The UK Car-Parrinello HEC Consortium
The UK Car-Parrinello HEC Consortium
批准号:
EP/X035891/1
负责人:
Philip Hasnip
金额:
$71.77万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
许多现代技术进步要么依赖于新材料的发展,要么依赖于对现有材料的更好控制和理解。由于材料的性质取决于其组成的原子核和电子,因此对其电子结构的准确建模是至关重要的。原则上,这应该是直截了当的,因为支配它们行为的基本量子力学方程已经知道了近100年;然而,求解这些方程非常困难。关键的进展是为能够描述真实材料的多电子系统开发了高质量的计算机模拟方法,而英国从一开始就处于这一新领域的前沿。UKCP HEC专注于密度泛函理论方法,通过开发理论、软件和算法,并在与一系列学科和行业相关的用例中利用这些创新工具,在这方面发挥了重要作用。UKCP还通过计算培训、RSE时间和Tier-1和Tier-2 HPC上的计算机分配来支持实验社区。DFT理论家、软件开发人员和用户之间的密切互动推动了创新,扩大了模拟能力,并放大了工作的影响。提议的研究并不容易符合“物理”、“化学”等传统类别;相反,UKCP是一个多学科联盟,使用共同的理论基础来推动材料科学的许多领域,在短期和长期都有可能产生重大影响。UKCP目前由物理、化学、材料科学和工程领域的24个不同节点组成,拥有150多名活跃的研究人员。每个节点是一个不同的大学系,由一名关键学者(补助金上的一名合作伙伴)代表。这项建议通过大量ARCHER2和Tier-2 HPC资源加上研究软件工程师(RSE)的支持,为整个UKCP的大量研究提供计算支持(已授予的拨款超过4000万GB)。RSE为主要的UKCP代码(CASTEP、CONQUEST和ONETEP)提供基本的专家编码支持,根据一些UKCP项目的需要开发新的代码功能,并协助培训和支持UKCP代码的用户社区。UKCP将开发新的算法、工作流程和理论方法,以提高我们的模拟能力,包括新的功能和显著提高的精度和速度。新的算法包括在DFT中嵌入机器学习方法以加快计算速度,以及支持处理大型系统(将CASTEP和ONETEP代码整合到单个工作流程中,并使DFT代码能够嵌入多尺度、多物理模拟)。GPU端口和改进的并行度使UKCP软件能够有效地利用当前和未来的HPC架构&具有更高的能效。新的功能包括具有自旋-轨道耦合的核磁共振光谱,因此可以高精度地研究完整的元素周期表,并在包括温度和环境影响在内的激发态建模方面取得进展。这些发展使更大、更复杂的系统得以研究,并将对未来技术的许多领域产生重大影响,包括LED照明、改进的耐磨性/耐腐蚀性、下一代电池、低功率电子和自旋电子设备、改进的能量收集(热电)材料、用于碳捕获/存储的新材料和用于净水的纳米颗粒。还有一些基础研究领域,以加深我们对物质基本性质的理解,如分子/金属界面的动力学,固体/液体界面的电子相互作用,生物过程中的量子效应,蛋白质与配体的结合和高压氢相。
英文摘要
Many modern technological advances are dependent upon either the development of new materials, or better control and understanding of existing materials. As materials' properties depend on their constituent nuclei and electrons, accurate modelling of their electronic structure is crucial. In principle, this should be straightforward, as the fundamental quantum mechanical equations governing their behaviour have been known for almost 100 years; however, solving these equations is extraordinarily hard. The key advance has been the development of high quality computer simulation methods for many-electron systems able to describe realistic materials, and the UK has been at the forefront of this new field since the very start. The UKCP HEC, focused on density functional theory methods, has played a fundamental part in this effort via both developing theories, software and algorithms, and exploiting these innovative tools in use cases relevant to a range of disciplines and industries.UKCP also supports experimental communities, via computational training, RSE time and computer allocations on Tier-1 and Tier-2 HPC. The close interaction between DFT theorists, software developers and users drives innovation and expands simulation capabilities, as well as magnifying the impact of the work. The research proposed does not easily fit traditional categories of "physics", "chemistry" etc; instead, UKCP is a multidisciplinary consortium using a common theoretical foundation to advance many areas of materials-based science, with the potential for significant impact both in the short and long-term. UKCP currently comprises 24 different nodes in physics, chemistry, materials science & engineering, with over 150 active researchers. Each node is a different University Department, represented by one key academic (a Co-I on the grant). This proposal provides computational support for a large body of research across UKCP (over £40M in already-awarded grants) with a substantial allocation of ARCHER2 and Tier-2 HPC resources plus Research Software Engineer (RSE) support. The RSE provides essential expert coding support for the principal UKCP codes (CASTEP, CONQUEST & ONETEP), develops new code features as required for some UKCP projects, and assists with training and supporting the UKCP codes' user-communities.The innovations in this proposal enable the next generation of simulations and further widen our computational horizons. UKCP will develop new algorithms, workflows & theoretical methods to increase our simulation abilities, in terms of both new functionality and dramatically improved accuracy & speed. New algorithms include embedding machine learning methods into DFT to speed up calculations, and enabling treatment of large systems (bringing together the CASTEP & ONETEP codes into a single workflow and enabling DFT codes to be embedded in multiscale, multiphysics simulations). GPU ports and improved parallelism enable UKCP software to exploit current and future HPC architectures effectively & with greater energy efficiency. New functionality includes NMR spectroscopy with spin-orbit coupling, so the full periodic table can be studied with high accuracy, and advances in excited state modelling, including temperature and environmental effects. These developments enable larger, more complex systems to be studied and will make significant impacts on many areas of future technology, including LED lighting, improved wear/corrosion resistance, next generation batteries, low power electronics & spintronics, improved energy-harvesting (thermoelectric) materials, new materials for carbon capture/storage and nanoparticles for water purification. There are also areas of fundamental research, to further our understanding of basic properties of matter, such as dynamics at molecule/metal interfaces, electron interactions in solid/liquid interfaces, quantum effects in biological processes, protein-ligand binding & high-pressure hydrogen phases
期刊论文(9)
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Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules.
用于类药分子键解离能预测的可转移机器学习原子间势。
DOI:
10.17863/cam.104555
发表时间:
2023
期刊:
影响因子:
--
作者:
[Gelžinyte E]
通讯作者:
Gelžinyte E
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows.
wfl Python 工具包,用于创建机器学习原子间势和相关原子模拟工作流程。
DOI:
10.17863/cam.100069
发表时间:
2023
期刊:
影响因子:
--
作者:
[Gelžinyte E]
通讯作者:
Gelžinyte E
DOI:
10.1038/s41524-024-01206-9
发表时间:
2023-09
期刊:
npj Computational Materials
影响因子:
9.7
作者:
[Sun-Woo Kim;Kang Wang;Siyu Chen;L. Conway;G. Pascut;I. Errea;C. Pickard;B. Monserrat]
通讯作者:
Sun-Woo Kim;Kang Wang;Siyu Chen;L. Conway;G. Pascut;I. Errea;C. Pickard;B. Monserrat
DOI:
10.17863/cam.104854
发表时间:
2023
期刊:
影响因子:
--
作者:
[Gelzinyte E]
通讯作者:
Gelzinyte E
CASTEP-USER: Predictive Materials Modelling For Experimental Scientists
-
批准号:EP/W030438/1
-
项目类别:Research Grant
-
资助金额:$68.97万
-
财政年份:2022
-
负责人:Philip Hasnip
-
依托单位:
Materials and Molecular Modelling (MMM) Exascale Design and Development Working Group (DDWG)
-
批准号:EP/V001256/1
-
项目类别:Research Grant
-
资助金额:$6.8万
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财政年份:2020
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负责人:Philip Hasnip
-
依托单位:
York: Transforming Research-Oriented Software Engineering
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批准号:EP/R025770/1
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项目类别:Fellowship
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资助金额:$103.24万
-
财政年份:2018
-
负责人:Philip Hasnip
-
依托单位:
国内基金
海外基金
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