AI-driven materials design: a mini-review

2025-02-05

AI Summary

Traditional materials design relies on labor-intensive trial-and-error approaches, resulting in low efficiency and scalability. Method: This paper systematically reviews the evolution of AI-driven materials design, emphasizing the paradigm shift from high-throughput forward prediction to inverse generative design. It establishes, for the first time, the foundational “screening → generation” transformation enabled by deep generative models—including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models—and integrates machine learning, evolutionary algorithms, and reinforcement learning into a unified technical framework. Contribution/Results: The study maps key technological trajectories, critically analyzes persistent challenges—namely data scarcity, limited model interpretability, and difficulty closing the experimental loop—and proposes a methodology guide for function-driven, goal-oriented design of functional materials. The work provides both theoretical foundations and practical pathways toward intelligent, programmable materials design.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance.

Problem

• AI accelerates materials design
• Inverse design meets specific properties
• Deep generative models enable paradigm shift

Innovation

• AI-enhanced computational techniques
• Inverse design for specific properties
• Deep generative models for materials