IDEA: Inverse Design of Electrochemical Interfaces with Explainable AI

IDEA奖学金是一项为期5年的计划，旨在通过新兴的人工智能，应用于基础电化学工程研究的数字化转型，为英国的工业脱碳和数字化铺平道路。电化学工程是21世纪许多关键能源技术的核心，如H2生产，CO2还原，能量储存等。所有这些领域的进一步发展都需要更好地了解电化学系统中的电极-电解质界面，因为几乎所有的关键现象都发生在这样的界面上，这最终决定了动力学，热力学和系统的长期性能。设计下一代电化学接口以满足未来需求是所有类型电化学应用的共同挑战。 设计电化学界面传统上依赖于高通量筛选实验或模拟。考虑到设计空间的复杂性，这种蛮力方法具有高度迭代性和低成功率，这已成为电化学研究界面临的共同挑战，这一点也就不足为奇了。该奖学金的愿景是通过新颖的可解释人工智能（XAI）和数字解决方案，在未来的电化学界面的整个生命周期中进行设计，优化和自我进化。它将创建一个逆向设计框架，在该框架中，我们使用一组所需的性能指标作为XAI模型的输入，以我们可解释的物理意义的方式生成满足要求的电化学界面设计。该方法一旦开发出来，将解决对净零路线图至关重要的典型挑战，其中包括改进当前的系统，如H2生产/燃料电池和CO2还原，以及开发目前尚不存在的工业规模的新电化学系统，如N2还原和多离子储能。

The IDEA Fellowship is a 5-year programme to pave the way for the UK's industrial decarbonisation and digitalisation, via emerging AI, digital transformations applied to fundamental electrochemical engineering research. Electrochemical engineering is at the heart of many key energy technologies for the 21st century such as H2 production, CO2 reduction, energy storage, etc. Further developments in all these areas require a better understanding of the electrode-electrolyte interfaces in the electrochemical systems because almost all critical phenomena occur at such interface, which eventually determine the kinetics, thermodynamics and long-term performance of the systems. Designing the next generation of electrochemical interfaces to fulfil future requirements is a common challenge for all types of electrochemical applications. Designing an electrochemical interface traditionally relies on high throughput screening experiments or simulations. Given the complex nature of the design space, it comes with no surprise that this brute-force approach is highly iterative with low success rates, which has become a common challenge faced by the electrochemical research community. The vision of the fellowship is to make a paradigm-shift in how future electrochemical interfaces can be designed, optimised and self-evolved throughout their entire life cycle via novel Explainable AI (XAI) and digital solutions. It will create an inverse design framework, where we use a set of desired performance indicators as input for the XAI models to generate electrochemical interface designs that satisfy the requirements, in a physically-meaningful way interpretable by us. The methodology, once developed, will tackle exemplar challenges of central importance to the net zero roadmap, which include improving current systems such as H2 production/fuel cell and CO2 reduction, but also developing new electrochemical systems which do not yet exist today at industrial scale such as N2 reduction and multi-ion energy storage.