Ashbrook Sharon E
School of Chemistry, EaStCHEM and Centre of Magnetic Resonance, University of St Andrews, North Haugh, St Andrews KY16 9ST, UK.
Faraday Discuss. 2025 Jan 8;255(0):583-601. doi: 10.1039/d4fd00155a.
This explored the field of NMR crystallography, and considered recent developments in experimental and theoretical approaches, new advances in machine learning and in the generation and handling of large amounts of data. Applications to a wide range of disordered, amorphous and dynamic systems demonstrated the range and quality of information available from this approach and the challenges that are faced in exploiting automation and developing best practice. In these closing remarks I will reflect on the discussions on the current state of the art, questions about what we want from these studies, how accurate we need results to be, how we best generate models for complex materials and what machine learning approaches can offer. These remarks close with thoughts about the future direction of the field, who will be carrying out this type of research, how they might be doing it and what their focus will be, along with likely possible challenges and opportunities.
本文探讨了核磁共振晶体学领域，考量了实验和理论方法的最新进展、机器学习以及大量数据生成与处理方面的新进展。对广泛的无序、非晶态和动态系统的应用展示了该方法所能提供的信息范围和质量，以及在利用自动化和发展最佳实践方面所面临的挑战。在这些结束语中，我将思考关于当前技术水平的讨论、我们对这些研究的期望、结果需要多准确、如何为复杂材料生成最佳模型以及机器学习方法能提供什么等问题。这些结束语还涉及对该领域未来方向的思考，谁将开展这类研究、他们可能如何进行以及重点是什么，以及可能面临的挑战和机遇。
