用生成式AI逆向设计无机化合物，突破传统筛选局限
Inverse Design of Inorganic Compounds with Generative AI
- 构建数据-表示-模型一体化流程，捕捉无机物的成分、结构与电子特性
- 实现从性能目标反推化合物结构，支持晶体与金属配合物等复杂体系
- 适合材料研发人员，尤其关注可合成性与标准化评估的团队
机器学习正重塑化学研究。除了预测模型加速虚拟筛选外，生成式AI致力于实现逆向设计，将传统的“化合物→性质”预测反转为“性质→化合物”生成。尽管有机化学领域已有丰富AI工具，如药物发现，但无机化合物因内在复杂性，应用仍受限。本文综述了如何应对这一挑战，涵盖分子到晶体、过渡金属配合物及微孔材料等多样体系。重点分析生成式AI方法如何演变为融合数据表示与建模的完整流程，以刻画无机化合物的化学组成、几何构型、对称性及电子结构。未来方向包括建立基准测试标准和开发可合成性评估指标。
原文摘要 · Abstract (English)
Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However, the application of these methods to inorganic compounds remains limited by the challenges posed by their intrinsic nature. This Review analyzes how these challenges have been addressed, considering widely diverse systems ranging from molecules to crystals, including transition metal complexes and microporous materials. The analysis focuses on how generative AI methods have evolved towards data-representation-model pipelines that address the full complexity of inorganic compounds, including their chemical composition, geometry, symmetry, and electronic structure. Future directions, like benchmark standardization and the development of synthesizability metrics, are also discussed.
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