Journal of Materials Informatics (JMI) aims to advance and accelerate the pace of materials discovery, design, and deployment by synergistically combining theory, experiment, computation, and artificial intelligence in a tightly integrated and smart manner. The journal synthesizes foundational physics, chemistry, mathematics, mechanics, computer science and engineering, and materials science and engineering with emerging experiments, computations, and real-world applications, for interdisciplinary researchers and those who are new to the field. Although materials informatics is currently in the nascent stage, it is reshaping materials science and engineering in many ways at an exponential speed and its full power of synergy is still far from being realized.
The discovery and maturation of advanced materials are greatly fostered by data science, Internet, computer science and engineering, and digital technologies with huge data generated from high-throughput computations and combinatorial and high-throughput experiments. Materials informatics provides the foundations of a new paradigm of materials discovery and optimization by adding the novel tool of artificial intelligence and machine learning to the toolbox of materials science and engineering, which will definitely strengthen and enhance the power of methodologies in materials research and development. Materials informatics utilizes artificial intelligence and machine learning to analyze large ensembles of materials data from experiments, computations, manufactures, industries, daily life, etc., efficiently and cost-effectively and to deliver materials knowledge and technology in user-friendly ways to the designers of materials and products, and manufacturers. The great impact of JMI is expected to quickly build up the broader materials informatics ecosystem, provide opportunities and challenges for the design and discovery of new materials, span an array of applications of materials with targeted properties and performance.
The journal provides a platform for presentation, publication, and exchange of researches related to materials informatics, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge.
A representative journal scope includes:
- Materials data acquisition, standardization, database construction, information fusion for multi-source materials data;
- Machine learning and statistical learning of materials data;
- Data-driven discovery, design, and development of materials with enhanced properties, lowed cost and more environment friendliness;
- Data-driven development of materials science;
- Integrated multiscale and cross-scale computation, and high-throughput computation with data science/machine learning;
- Development of softwares, codes, and algorithms for materials computation and simulation, and for machine learning and statistical learning;
- High-throughput experimental technologies;
- High information gained experimental technologies to characterize multiple properties in an integrated device or/and system;
- Synergistic materials research combining theory, experiment, computation, and artificial intelligence;
- Development in materials science and engineering to predict structure-property-performance relationships of materials with machine learning or/and domain knowledge approaches;
JMI strongly encourages submission of the essential digital data and home-made software package that support published articles.