Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection and retraining for each new property. Here we introduce and validate GATE (Geometrically Aligned Transfer Encoder) -- a generalizable AI framework that jointly learns 34 physicochemical properties spanning thermal, electrical, mechanical, and optical domains. By aligning these properties within a shared geometric space, GATE captures cross-property correlations that reduce disjoint-property bias -- a key factor causing false negatives in multi-criteria screening. To demonstrate its generalizability, GATE -- without any problem-specific reconfiguration -- was directly applied to the discovery of immersion cooling fluids for data centers, a stringent real-world challenge defined by the Open Compute Project (OCP). Screening billions of candidates, GATE identified 92,861 molecules as promising for practical deployment. Four were experimentally or literarily validated, showing strong agreement with wet-lab measurements and performance comparable to or exceeding a commercial coolant. These results establish GATE as a ready-to-use, generalizable AI platform readily applicable across diverse materials discovery tasks.

翻译：人工智能（AI）已成为材料发现的有力加速器，但现有模型大多针对特定问题，每项新性质的预测均需额外数据收集与重新训练。本文提出并验证了GATE（几何对齐迁移编码器）——一种通用AI框架，可联合学习涵盖热学、电学、力学及光学领域的34种物理化学性质。通过将这些性质对齐至共享几何空间，GATE能够捕捉跨性质关联，从而减少离散性质偏差——这是导致多标准筛选中假阴性的关键因素。为验证其通用性，GATE在未经任何问题特定重构的情况下，直接应用于数据中心浸没式冷却液的发现任务，这是由开放计算项目（OCP）定义的严格现实挑战。通过筛选数十亿候选分子，GATE识别出92,861个具有实际应用潜力的分子。其中四个分子已通过实验或文献验证，其与湿实验室测量结果高度吻合，性能达到或超越商用冷却剂水平。这些结果表明GATE可作为即用型通用AI平台，广泛适用于各类材料发现任务。