# MAID — International Conference on Materials AI Discovery

- **Submission deadline**: 2027-04-05
- **Notification date**: 2027-05-05
- **Conference date**: 2027-08-13
- **Location**: Hong Kong, China
- **Trackers**: 0
- **Attendees**: 0
- **Canonical page**: https://www.myhuiban.com/conference/5875

## Call for papers

MAID 2027 welcomes original research contributions across a broad range of AI-driven materials discovery topics. Authors are invited to submit full papers describing original, previously unpublished research. TRACK 01 Machine Learning Potentials & Force Fields ML-based interatomic potentials for accelerated materials simulations, including neural network potentials, Gaussian approximation potentials, and equivariant models. TRACK 02 Generative Models for Materials Design GANs, diffusion models, VAEs, and transformer-based architectures for inverse design, crystal structure prediction, and de novo molecule generation. TRACK 03 High-Throughput Computational Screening DFT-based and ML-accelerated virtual screening of large chemical spaces, materials databases, and automated workflow pipelines. TRACK 04 Autonomous Laboratories & Robotics Self-driving labs, automated synthesis platforms, robotic experimentation, and closed-loop AI-driven optimization of materials properties. TRACK 05 Multimodal Data Fusion & LLMs Integrating text, images, spectral data, and simulation results with large language models, vision-language models, and multimodal foundation models for materials science. TRACK 06 Physics-Informed ML & Uncertainty Quantification Embedding physical constraints, symmetries, and conservation laws into ML models; Bayesian methods and conformal prediction for reliable materials property prediction. TRACK 07 AI for Sustainable & Energy Materials Applications to batteries, catalysts, photovoltaics, thermoelectrics, CO2 capture, and hydrogen storage materials.

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