鲁棒且高效的AI框架，用于可扩展材料设计与性质预测
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
- Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本论文提出统一AI框架，通过图学习、预训练和扩散模型，实现数据高效的性质预测与可控晶体生成，降低计算成本并提升可扩展性。
AI中文摘要：
本论文开发了鲁棒且高效的AI框架，通过处理材料设计流程中的两大关键阶段——晶体性质预测与晶体结构生成——来加速晶体材料的发现。鉴于密度泛函理论（DFT）的高计算成本以及标记材料数据的有限可用性，本论文探索了图表示学习、预训练、多模态学习和生成建模，以实现可扩展的材料设计。在性质预测方面，论文首先介绍了CrysXPP，它通过无监督图自编码学习可迁移的晶体表示，减少了对大型性质标记数据集的依赖。随后提出了CrysGNN，一种大规模自监督图预训练框架，能够捕获原子连接性、化学属性和全局结构信息，并通过知识蒸馏将这些知识迁移到下游性质预测器中。CrysMMNet通过联合建模图结构和文本描述进一步丰富了晶体表示，从而融合了局部化学知识和全局结构知识。在晶体生成方面，论文引入了TGDMat，一种文本引导的联合扩散框架，该框架联合建模晶格参数、原子类型和原子坐标，并在去噪过程中融入文本结构知识。这使得能够生成更有效且更稳定的周期性材料，同时支持基于自然语言描述的条件生成。总体而言，本论文建立了一个统一的基于AI的框架，用于数据高效的性质预测和可控的晶体生成，展示了图学习、多模态表示和生成模型如何降低计算成本并提高材料发现的可持续性。
英文摘要：
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction and crystal structure generation. Motivated by the high computational cost of Density Functional Theory (DFT) and the limited availability of labeled materials data, the thesis explores graph representation learning, pretraining, multimodal learning, and generative modeling for scalable materials design. For property prediction, the thesis first introduces CrysXPP, which learns transferable crystal representations through unsupervised graph autoencoding, reducing dependence on large property-labeled datasets. It then proposes CrysGNN, a large-scale self-supervised graph pretraining framework that captures atomic connectivity, chemical attributes, and global structural information and transfers this knowledge to downstream property predictors through knowledge distillation. CrysMMNet further enriches crystal representations by jointly modeling graph structure and textual descriptions, thereby incorporating both local chemical and global structural knowledge. For crystal generation, the thesis introduces TGDMat, a text-guided joint diffusion framework that jointly models lattice parameters, atomic types, and atomic coordinates while incorporating textual structural knowledge during denoising. This enables the generation of more valid and stable periodic materials while also supporting conditional generation from natural-language descriptions. Overall, the thesis establishes a unified AI-based framework for data-efficient property prediction and controllable crystal generation, demonstrating how graph learning, multimodal representations, and generative models can reduce computational cost and improve the scalability of materials
