Physical function arises not only from what matter is made of, but from how interactions, geometry, topology and nonequilibrium energy flows are organized across space and time. We seek the principles that make it possible to translate desired functions into physically realizable structures and dynamics. Our research connects quantum control, active and architected matter, programmable and living materials, molecular self-assembly, precision manufacturing, adaptive energy conversion and physical computation. The long-term goal is to establish a programmable physics in which physical systems can sense, compute, reconfigure, repair and fabricate, while revealing the thermodynamic, informational and quantum limits of what matter can be designed to do.

物理功能不仅取决于物质由什么组成，还取决于相互作用、几何结构、拓扑关系与非平衡能量流如何在时空中被组织。我们研究如何从目标功能反向推导可实现的物理结构与动力学，建立连接量子调控、主动与构筑物质、可编程材料与活体材料、分子自组装、精密制造、自适应能量转换和物理计算的统一原理。其长期目标是建立一种“可编程物理学”，使物理系统能够感知、计算、重构、修复和制造，同时揭示物质设计受到的热力学、信息论与量子极限。

核心突破目标：不是获得一种新材料，而是建立从“目标功能”到“结构与动力学”的通用反向设计理论——类似一种能够把功能要求编译为物理系统的“自然编译器”。

Living systems maintain, reproduce and transform themselves through coupled networks of metabolism, information, regulation, development and evolution. We seek predictive principles connecting molecular composition and genetic information to cellular organization, phenotype, behavior and ecological function. Our research encompasses cell-free systems, artificial cells, minimal and de novo genomes, xenobiology and non-natural biochemistries, synthetic multicellular systems, biological–electronic hybrids, artificial ecosystems and open-ended evolution. Rather than merely modifying existing organisms, we aim to specify and construct new living systems, lineages and ecologies that remain viable, adaptive and controllable across generations.

生命系统通过代谢、信息、调控、发育和演化网络的协同作用，实现自我维持、自我复制与持续转变。我们研究从分子组成和遗传信息到细胞组织、表型、行为及生态功能之间的可预测规律。研究涵盖无细胞系统、人工细胞、最小与从头设计基因组、异源生物学与非天然生化体系、合成多细胞系统、生物—电子融合系统、人工生态系统和开放式演化。我们的目标不只是改造已有生物，而是能够规定并构造新的生命系统、生命谱系和生态体系，使其在跨世代演化中保持存续能力、适应能力与可控性。

核心突破目标：从非生命组分出发，构造具有自我维持、繁殖、适应和开放式演化能力的新生命谱系，并建立从组成到生命表型的预测理论。

Intelligence is more than computation or learning; it is the capacity to construct models, form goals, adapt behavior and coordinate action under uncertainty and limited resources. We seek a substrate-independent theory connecting architecture, embodiment, memory, world models, social interaction and energetic constraints to agency, generalization and collective intelligence. Our research integrates biological cognition, artificial general intelligence, brain-inspired and neuromorphic computing, embodied and morphological intelligence, material intelligence, synthetic biological intelligence, collective intelligence, metacognition, empirical studies of consciousness and human–AI coevolution. We aim to create systems capable of continual learning, causal discovery, self-modeling, collaborative science and bounded self-improvement, transforming intelligence from an abstract concept into a measurable natural phenomenon and a safely constructible capability.

智能不仅是计算或学习，更是在不确定环境和有限资源条件下构建世界模型、形成目标、调整行为并协调行动的能力。我们致力于建立一种不依赖特定物理载体的智能理论，揭示系统架构、具身性、记忆、世界模型、社会交互和能量约束如何共同产生能动性、泛化能力与集体智能。研究涵盖生物认知、通用人工智能、类脑与神经形态计算、具身与形态智能、物质智能、合成生物智能、集体智能、元认知、意识的实证研究以及人机协同演化。我们的目标是构建能够持续学习、因果发现、自我建模、协同开展科学研究并进行有边界自我改进的智能系统，使智能从抽象概念转变为可测量的自然现象和可安全构造的系统能力。

核心突破目标：建立跨越生物脑、人工网络、材料系统和群体系统的智能定量理论，解释智能何时涌现、如何扩展以及受到哪些能量、计算和信息极限约束。