Aims & Scope

AI for Materials is a gold open-access, peer-reviewed journal that aims to bridge the dynamic fields of artificial intelligence (AI) and advanced materials research. The journal’s mission is to provide a dedicated, high-impact platform for researchers, engineers, and practitioners to publish pioneering studies that leverage AI-driven methodologies to enhance the discovery, design, development, and application of materials across different scales. AI for Materials recognizes the transformative potential of AI in accelerating innovations in material science and applications, inviting contributions that address both fundamental and applied challenges in diverse areas, including computational modeling, data-driven material design, automation in experimental research, and real-time material property prediction.

The journal seeks to foster interdisciplinary collaboration, welcoming research from fields such as machine learning, data science, physics, chemistry, and engineering to advance the development of both hard and soft materials. Covered materials include nano- to atomic-scale functional hard materials, as well as soft materials like polymers, DNA, and molecular assemblies, each with applications across a wide range of sectors. These sectors encompass environmental decontamination, sensing, catalysis, biomedicine, energy conversion, energy storage, electronics, photonics, optoelectronics, and space exploration. By showcasing research that integrates AI with materials science, AI for Materials aims to push the boundaries of what is possible, driving forward the creation of innovative materials that address real-world needs. Published quarterly online by Scilight Press, the journal serves as a comprehensive resource for the latest advancements at the intersection of AI and material science.

It is published quarterly online by Scilight Press.

AI for Materials covers a wide range of topics at the intersection of artificial intelligence and material science, focusing on both hard materials (such as nano-to-atomic scale materials) and soft materials (like polymers, DNA, and molecular assemblies). The journal emphasizes AI-driven methodologies that advance the synthesis, characterization, design, and application of materials across multiple scales and disciplines. Key research areas include, but are not limited to:

Nano-to-Atomic Hard Materials: AI-guided studies in the synthesis, characterization, and application of hard materials at the nano- and atomic-scales, including quantum dots and atomic-scale structures, with applications in catalysis, energy, electronics, and environmental solutions, where their unique nanoscale properties enhance performance and efficiency.

Quantum Materials: Exploration of AI techniques for the design and manipulation of quantum materials, focusing on applications in quantum information systems, quantum computing, and sensors, where quantum properties like coherence and entanglement are essential.

Soft Materials: AI-driven innovations in the synthesis and application of soft materials, including polymers, DNA, and other molecular structures, for use in flexible electronics, responsive materials, and biomedical devices.

Environmental Decontamination: AI-empowered solutions for creating sustainable materials that address pollution control, water purification, and energy-efficient systems, tackling global environmental challenges and advancing green technology.

Catalysis: Application of AI models in designing and optimizing nanocatalysts and atomic-scale catalysts for chemical reactions in environmental, industrial, and energy processes, where precise control at the atomic level can enhance efficiency and sustainability.

Sensing: AI-driven development of nano- and atomic-scale materials for sensors used in environmental monitoring, healthcare, and security, enhancing sensitivity, selectivity, and performance.

Biomedical and Pharmaceutical Applications: Utilizing AI to develop advanced nanomaterials for drug delivery, medical imaging, diagnostics, and therapeutic applications, targeting precision medicine and improving healthcare outcomes.

Energy Conversion: AI-guided discovery and optimization of materials for efficient energy conversion, including solar cells, fuel cells, and thermoelectric materials that drive renewable energy technologies.

Energy Storage: AI-driven advancements in materials for high-capacity, long-life batteries, supercapacitors, and other energy storage technologies critical for sustainable energy infrastructure.

Electronics: AI applications in designing nanomaterials for next-generation electronics, including transistors, semiconductors, and integrated circuits with enhanced performance, miniaturization, and energy efficiency.

Photonics: Development of photonic materials through AI-driven methods, enabling advancements in optical computing, telecommunications, and photonic devices that rely on precise light manipulation.

Optoelectronics: AI-driven material innovations in optoelectronics, such as light-emitting diodes (LEDs), laser diodes, and photodetectors, which are crucial for high-performance display and communication technologies.

Space and Aerospace: Application of AI to design lightweight, durable materials suited for extreme environments in space and aerospace, supporting innovations in satellite, spacecraft, and aerospace material technologies.

Information and Communication Technologies: Advancements in nanomaterials for data storage, sensors, and communication devices, where AI helps optimize material properties for high-performance, durable, and miniaturized ICT applications.

Materials Database Development: Creating comprehensive materials databases for material science, emphasizing data organization, standardization, and accessibility to accelerate materials discovery through advanced AI-driven techniques.

AI-Algorithm Innovation: Developing novel algorithms for materials prediction and optimization, enabling more efficient and accurate guidance for advanced materials design.

Machine Learning Force Fields: Developing efficient and accurate machine learning-based force fields enables precise modeling of atomic interactions, bridging the gap between computational speed and predictive accuracy in atomic simulations.

AI for Materials encourages contributions from all areas where AI intersects with material science, welcoming both experimental and theoretical studies that address cross-disciplinary challenges. This scope ensures that the journal serves as a comprehensive resource for novel AI methodologies and applications in material science, showcasing innovations with broad applicability across sectors and disciplines.