MiqroForge Platform Integrates AI to Accelerate Cross-Scale Computational Materials Science

Computational materials science and biology increasingly rely on complex, multi-scale simulations, yet current software often struggles to meet these demands due to limited platform capabilities. Jianan Wang, Wenbo Guo, and Xin Yue, from Miqro Era Quantum Technology and Digital Technology companies, alongside their colleagues, present MiqroForge, a new intelligent platform designed to overcome these challenges. MiqroForge integrates diverse computing resources and employs artificial intelligence to schedule tasks, streamlining workflows and improving computational efficiency dynamically. By combining this power with an accessible visual interface and collaborative data sharing, the platform lowers barriers to entry. It fosters a more connected ecosystem for researchers working across classical and quantum computing domains.

Widely adopted in mature software engineering, collaborative development accelerates progress. However, computational chemistry, computational materials science, and computational biology face persistent demands for multi-scale simulations constrained by simplistic platform designs. This research presents MiqroForge, an intelligent cross-scale platform integrating quantum computing capabilities. By combining AI-driven dynamic resource scheduling with an intuitive visual interface, MiqroForge significantly lowers entry barriers while optimising computational efficiency. The platform fosters a collaborative ecosystem through shared node libraries and data repositories, thereby bridging practitioners across disciplines.

Cross-Scale Computation with Modular Nodes

MiqroForge is a platform designed to facilitate cross-scale computation, integrating classical and quantum resources for complex scientific problems. The platform simplifies complex multi-step computations by providing a standardized framework for connecting diverse computational resources and enabling seamless data transfer between them. It utilizes a modular node system, where each node represents a specific computational task, such as a quantum chemistry simulation or molecular dynamics calculation, allowing users to combine these nodes to create complex workflows. MiqroForge integrates a heterogeneous resource pool, including CPU and GPU clusters, access to both real quantum hardware and simulators, and configurable high-speed storage.

An intelligent scheduling system manages workflows through a three-layer approach. Task Management handles workflow lifecycle, monitoring, and error handling. The Intelligent Decision Layer dynamically allocates resources based on load and task priority. The Execution Engine provides fine-grained resource allocation with policies for latency-sensitive tasks and quantum computing. A robust data governance framework manages data throughout the computational process, capturing complete execution context for reproducibility, providing hierarchical data storage and automatic purging of non-essential data, and offering tools for visualizing and analyzing data.

MiqroForge adheres to OpenAPI 3. 0 specifications, allowing integration of third-party resources. Key innovations focus on bridging the gap between classical and quantum computing, automating data transfer and resource allocation, ensuring reproducibility, and enabling scalability through heterogeneous resources. Future development will focus on expanding the library of available nodes, improving the scheduling system to optimize resource utilization, and fostering a community contribution platform.

Visual Workflow Accelerates Multi-Scale Simulations

MiqroForge is a new platform designed to accelerate multi-scale simulations in fields like materials science and computational biology, areas often hampered by complex workflows and platform limitations. Unlike traditional approaches, MiqroForge allows users to construct simulations by connecting pre-built computational ‘nodes’ in a visual workflow, using a ‘connect-fill-run’ paradigm where users link nodes, define input parameters, and then execute the entire simulation. Each node encapsulates a self-contained computational task, packaged within a Docker container to ensure reproducibility and portability across different computing resources.

This containerization guarantees consistent results regardless of the underlying hardware or software environment, a critical feature for collaborative research. Nodes are designed to be reusable, promoting modularity and simplifying the process of building complex simulations from smaller, well-defined components. MiqroForge streamlines the simulation process through detailed node configuration files, which define the node’s identity, input and output interfaces, and resource requirements, allowing the platform to intelligently schedule tasks and optimize performance. The system automatically analyzes node performance data to estimate resource needs, such as memory and CPU time, and suggests appropriate parallelization strategies.

This intelligent scheduling system significantly improves computational efficiency, allowing researchers to tackle larger and more complex simulations than previously possible. A key feature is the platform’s emphasis on self-documentation and validation, with each node including comprehensive documentation, example configurations, and test cases, enabling users to quickly understand its functionality and verify its correctness. By combining a user-friendly interface with powerful computational capabilities, MiqroForge promises to transform the way researchers approach multi-scale simulations, enabling new insights in a wide range of scientific disciplines.

MiqroForge addresses limitations in current computational workflows, particularly within materials science, computational biology, and potentially quantum chemistry. By fostering collaboration through shared resources and data repositories, MiqroForge seeks to bridge the gap between classical and emerging computational methods. The authors acknowledge that existing workflow tools often lack standardization, cross-language compatibility, and intelligent resource management, hindering broader adoption and research expansion. MiqroForge attempts to overcome these challenges, though the platform’s full potential may depend on the increasing integration of quantum algorithms and the need for workflows that combine classical and quantum approaches. Future work will likely focus on expanding the platform’s capabilities and demonstrating its effectiveness across diverse scientific applications.