用生成式AI预测化学中从未见过的新现象，需融合统计力学原理。
Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
- 结合生成式AI与统计力学，提升分子模拟的预测能力。
- 现有方法在预测新化学现象上仍受限，需突破理论瓶颈。
- 适合关注下一代化学模拟与生成模型的科研人员。
生成式人工智能（Generative AI）的兴起为计算化学带来了新机遇。该方法在跨化学物种的分子结构采样、力场开发及仿真加速方面取得显著进展。本文从生成式AI与计算化学的基本理论出发，综述了自编码器、生成对抗网络、强化学习、流模型和语言模型等主流方法，并重点展示其在力场构建、蛋白质与RNA结构预测等领域的应用。核心挑战在于能否预测未观测到的化学涌现现象。我们强调，真正有用的模拟方法应能预测未知现象，因此生成式AI也必须满足这一标准。未来模型需融入统计力学等核心化学原理，以实现更高阶的预测能力。
原文摘要 · Abstract (English)
The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。