Transparent Intelligence Publishing is an international academic publisher dedicated to advancing scholarly research in materials science, materials engineering, computational modeling, artificial intelligence, and data-driven materials discovery.
We provide a professional publishing platform through which researchers, engineers, data scientists, educators, and industry specialists can communicate original findings, exchange academic knowledge, and contribute to the development of advanced, intelligent, functional, and sustainable materials.
Through its portfolio of peer-reviewed journals, the Press supports interdisciplinary research connecting fundamental materials science with computational methods, digital engineering, advanced characterization, artificial intelligence, and emerging manufacturing technologies. Our publishing activities are intended to help relevant research reach an international audience and encourage constructive collaboration across disciplines, institutions, industries, and geographic regions.
The mission of Transparent Intelligence Publishing is to support the responsible creation, evaluation, publication, and dissemination of high-quality scholarly research in materials science and engineering.
We seek to:
Provide reliable platforms for publishing original and meaningful academic research.
Support innovation in materials science, computational engineering, artificial intelligence, materials informatics, and digital manufacturing.
Encourage collaboration among materials scientists, engineers, data scientists, computational researchers, experimental specialists, and industry professionals.
Promote transparent, ethical, rigorous, and editorially independent scholarly publishing.
Improve the international visibility and accessibility of published research.
Strengthen connections among scientific research, engineering practice, industrial development, sustainability, and technological innovation.
Support reproducible research, responsible data use, and the clear communication of scientific methods and findings.
The journals published by the Press cover interconnected areas of materials science and engineering, with particular emphasis on computational, data-driven, intelligent, advanced, and sustainable materials research.
We support research on the development and responsible application of artificial intelligence and machine-learning methods throughout the materials research lifecycle, from hypothesis generation and candidate screening to synthesis planning and experimental validation.
Relevant subjects include:
AI-Assisted Materials Synthesis
Artificial Intelligence in Materials Science
Machine Learning for Materials Discovery
Research in these areas may address autonomous experimentation, generative materials design, active learning, physics-informed machine learning, synthesis prediction, optimization of experimental conditions, model interpretability, uncertainty, reliability, and human-AI collaboration in materials research.
The Press welcomes research concerning the collection, organization, analysis, interpretation, and responsible use of materials data. This area connects materials science with data science, statistical analysis, information systems, and computational knowledge discovery.
Relevant subjects include:
Data-Driven Materials Design
High-Throughput Materials Screening
Materials Characterization and Data Analysis
Materials Data Analytics
Materials Informatics
Predictive Modeling of Material Properties
Research may examine materials databases, data quality, feature engineering, structure-property relationships, property prediction, knowledge extraction, representation learning, high-dimensional materials data, data integration, uncertainty quantification, benchmark development, and reproducible data-driven workflows.
These fields contribute to more efficient materials discovery by helping researchers identify patterns, compare candidates, predict performance, and derive scientifically defensible design rules from experimental and computational data.
We publish research involving computational approaches for understanding, designing, simulating, and optimizing materials and materials-based systems across multiple scales.
Relevant subjects include:
Computational Materials Engineering
Computational Materials Science
Digital Materials Engineering
Digital Twin for Materials Systems
Integrated Computational Materials Engineering (ICME)
Materials Modeling and Simulation
Materials Optimization
Multiscale Materials Modeling
Research in this area may involve atomistic simulation, molecular dynamics, density functional theory, phase-field modeling, finite-element analysis, thermodynamic and kinetic modeling, process-structure-property relationships, computational mechanics, and integrated engineering workflows.
The scope also includes digital representations of materials, manufacturing processes, and material-enabled systems. Digital twins and integrated computational frameworks may be used to monitor performance, evaluate uncertainty, support lifecycle decisions, and connect materials design with manufacturing and engineering applications.
The Press supports experimental and computational research focused on identifying, measuring, and interpreting the structural, chemical, physical, mechanical, thermal, electrical, optical, and functional properties of materials.
This area includes:
Materials Characterization and Analysis
Relevant research may involve microscopy, spectroscopy, diffraction, surface analysis, mechanical testing, thermal analysis, imaging, in situ and operando measurements, nondestructive evaluation, and computational interpretation of characterization data.
We also welcome studies that combine characterization techniques with statistical analysis, artificial intelligence, simulation, or multiscale modeling to improve the understanding of processing-structure-property-performance relationships.
We publish research on materials engineered to provide specific structural, physical, chemical, electronic, optical, thermal, biological, or responsive functions.
Relevant subjects include:
Advanced Functional Materials
Nanomaterials
Smart Materials
This area may include functional ceramics, advanced alloys, polymers, composites, electronic and photonic materials, energy materials, magnetic materials, metamaterials, nanostructured materials, coatings, membranes, porous materials, and stimuli-responsive systems.
Research may address materials synthesis, processing, characterization, modeling, performance evaluation, device integration, reliability, scalability, and practical engineering applications.
The Press recognizes the need to consider environmental performance, resource efficiency, circularity, durability, and lifecycle impacts throughout the design and development of materials.
Relevant subjects include:
Sustainable Materials Design
Sustainable Materials Development
Research in this area may examine low-impact materials, renewable feedstocks, recyclable and reusable materials, resource-efficient processing, waste-derived materials, materials for clean energy, durability and service-life extension, lifecycle assessment, circular materials systems, and environmentally responsible manufacturing.
We welcome studies that combine sustainability objectives with computational design, artificial intelligence, optimization, advanced characterization, and experimental validation.
Contemporary materials challenges frequently extend beyond the boundaries of a single academic discipline. For this reason, we particularly value interdisciplinary research that brings together expertise from fields such as:
Materials science and engineering
Physics and applied physics
Chemistry and chemical engineering
Mechanical and manufacturing engineering
Computational science and engineering
Artificial intelligence and computer science
Data science, statistics, and scientific computing
Nanoscience and nanotechnology
Electrical and electronic engineering
Energy and environmental engineering
Industrial engineering and systems engineering
Sustainable design and lifecycle engineering
Robotics, automation, and autonomous experimentation
Digital manufacturing and additive manufacturing
By encouraging communication across these disciplines, we aim to support research that is scientifically rigorous, computationally sound, experimentally grounded, and relevant to practical materials and engineering challenges.
The Press is committed to maintaining responsible editorial and publishing practices. Manuscripts are evaluated according to their scientific relevance, methodological clarity, originality, ethical compliance, and contribution to their fields.
Independent editorial assessment and peer review
Clear disclosure of methods, data sources, limitations, and conflicts of interest
Responsible use of artificial intelligence and computational tools
Respect for research integrity, authorship standards, and publication ethics
Constructive communication among authors, editors, and reviewers
Support for transparent and reproducible scholarly research
International accessibility and long-term discoverability of published work
Through these principles, Transparent Intelligence Publishing seeks to provide a dependable environment for publishing and exchanging research across the expanding fields of computational, data-driven, intelligent, advanced, and sustainable materials science.
Transparent Intelligence Publishing