling, finite-element analysis, computational mechanics, physics-based models, and hybrid computational methods
Multiscale Materials Modeling, including methods that connect electronic, atomistic, molecular, microstructural, mesoscale, continuum, component, and engineering-system scales
Materials Data Analytics, including data collection, preprocessing, feature engineering, dimensionality reduction, statistical analysis, uncertainty quantification, visualization, data fusion, pattern recognition, and interpretable materials analytics
Predictive Modeling of Material Properties, including prediction of mechanical, thermal, electrical, optical, magnetic, chemical, electrochemical, functional, durability, degradation, and service-performance properties
High-Throughput Materials Screening, including automated candidate evaluation, virtual screening, combinatorial computation, high-throughput experimentation, workflow automation, active learning, and accelerated materials selection
Digital Materials Engineering, including digital representations of materials, integrated computational workflows, digital manufacturing, process monitoring, materials-data infrastructure, digital thread development, and intelligent engineering systems
Integrated Computational Materials Engineering (ICME), including integrated models linking processing, structure, properties, performance, manufacturing, component design, and lifecycle considerations
Materials Optimization, including composition optimization, microstructure optimization, process optimization, topology and structural optimization, multi-objective optimization, robust design, and optimization under uncertainty
Materials Characterization and Data Analysis, including computational and data-driven analysis of microscopy, spectroscopy, diffraction, tomography, thermal analysis, mechanical testing, imaging, sensor data, microstructures, defects, and multimodal characterization datasets
Digital Twin for Materials Systems, including digital twins of materials, manufacturing processes, components, and materials systems; real-time model updating; sensor-data integration; model calibration; condition monitoring; performance forecasting; and lifecycle management
Sustainable Materials Design, including computational and data-driven methods for resource-efficient materials development, low-impact processing, lifecycle assessment, circular materials systems, recyclability, durability, material recovery, waste reduction, and environmentally responsible engineering design
The journal welcomes theoretical, computational, methodological, experimental–computational, and industrial studies that advance the use of modeling, simulation, data analytics, and digital engineering in materials research.
Relevant contributions may include the development, validation, comparison, integration, or application of computational methods; materials databases; data-driven models; multiscale frameworks; optimization algorithms; digital twins; automated workflows; and integrated engineering systems.
Studies combining computational or data-driven methods with experimental synthesis, characterization, manufacturing, or performance testing are particularly encouraged. Experimental work should provide meaningful validation, calibration, interpretation, or evaluation of the proposed computational or data-driven approach.
The journal also welcomes research addressing:
Model verification, validation, and calibration
Reproducibility and computational transparency
Data quality, provenance, and interoperability
Uncertainty and sensitivity analysis
Numerical accuracy and convergence
Transferability and generalizability of models
Benchmark datasets and reference workflows
Explainability and physical interpretability
Computational efficiency and scalability
Integration of simulations with experiments
Industrial implementation and technology transfer
Materials lifecycle and sustainability assessment
Research involving materials datasets, computational models, simulations, or digital workflows should clearly describe, where relevant:
Data sources and provenance
Data collection and preprocessing procedures
Model assumptions and governing equations
Boundary and initial conditions
Numerical methods and computational parameters
Training, validation, and test-data separation
Measures used to prevent data leakage
Verification and validation procedures
Evaluation metrics and baseline comparisons
Uncertainty, sensitivity, and error analysis
Software, code, model, and data availability
Computational limitations and conditions of applicability
Physical and engineering interpretation of the results
JCDME welcomes interdisciplinary studies connecting computational and data-driven materials engineering with physics, chemistry, chemical engineering, mechanical engineering, manufacturing engineering, industrial engineering, applied mathematics, computer science, data science, statistics, energy systems, electronics, nanotechnology, automation, robotics, and environmental engineering.
Submissions must demonstrate a clear and substantial connection between computational methods, materials data, modeling, simulation, optimization, digital engineering, or integrated computational approaches and a meaningful materials-science or materials-engineering problem.
Manuscripts focused solely on general computer science, standalone algorithm development, generic data analysis, or non-materials datasets without a relevant materials application or scientific evaluation are outside the journal’s scope.
Routine simulations using established software without methodological advancement, adequate validation, new physical insight, or a substantial engineering contribution may also be considered outside scope. Purely experimental materials studies that do not include a meaningful computational, data-driven, modeling, simulation, optimization, or digital-engineering component are not normally considered for publication.
Institute for Advanced Materials Research Press