Probabilistic Machine Learning Driven Discovery and Design of New Materials for Sustainable Energy and Transport

概率机器学习驱动可持续能源和运输新材料的发现和设计

• 批准号: RGPIN-2018-04642
• 负责人: Singh, ChandraVeer
• 金额: $ 9.32万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758269
• 关键词: Probabilistic; Machine; Learning; Driven; Discovery

Providing clean, reliable and environment-friendly energy is a critical global challenge. To overcome this, the transportation and energy industries are undergoing a paradigm shift by adopting newer and better materials technologies. For speeding up the process of materials development, traditional trial-and-error based experimental approaches are being replaced by a synergistic integration of computational materials science with targeted experimentation. My group focuses on using this Integrated Computational Materials Engineering approach: (i) to design lighter, stronger, and tougher materials for automotive and aerospace structures to boost fuel economy while maintaining their safety and performance; and (ii) to discover novel materials to make sustainable energy technologies such as batteries, catalysts, and solar cells more efficient and cost-effective. The first theme caters towards improving energy efficiency while the latter towards developing new technologies for clean energy production. Designing new materials is, however, quite complex and in this respect, the emerging field of machine learning (ML) can help accelerate the pace of materials development by capturing patterns from data consisting of a multitude of variables that are difficult to capture from human intuition. The overarching goal of the proposed research program is to design and discover new materials for lightweight transportation and sustainable energy by effectively combining mathematically robust Bayesian machine learning techniques with physically accurate atomistic modeling. For structural materials, the aim is to develop multiscale material models with high fidelity and efficiency that are able to predict the global response including failure. Using ML on datasets generated by high-throughput density functional theory computations, the proposed research will also: (i) map out the structure-mechanical property relationships for a wide range of two dimensional materials, (ii) screen electrode materials for metal-air batteries with optimum capacity and life-time performance, (iii) design gas-phase catalysts for CO2 reduction, and (iv) develop robust interatomic potentials for steels widely used in structural applications. Our long-term vision is to physically realize proposed material designs and commercialize them in close collaboration with experimental and industry partners. The proposed program will contribute by developing new scientific knowledge and materials technologies for NSERC's target areas in Advanced Manufacturing and train six PhD students as future leaders in the energy, manufacturing and transportation industries. Practically, it will lead to design tools for the Canadian manufacturing industry to create stronger and tougher lightweight materials, new battery materials for automotives, and new catalysts for solar energy conversion.

提供清洁，可靠和环保能源是全球挑战。为了克服这一点，运输和能源行业正在通过采用更新和更好的材料技术进行范式转变。为了加快材料开发的过程，传统的基于试验和错误的实验方法正在被计算材料科学与有针对性的实验的协同整合所取代。我的小组专注于使用这种集成的计算材料工程方法：（i）设计更轻，更强壮，更坚固的汽车和航空航天结构，以增强燃油经济性，同时保持其安全性和性能； （ii）发现新型材料以制造可持续的能源技术，例如电池，催化剂和太阳能电池，更有效和成本效益。第一个主题旨在提高能源效率，而后者则朝着开发新技术以进行清洁能源生产。但是，设计新材料非常复杂，在这方面，机器学习的新兴领域（ML）可以通过从人类直觉中很难捕获的多种变量的数据中捕获模式来帮助加速材料开发的速度。拟议的研究计划的总体目标是通过有效地将数学上强大的贝叶斯机器学习技术与物理上准确的原子建模相结合，设计和发现用于轻量运输和可持续能源的新材料。对于结构材料，目的是开发具有高忠诚度和效率的多尺度材料模型，能够预测包括失败在内的全球响应。使用高通量密度函数理论计算生成的数据集上的ML，拟议的研究还将：（i）绘制二维材料范围的结构机械性质关系，（ii）金属空气的屏幕电极材料具有最佳容量和寿命性能的电池，（iii）设计气相催化剂可减少CO2，并且（IV）为在结构应用中广泛使用的钢而产生强大的原子间电位。我们的长期愿景是在物理上实现拟议的材料设计，并与实验和行业合作伙伴密切合作将其商业化。拟议的计划将通过为NSERC的高级制造业目标领域开发新的科学知识和材料技术来做出贡献，并培训六位博士生作为能源，制造业和运输行业的未来领导者。实际上，这将为加拿大制造业的设计工具，以创造更强大，更坚固的轻质材料，新的汽车电池材料以及用于太阳能转换的新催化剂。