Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry and drug discovery. Developed for computationally intensive applications, most molecular simulation software involves significant use of hard-coded derivatives and code reuse across various programming languages. In this Review, we first align the relationship between molecular simulations and artificial intelligence (AI) and reveal the coherence between the two. We then discuss how the AI platform can create new possibilities and deliver new solutions to molecular simulations, from the perspective of algorithms, programming paradigms, and even hardware. Rather than focusing solely on increasingly complex neural network models, we introduce various concepts and techniques brought about by modern AI and explore how they can be transacted to molecular simulations. To this end, we summarized several representative applications of molecular simulations enhanced by AI, including from differentiable programming and high-throughput simulations. Finally, we look ahead to promising directions that may help address existing issues in the current framework of AI-enhanced molecular simulations.
Comprehensive Summary SPONGE (Simulation Package tOward Next GEneration molecular modeling) is a software package for molecular dynamics (MD) simulation of solution and surface molecular systems. In this version of SPONGE, the all‐ atom potential energy functions used in AMBER MD packages are used by default and other all‐atom/coarse‐ grained potential energy functions are also supported. SPONGE is designed to extend the timescale being approached in MD simulations by utilizing the latest CUDA‐ enabled graphical processing units (GPU) and adopting highly efficient enhanced sampling algorithms, such as integrated tempering, selective integrated tempering and enhanced sampling of reactive trajectories. It is highly modular and new algorithms and functions can be incorporated con veniently. Particularly, a specialized Python plugin can be easily used to perform the machine learning MD simulation with MindSpore, TensorFlow, PyTorch or other popular machine learning frameworks. Furthermore, a plugin of Finite‐Element Method (FEM) is also available to handle metallic surface systems. All these advanced features increase the power of SPONGE for modeling and simulation of complex chemical and biological systems. What is the most favorite and original chemistry developed in your research group? Our research centers at developing methods and theories to unravel molecular mechanisms of chemical and biological systems. By establishing theoretical models, developing enhanced sampling methods combined with machine learning techniques, we are able to conduct comprehensive thermodynamic and dynamic analyses for these complex systems. How do you get into this specific field? Could you please share some experiences with our readers? I got into theoretical chemistry as a PhD student. My PhD adviser Prof. Rudolph A. Marcus led me into this field and inspired me by his love of science. Enjoy life, always learn new things and be independent in thinking are something I learnt from my advisers (Professors Dalin Yang, Qihe Zhu, Rudy Marcus, and Martin Karplus) and would love to pass to my students. How do you supervise your students? We learn from each other. What is the most important personality for scientific research? Curiosity, passion, and persistence have been of great value to my career. What are your hobbies? What's your favorite book(s)? Reading, Ping‐Pong, and jogging. I always enjoy reading history. Who influences you mostly in your life? Too many, family, academic advisors, friends, students, and colleagues.
Preparation of non‐conjugated luminescent polymers (NCLPs) with excellent cluster luminescence (CL) performance is of great significance for scientific and industrious applications, and yet improving the performance of NCLPs through proper structural design is still a huge challenge. Herein, we report a non‐conjugated ionized polymeric system consisting of (−)‐camphorsulfonic acid ((−)‐CSA) and poly(2‐vinylpyridine) (P2VP). These acid‐base complexes exhibit typical excitation‐dependent fluorescence and room‐temperature phosphorescence (RTP) with a lifetime up to 364 ms. We discover that changing the stereoregularity from atactic to isotactic significantly improves the CL performance of the complex. It (1) broadens the fluorescence emission spectra to cover the entire visible region, (2) enhances the fluorescence emission intensity at long wavelength beyond 500 nm, (3) enhances the phosphorescence intensity, and (4) extends the phosphorescence lifetime. Systematical experimental characterization and molecular dynamics simulation unravel the key role of stereoregularity in determining the formation of different pyridine aggregates that strongly influence the CL performance. Moreover, the different luminescence shows great potential in excitation divided information display and time‐resolved encrypted display. This work not only points to a new direction for developing NCLPs with excellent performance, but also broadens the applications of NCLPs materials.
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