Machine Learning (ML) techniques have found applications in estimating
chemical kinetics properties. With the accumulated drug molecules identified
through "AI4drug discovery", the next imperative lies in AI-driven design for
high-throughput chemical synthesis processes, with the estimation of properties
of unseen reactions with unexplored molecules. To this end, the existing ML
approaches for kinetics property prediction are required to be
Out-Of-Distribution (OOD) generalizable. In this paper, we categorize the OOD
kinetic property prediction into three levels (structure, condition, and
mechanism), revealing unique aspects of such problems. Under this framework, we
create comprehensive datasets to benchmark (1) the state-of-the-art ML
approaches for reaction prediction in the OOD setting and (2) the
state-of-the-art graph OOD methods in kinetics property prediction problems.
Our results demonstrated the challenges and opportunities in OOD kinetics
property prediction. Our datasets and benchmarks can further support research
in this direction.

利用机器学习（ML）技术，本研究探索了化学动力学性质的估计。通过 “AI4drug discovery” 发现的药物分子为基础，下一步的关键是以人工智能驱动的设计高通量化学合成过程，并估计未知反应和未探索分子的性质。为此，对于动力学性质预测的现有 ML 方法需要具备 Out-Of-Distribution（OOD）的可推广性。本文将 OOD 动力学性质预测分为三个级别（结构，条件和机制），揭示了这些问题的独特方面。在这个框架下，我们创建了全面的数据集，用于评估（1）在 OOD 设置中用于反应预测的最新 ML 方法，以及（2）用于动力学性质预测问题的最新图形 OOD 方法。我们的结果展示了 OOD 动力学性质预测中的挑战和机遇。我们的数据集和基准可以进一步支持这一研究方向。

化学动力学性质的超出分布通用预测方向

Towards out-of-distribution generalizable predictions of chemical  kinetics properties

Game development is a complex task involving multiple disciplines and
technologies. Developers and researchers alike have suggested that AI-driven
game design assistants may improve developer workflow. We present a recommender
system for assisting humans in game design as well as a rigorous human subjects
study to validate it. The AI-driven game design assistance system suggests game
mechanics to designers based on characteristics of the game being developed. We
believe this method can bring creative insights and increase users'
productivity. We conducted quantitative studies that showed the recommender
system increases users' levels of accuracy and computational affect, and
decreases their levels of workload.

利用人工智能驅動的遊戲設計輔助系統來幫助設計師建議遊戲機制，通過量化研究證明系統能提高用戶生產率，精度，減少工作負荷。