# The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
## 4 Cloud-Based Shared Chemistry Experiment Operational Model
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Proposed by/Team** & **Model Name** & **Core Technologies** & **Key Features** & **Application Cases** \\ \hline \multirow{4}{*}{Chiappa Team} & \multirow{4}{*}{Experiment as a Service (EaaS)} & \multirow{4}{*}{Device API Standardization} & Cross-institutional device on-demand access, supports sharing of instruments like spectrometers & Cloud Manufacturing Platform \\  & & & Over 200 instruments online scheduling, visual tracking of experimental processes, support planning and execution & \\  & & & Over 200 instruments online scheduling, visual tracking of experimental processes, supports remote experiment planning and execution & \\  & & & Digital Twin + Internet of Things (IoT) & Virtual integration of experimental resources, supports global collaboration & Automated Chemistry Experiment Platform \\  & & & Tamper-proof & \\  & & & & \\ [PERSON] et al. [1] & Distributed Cloud Lab & Smart Contracts + IPFS Storage & experimental data, fully traceable processes, establishes trust among multiple parties & Decentralized Experiment Verification System \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative Analysis of Technical Solutions and Typical Cases in Cloud-shared Chemical Experiment Operation Models. aspects: first, elastic resource allocation can enhance equipment utilization; second, intelligent scheduling algorithms compress average experimental cycles; and third, building a knowledge-sharing network promotes cross-disciplinary innovation. Given this, we are committed to creating a distinctive cloud sharing chemical experiment operation model, innovatively proposing the \"Cloud-based Chemical Experiment Sharing Service Platform\" operation model. This model uses a digital platform as the core link, organically integrating cross-regional customer experimental demands with professional laboratory resource supply, establishing a bidirectional supply-demand collaboration network. Through a full-process online service system, demand parties (encompassing various enterprises/institutions such as A, B, and C) can submit experimental proposals in real time, track experiment progress at any time, and conveniently obtain experimental data reports. For their part, supply parties (professional laboratories) achieve the efficient output of equipment sharing, capacity collaboration, and technical services through intelligent resource scheduling systems. In this way, this cloud-based collaborative mechanism is expected to break the geographical restrictions and resource barriers existing in traditional experimental services, significantly enhance the utilization rate of experimental equipment, substantially shorten customer research and development cycles, and further promote the intelligent and efficient development of chemical experiments. Simultaneously, it will foster an innovative ecosystem characterized by knowledge sharing and technological complementarity within the industry. However, challenges remain severe: data sovereignty and security risks (such as the cross-border transmission and privacy protection of sensitive data in multi-institution collaboration), technical standardization barriers (compatibility issues of heterogeneous equipment interfaces and experimental protocols), and commercial sustainability (balancing high cloud platform maintenance costs with user payment willingness) still need to be overcome. Additionally, the shortage of professional talent, specifically those who understand both chemistry and cloud computing, may constrain the development and application of this model. Only by overcoming these bottlenecks can cloud sharing chemical experiments truly become the \"digital new infrastructure\" of global scientific research infrastructure.