🏫 AI Terakoya

Comprehensive learning platform for data-driven materials development. Systematically learn the application of AI and machine learning in materials science, covering materials informatics, chemoinformatics, bioinformatics, and cutting-edge topics including GNN, Transformer, and Bayesian optimization.

Unlock the future of chemical process optimization through data. Systematically learn data utilization in the process industry, from process monitoring and control, design of experiments, and quality control to Bayesian optimization, digital twins, and AI agents.

Comprehensive platform for systematic learning from machine learning fundamentals to practical applications. Gain deep understanding of both theory and implementation, covering supervised and unsupervised learning, neural networks, CNN, RNN, Transformer, generative models, and reinforcement learning.

Understanding materials science foundations through experiments and computation. Systematically learn the theoretical foundations of materials science, from crystallography, thermodynamics, and physical properties to materials synthesis and processing, XRD and electron microscopy analysis, and metals, ceramics, and polymers.

Cross-disciplinary learning series providing mathematical foundations for all domains. Master essential mathematical tools for materials science, process engineering, and machine learning through theory and implementation, covering mathematical physics (calculus, linear algebra, complex analysis), statistical mechanics and thermodynamics, probability and statistics, and numerical computation.

Learn quantum computing from qubits to applications in chemistry and materials. A hype-free, hands-on introduction covering superposition and entanglement, quantum gates and circuits, the landmark algorithms of Deutsch-Jozsa, Grover and Shor, and the NISQ era with variational methods such as VQE — all with verified NumPy code examples.