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M. Kolev

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Review Open access Jul 2026

How to Write a Bibliometric-Assisted Critical Review: From Science Mapping to Critical Synthesis

Bibliometric analysis is widely used to map scientific fields, identify influential publications, and visualize thematic structures, but maps, rankings, and network clusters cannot by themselves produce a scientifically critical review. This article proposes the Bibliometric-Assisted Critical Review (BACR) framework as a writing-oriented conceptual and pedagogical model for authors, doctoral researchers, supervisors, and reviewers working with large and heterogeneous literatures. BACR is presented as a methodological framework and authoring heuristic, not as an empirical study, a new reporting standard, or a substitute for systematic reviews, scoping reviews, meta-analyses, or conventional bibliometric reviews. The framework distinguishes a broad bibliometric corpus from a narrower critical reading corpus and explains how bibliometric signals can be translated into reading decisions, appraisal questions, synthesis moves, and transparent reporting practices. Its practical outputs include a nine-phase writing process, a bibliometric-to-critical translation matrix, a critical appraisal template, a worked mini-example, a recommended manuscript architecture, a preliminary checklist, and a table of common errors. BACR is intended to help authors use science mapping to strengthen, rather than replace, close reading and critical synthesis. The framework is still preliminary and needs to be adapted to specific fields, tested through pilot applications, and validated.

M. Kolev · 0 citations
Open access Jul 2026

Machine Learning in Materials Science: Data-Driven Discovery and Functional Applications

Machine learning (ML) in materials science refers to computational methods that learn statistical, structural, or physics-informed relationships from experimental, computational, and literature-derived materials data. These methods are used to predict materials properties, identify structure–property and process–structure–property–performance relationships, discover candidate materials, optimize synthesis and processing routes, and guide functional applications. ML is narrower than artificial intelligence (AI), which also includes broader reasoning, planning, search, and automation capabilities. It is also distinct from materials informatics, which is the wider data-centered framework that includes databases, descriptors, metadata, workflows, visualization, and knowledge management. ML can complement high-throughput computation by building surrogate models from density functional theory, finite-element simulation, molecular dynamics, or experimental data, but it is not identical to high-throughput screening itself. Unlike conventional physics-based modeling, which begins with explicit governing equations or mechanistic assumptions, ML usually infers predictive relationships from data; modern approaches increasingly combine both perspectives through physics-informed features, uncertainty quantification, and human expertise.

M. Kolev · 0 citations