Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics. Practical design must satisfy multiple properties and physical constraints, yet existing machine-learning-based approaches to such design often depend on large datasets, retraining, or task-specific generators. Here, we show that direct predictor-guided gradient optimization enables data-efficient, constraint-rich crystal design by combining off-the-shelf predictors with site-wise element masks, template initialization, and task-specific losses. In perovskites, it outperformed generative and Bayesian baselines under three targets -- band gap, formation energy, and tolerance factor -- and two hard constraints. DFT assessment further showed band-gap targeting competitive with a leading generative model despite using predictors trained on roughly one-tenth of the data. By flexibly combining pretrained predictors with application-oriented masks and custom losses, the same framework supported half-metal design. Such modularity could help researchers and engineers translate diverse application requirements directly into optimized candidate crystals with minimal computational cost.

翻译：先进的晶体设计可加速从光伏到自旋电子学等应用领域中的材料发现。实际设计需满足多种物性约束，而现有的基于机器学习的方法通常依赖大规模数据集、重训练或特定任务生成器。本文证明，通过将现成预测器与位点元素掩码、模板初始化及任务特定损失函数相结合，直接基于预测器的梯度优化能够实现数据高效且约束丰富的晶体设计。针对钙钛矿材料，该方法在带隙、形成能和容忍因子三个目标及两个硬约束下均优于生成式基线和贝叶斯基线。DFT评估进一步表明，尽管所用预测器仅基于约十分之一的数据训练，其带隙优化性能仍与领先生成模型相当。通过灵活组合预训练预测器、面向应用的掩码和自定义损失函数，同一框架还支持半金属设计。这种模块化特性可帮助研究人员和工程师以最小计算成本将多样化应用需求直接转化为优化的候选晶体。