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 positives in multi-criteria screening. To demonstrate its generalizable utility, GATE -- without any problem-specific model reconfiguration -- 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 generalizable AI platform readily applicable across diverse materials discovery tasks.

翻译：人工智能已成为加速材料发现的有力工具，但现有模型大多局限于特定问题，每项新性质的研究都需要额外数据收集和重新训练。本文介绍并验证了GATE（几何对齐迁移编码器）——一种通用化人工智能框架，可联合学习涵盖热学、电学、力学和光学领域的34种物理化学性质。通过将这些性质对齐到共享几何空间中，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出现假阳性的关键因素。为验证其通用性，GATE在未经任何问题特定模型重构的情况下，应用于数据中心浸没式冷却液的发现，这是由开放计算项目定义的严苛现实挑战。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。其中四个分子通过实验或文献验证，与实验室实测数据高度吻合，性能达到或超越商用冷却剂。这些结果表明GATE可作为通用化人工智能平台，广泛应用于各类材料发现任务。