Artificial Intelligence (AI), particularly deep learning, is profoundly transforming the field of molecular modeling and simulation, offering unprecedented accuracy and efficiency across multiple scales, from quantum mechanics to macroscopic phenomena. This interdisciplinary domain is crucial for breakthroughs in materials science, drug discovery, and chemistry.
The integration of AI has significantly propelled the development of molecular simulation, addressing long-standing challenges related to accuracy and computational cost. A key advancement is the proliferation of Machine Learning Potentials (MLPs), which aim to replicate quantum mechanical accuracy at a computational cost comparable to classical force fields. For instance, frameworks like DeePMD-kit (Ref. 1) exemplify this progress, providing robust, multi-backend platforms for generating and deploying MLPs, thereby enabling large-scale molecular dynamics simulations with near ab initio accuracy. Furthermore, innovations such as DeePKS + ABACUS (Ref. 2) are actively bridging the gap between expensive quantum mechanical calculations and MLPs, demonstrating pathways to achieve ab initio accuracy for larger systems. Similarly, deep learning methods are extending to tight-binding approaches (Ref. 3), enabling ab initio accuracy in large-scale electronic simulations at finite temperatures.
Beyond specific potentials, the field has also witnessed the rise of large atomic models and pre-trained molecular models, mirroring trends in natural language processing. DPA-2 (Ref. 4) stands out as a large atomic model capable of multi-task learning, integrating diverse data for broader applicability. Concurrently, Uni-Mol (Ref. 5) represents a significant advancement in universal 3D molecular representation learning, providing a framework to capture complex molecular properties and interactions directly from their three-dimensional structures. These advancements provide powerful tools for high-fidelity descriptions of interatomic interactions and molecular properties, moving beyond traditional empirical methods.
Despite these advancements representing the cutting edge of AI-empowered molecular modeling, significant challenges remain, particularly in achieving seamless multi-scale integration and developing robust methodologies that can effectively span disparate spatial and temporal scales. Current efforts often focus on improving individual components within a single scale or two adjacent scales (e.g., better potentials, larger models). This workshop aims to foster discussion and collaboration on how to strategically combine and extend these cutting-edge AI techniques to construct truly integrated multi-scale simulation frameworks. By bringing together leading experts, this workshop will identify key research directions and synergistic opportunities to push the boundaries of AI-empowered multi-scale molecular modeling, accelerating scientific discovery across numerous fields.
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