A tremendous range of design tasks in materials, physics, and biology can be formulated as finding the optimum of an objective function depending on many parameters without knowing its closed-form expression or the derivative. Traditional derivative-free optimization techniques often rely on strong assumptions about objective functions, thereby failing at optimizing non-convex systems beyond 100 dimensions. Here, we present a tree search method for derivative-free optimization that enables accelerated optimal design of high-dimensional complex systems. Specifically, we introduce stochastic tree expansion, dynamic upper confidence bound, and short-range backpropagation mechanism to evade local optimum, iteratively approximating the global optimum using machine learning models. This development effectively confronts the dimensionally challenging problems, achieving convergence to global optima across various benchmark functions up to 2,000 dimensions, surpassing the existing methods by 10- to 20-fold. Our method demonstrates wide applicability to a wide range of real-world complex systems spanning materials, physics, and biology, considerably outperforming state-of-the-art algorithms. This enables efficient autonomous knowledge discovery and facilitates self-driving virtual laboratories. Although we focus on problems within the realm of natural science, the advancements in optimization techniques achieved herein are applicable to a broader spectrum of challenges across all quantitative disciplines.

翻译：在材料、物理和生物学领域中，大量设计任务可归结为在未知函数显式表达式或导数的情况下，寻找依赖多参数目标函数的最优值。传统导数自由优化技术常依赖于目标函数的强假设，因而在优化超过100维的非凸系统时表现不佳。本文提出一种用于导数自由优化的树搜索方法，能够加速高维复杂系统的最优设计。具体而言，我们引入随机树扩展、动态置信上界及短程反向传播机制以规避局部最优解，并利用机器学习模型迭代逼近全局最优解。该方法有效应对维度挑战，在各类基准函数中（最高达2000维）实现全局最优收敛，性能较现有方法提升10至20倍。我们的方法在材料、物理和生物学领域的多种真实复杂系统中展现出广泛适用性，显著优于现有最优算法。这为实现高效自主知识发现及推动自驱动虚拟实验室提供了可能。尽管本文聚焦自然科学领域的问题，但所取得的优化技术进展可拓展适用于所有定量学科中更广泛的挑战。