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
基本信息
- 批准号:RGPIN-2018-04642
- 负责人:
- 金额:$ 4.66万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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、可靠和环境友好的能源是一项严峻的全球挑战。为了克服这一点,交通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,传统的基于试错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集成所取代。我的团队专注于使用这种集成计算材料工程方法:(i)为汽车和航空航天结构设计更轻,更强,更坚韧的材料,以提高燃油经济性,同时保持其安全性和性能;(ii)发现新材料,使可持续能源技术,如电池,催化剂和太阳能电池更高效,更具成本效益。第一个主题是提高能源效率,第二个主题是开发清洁能源生产的新技术。然而,设计新材料是相当复杂的,在这方面,新兴的机器学习(ML)领域可以通过从由大量变量组成的数据中捕获模式来帮助加快材料开发的步伐,这些变量很难从人类直觉中捕获。** 拟议研究计划的总体目标是通过有效地将数学上鲁棒的贝叶斯机器学习技术与物理上精确的原子建模相结合,设计和发现用于轻型运输和可持续能源的新材料。对于结构材料,目标是开发具有高保真度和效率的多尺度材料模型,能够预测包括失效在内的全局响应。在高通量密度泛函理论计算生成的数据集上使用ML,拟议的研究还将:(i)绘制出各种二维材料的结构-机械性能关系,(ii)具有最佳容量和寿命性能的金属-空气电池的丝网电极材料,(iii)设计用于CO2还原的气相催化剂,以及(iv)为广泛用于结构应用的钢开发稳健的原子间相互作用势。我们的长期愿景是与实验和行业合作伙伴密切合作,实现拟议的材料设计并将其商业化。** 拟议的计划将通过为NSERC的先进制造目标领域开发新的科学知识和材料技术做出贡献,并培养六名博士生成为能源,制造和运输行业的未来领导者。实际上,它将为加拿大制造业提供设计工具,以创造更强大,更坚韧的轻质材料,用于汽车的新电池材料以及用于太阳能转换的新催化剂。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Singh, ChandraVeer其他文献
Singh, ChandraVeer的其他文献
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{{ truncateString('Singh, ChandraVeer', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2021
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2020
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
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:
522649-2018 - 财政年份:2019
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
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 - 财政年份:2019
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
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:
522649-2018 - 财政年份:2018
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
"Enhancing the performance limits of nano-structured materials through atomistic modeling, experimental validation and design optimization"
“通过原子建模、实验验证和设计优化提高纳米结构材料的性能极限”
- 批准号:
418392-2012 - 财政年份:2017
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
Experimental characterization and modeling of mechanical properties of high and intermediate Mn steels
高锰钢和中锰钢机械性能的实验表征和建模
- 批准号:
492306-2015 - 财政年份:2016
- 资助金额:
$ 4.66万 - 项目类别:
Engage Grants Program
"Enhancing the performance limits of nano-structured materials through atomistic modeling, experimental validation and design optimization"
“通过原子建模、实验验证和设计优化提高纳米结构材料的性能极限”
- 批准号:
418392-2012 - 财政年份:2016
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
"Enhancing the performance limits of nano-structured materials through atomistic modeling, experimental validation and design optimization"
“通过原子建模、实验验证和设计优化提高纳米结构材料的性能极限”
- 批准号:
418392-2012 - 财政年份:2015
- 资助金额:
$ 4.66万 - 项目类别:
Discovery Grants Program - Individual
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