Recent Advances and Perspectives in AI-driven Materials Discovery
TL;DR
This review examines the latest progress through three synergistic axes: acceleration of quantum mechanical simulations via universal machine learning interatomic potentials trained on large-scale density functional theory datasets, materials-aware artificial intelligence leveraging large language models, synthetic narrative datasets, and contrastive multimodal learning for zero-shot materials retrieval.
Abstract
The rapid advancement of Artificial intelligence (AI) and machine learning is transforming materials discovery, offering the potential to accelerate the traditionally slow cycle of materials development. This review examines the latest progress through three synergistic axes: (1) acceleration of quantum mechanical simulations via universal machine learning interatomic potentials trained on large-scale density functional theory datasets, (2) materials-aware artificial intelligence leveraging large language models, synthetic narrative datasets, and contrastive multimodal learning for zero-shot materials retrieval, and (3) autonomous laboratories integrating Bayesian optimization and reinforcement learning for closed-loop experimental optimization. We discuss the specific opportunities and remaining challenges for applying these methodologies to ceramic materials and present a vision toward fully autonomous AI-driven materials discovery.