A platform for future development and application of the ONETEP software

计算机模拟在我们的社会中扮演着越来越重要的角色，例如，飞行模拟器可以让飞行员接受比在空中更便宜、更安全的培训。在科学和技术领域，计算机模拟是理解甚至预测真实材料复杂过程的有力工具。模拟通常与传统实验一起使用，但它们也可以用于实验过于昂贵甚至无法进行的情况，例如在极端条件下研究材料时，例如在地核中发现的高温和高压。上个世纪之交，随着量子力学（QM）的发现，一场科学革命开始了。量子力学是一种在原子尺度上以惊人的精度描述世界的理论，因此为所有低能物理学、化学和生物学提供了基础。至少在原则上，量子力学是我们今天社会所依赖的微电子、化学和制药工业的基础。问题在于量子力学的方程非常复杂。即使在最快的计算机上，也只能精确地解决小分子的问题，而今天科学家感兴趣的系统包含数千个。由于问题的规模，即使计算机技术的快速和不懈的进步也无法克服这个问题。完成一项任务所需的工作量通常随着它的大小而增加，例如，修剪草坪所花费的时间与它的面积成正比：花园的大小是花园的两倍，所花费的时间也是花园的两倍。这是线性扩展的一个例子，但是执行许多任务的工作量增加得更快。在纸牌游戏中，安排一手牌的比例通常是所涉及物品数量的平方：纸牌数量的三倍，则需要9倍（3的平方）的时间。有些甚至更糟糕，例如解决旅行推销员问题，找到到达给定地点的最快路线。增加一个额外的位置会使解决问题的时间增加一倍。如果一分钟可以完成三个地点，那么四个地点需要两分钟，五个地点需要四分钟。仅仅22个就需要整整一年！当增加被模拟的原子数量时，求解QM的精确比例是这样的。然而，在20世纪60年代，密度泛函理论（DFT）的发现取得了飞跃，并因此获得了1998年的诺贝尔奖。DFT的显著结果是，整个系统的物理性质（我们想要问的问题的答案）原则上可以在与原子数量成线性比例的时间内计算出来。这里提出的研究涉及世界上执行线性缩放DFT计算的主要软件之一。到目前为止，该ONETEP代码已在包含多达30,000个原子的系统上进行了演示。该方法将在广泛的领域扩展量子力学模拟的范围和规模。我们打算利用该平台资助解决的问题包括：-通过模拟蛋白质之间的相互作用来深入了解新药的设计-设计用于太阳能转换和存储的新材料-研究纳米颗粒作为化学反应催化剂的特性-了解与导致机械磨损的摩擦相关的微观过程。我们的愿景是创建一个虚拟实验室，使用ONETEP作为一系列技术之一模拟真实实验的结果，例如物质如何吸收光。虚拟实验室提供了完全的控制：你可以改变材料或分子中原子的排列，并看到效果。虚拟实验室永远不会取代真实的实验室，但它有望成为一个强大的工具。

Computer simulations play a growing role in our society e.g. flight simulators allow pilots to be trained more cheaply and safely than in the air. In science and technology, computer simulation is a powerful tool for understanding or even predicting complex processes in real materials. Simulations are often used alongside conventional experiments, but they can also be used in situations where experiments would be too expensive or even impossible to perform e.g. when studying materials in extreme conditions such as the high temperatures and pressures found in the Earth's core.The turn of the last century saw the start of a scientific revolution with the discovery of quantum mechanics (QM), a theory that describes the world on the atomic scale with astonishing accuracy, and thus provides the foundation for all of low-energy physics, chemistry and biology. In principle at least, quantum mechanics underlies the microelectronics, chemical and pharamaceutical industries upon which our society relies today. The challenge is that the equations of QM are very complicated. Even on the fastest computers it is only possible to solve them exactly for small molecules, whereas the systems of interest to scientists today contain many thousands. Even the rapid and relentless progress of computer technology cannot overcome this because of the scaling of the problem.The work needed to complete a task usually increases with its size e.g. the time taken to mow a lawn is proportional to its area: double the size of the garden and it takes twice as long. This is an example of linear scaling, but the effort to do many tasks increases more rapidly. Arranging a hand in a game of cards usually scales as the square of the number of objects involved: triple the number of cards and it takes nine (three squared) times as long. Some are even worse e.g. solving the travelling salesman problem to find the quickest route which visits a given set of locations. Adding one extra location doubles the amount of time to solve the problem. If three locations can be done in one minute, four will take two minutes, and five will take four minutes. Just 22 will take a whole year! Solving QM exactly scales like this when increasing the number of atoms being simulated.However in the 1960s a leap forward was made with the discovery of density-functional theory (DFT), for which the Nobel Prize was awarded in 1998. The remarkable result of DFT is that the physical properties of the whole system (the answers to the questions we want to ask) can in principle be calculated in a time that scales linearly with the number of atoms. The research proposed here relates to one of the leading pieces of software for performing linear-scaling DFT calculations in the world. This ONETEP code has been demonstrated on systems containing up to 30,000 atoms so far. This method will expand the scope and scale of QM simulations across a wide range of fields. The problems we intend to address using this Platform grant include:- giving insight into the design of new drugs by simulating the interactions between proteins- designing new materials for solar energy conversion and storage- studying the properties of nanoparticles as catalysts for chemical reactions- understanding the microscopic processes associated with friction that cause machinery to wear outOur vision is to create a virtual laboratory which uses ONETEP as one of a family of techniques to simulate the results of real experiments e.g. how a material absorbs light. The virtual lab gives total control: you can change the arrangement of atoms in a material or molecule and see the effect. The virtual lab will never replace the real one, but it promises to be a powerful tool alongside it.