物理学报2025，Vol.74Issue(24)：12-28,17.DOI:10.7498/aps.74.20250989
目标性质导向的材料生成:迈向按需构筑的材料逆向设计
Goal-property-guided material generation:Toward on-demand construction via inverse design of materials
摘要
Abstract
In recent years,the application of machine learning in materials science has significantly accelerated the discovery of new materials.In particular,when combined with traditional methods such as first-principles calculations,machine learning models have proven effective in screening potential high-performance materials from existing databases.However,these methods are largely limited by the known chemical spaces,making it difficult to achieve the active design of novel material structures.To overcome this limitation,generative models have become a promising tool for inverse material design,providing new avenues for exploring unknown structures and property spaces.Although existing generative models have achieved initial progress in crystal structure generation,achieving property-guided material generation remains a significant challenge.In this review paper,we first introduce the representative generative models recently applied to materials generation,including CDVAE,MatGAN,and MatterGen,and analyzes their basic abilities and limitations in structural generation.We then focus on strategies for incorporating target properties into generative models to generate the property-guided structure.Specifically,we discuss four representative methods:Con-CDVAE based on target property vectors,SCIGEN with integrated structural constraints and guidance mechanisms,a fine-tuned version of MatterGen leveraging adapter-based property control,and a CDVAE latent space optimization strategy guided by property objectives.Finally,we summarize the key challenges faced by property-guided generative models and provide an outlook on future research directions.This review aims to offer researchers a systematic reference and inspiration for advancing property-driven generative approaches in material design and provides researchers with a systematic reference and insight into the advancement of property-driven generative methods for materials design.关键词
机器学习/生成模型/逆向设计/性质导向Key words
machine learning/generative models/inverse design/property-guided引用本文复制引用
刘章赫,陈新宇,周跫桦,王金兰..目标性质导向的材料生成:迈向按需构筑的材料逆向设计[J].物理学报,2025,74(24):12-28,17.基金项目
国家重点研发计划(批准号:2021YFA1500703)、国家自然科学基金(批准号:22033002,T2321002,22373013)和江苏省科技计划专项资金前沿引领技术基础研究重大项目(批准号:BK20222007,BK20232012)资助的课题. Project supported by the National Key Research and Development Program of China(Grant No.2021YFA1500703),the National Natural Science Foundation of China(Grant Nos.22033002,T2321002,22373013),and the Frontier Leading Technology Basic Research Major Project of Jiangsu Provincial Science and Technology Planning Special Fund,China(Grant Nos.BK20222007,BK20232012). （批准号:2021YFA1500703）
