Knowledge Led Structure Prediction for Nanostructures

纳米粒子是广泛的日常技术应用的关键，包括石化催化、生物医学成像、光电子学、油漆、油墨、涂料和纳米医学。为了开发和优化这种应用，人们需要很好地了解所涉及的纳米粒子的原子结构，这是单靠实验很难获得的。类似的结构模型与理解纳米颗粒的毒理学和环境效应以及它们在天体化学中的作用有关。我们将开发一个链接的纳米结构预测代码(WASP@N)和网络接口数据库，旨在生成和存档这种纳米结构的结构模型。这种组合将提供一种快速而有效的方法来预测以前从未研究过的全新体系和嵌入在现实环境中的已知体系的原子结构；例如，在溶液中、在材料的气孔或表面上，和/或使用有机封顶剂，而不是在真空中隔离。后者对于理解纳米颗粒在许多工业过程(例如，液体纳米颗粒催化、油墨、涂料)以及例如纳米毒理学中的作用至关重要，但由于包括环境在内的额外计算成本，以前没有常规地这样做。我们坚信，将包括在WASP@N中的新算法方法与访问团簇结构数据库中真空中粒子的所有低能结构相结合，将使例如预测溶液中纳米粒子的原子结构变得更加有效，并成为应用计算化学家的标准技术。最后，星系团结构数据库还将作为实验和计算化学家、化学工程师、物理学家、电子工程师和毒物学家寻找纳米尺度材料结构信息的独立资源。

Nanoparticles are key to a wide range of day-to-day technological applications, including petrochemical catalysis, biomedical imaging, optoelectronics, paints, inks, coatings, and nanomedicine. To develop and optimise such applications, one needs a good understanding of the atomic structures of the involved nanoparticles that is difficult to obtain from experiment alone. Similar structural models are relevant to understanding the toxicology and environmental effects of nanoparticles and their role in astrochemistry. We will develop a linked nanoscale structure prediction code (WASP@N) and web-interfaced database aimed at generating and archiving such structural models of nanostructures. This combination will provide a fast and efficient way of predicting the atomic structure of both fundamentally new systems that have never been studied before and known systems embedded in a realistic environment; e.g. in solution, in the pores or on the surface of a material, and/or with an organic capping agent, rather than isolated in a vacuum. The latter is crucial for understanding nanoparticles in many industrial processes (e.g. liquid phase nanoparticle catalysis, inks, coatings) and, for instance, nanotoxicology, but has not been done routinely before due to additional computational cost of including the environment. We strongly believe that the combination of new algorithmic approaches to be included in WASP@N and access to all low energy structures for the particles in vacuum in the cluster structure database will make predicting the atomic structure of nanoparticles insolution, for example, much more efficient and a standard technique in the repertoire of the applied computational chemist. The cluster structure database, finally, will also be useful as a stand-alone resource for experimental and computational chemists, chemical engineers, physicists, electronic engineers and toxicologists looking for information on the structure of materials on the nanoscale.