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
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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)
专著(0)
科研奖励(0)
会议论文
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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)
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批准号:EP/V001256/1
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项目类别:Research Grant
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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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