Predictive Modelling of the Fundamentals of Failure in Metals
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
Our lack of detailed understanding of the atomic scale mechanisms which lead to failure of metals through processes such as cracking, creep, embrittlement or fatigue is surprising, given the significant technological and economic impact that such understanding could generate. Examples of what could be achieved include designing stronger, lighter turbine blades for aeroplane engines, improved lightweight alloys for the automobile industry or improved radiation shields for the nuclear industry.
Progress to date has been limited partly because the current generation of continuum models for metal failure rely heavily on empirical methods. The overarching aim of this proposal is to develop new models to enable continuum-scale modelling of failure processes, in particular crack growth, by incorporating pre-computed first-principles information. Adding reliable probabilistic "error bars'' which incorporate the effects of model error, limited data, epistemic uncertainty and coarse-graining would help to address one of the major barriers holding back wider adoption of materials modelling in industry (cf. Innovate UK/KTN special interest group on Uncertainty Quantification and Management for High Value Manufacturing).
Realising these long-term aims first requires developing (i) accurate atomic scale models for `slow' failure processes in metals and (ii) a rigorous model reduction procedure to capture information lost during coarse graining, allowing complex microstructures to be modelled. This project addresses (i) in detail by developing new methodology to compute energy barriers with QM accuracy in systems large enough to capture stress concentration, with application to dislocation motion and crack growth in technologically relevant but still structurally simple single crystal model systems (nickel, aluminium and tungsten). Requirement (ii) will be explored via a case study to be further developed in future proposals.
The project is aligned with research areas in which the UK is a world leader: condensed matter (electronic structure), materials engineering (metals and alloys) and numerical analysis.
Progress to date has been limited partly because the current generation of continuum models for metal failure rely heavily on empirical methods. The overarching aim of this proposal is to develop new models to enable continuum-scale modelling of failure processes, in particular crack growth, by incorporating pre-computed first-principles information. Adding reliable probabilistic "error bars'' which incorporate the effects of model error, limited data, epistemic uncertainty and coarse-graining would help to address one of the major barriers holding back wider adoption of materials modelling in industry (cf. Innovate UK/KTN special interest group on Uncertainty Quantification and Management for High Value Manufacturing).
Realising these long-term aims first requires developing (i) accurate atomic scale models for `slow' failure processes in metals and (ii) a rigorous model reduction procedure to capture information lost during coarse graining, allowing complex microstructures to be modelled. This project addresses (i) in detail by developing new methodology to compute energy barriers with QM accuracy in systems large enough to capture stress concentration, with application to dislocation motion and crack growth in technologically relevant but still structurally simple single crystal model systems (nickel, aluminium and tungsten). Requirement (ii) will be explored via a case study to be further developed in future proposals.
The project is aligned with research areas in which the UK is a world leader: condensed matter (electronic structure), materials engineering (metals and alloys) and numerical analysis.
Planned Impact
Academic impact. This project will achieve cross-disciplinary academic impact spanning the materials science, engineering and numerical analysis communities. The new modelling techniques generated will be widely disseminated and are expected to benefit the materials modelling community. The project is expected to benefit the PI by helping to consolidate his membership of this community as an independent researcher. As identified in more detail in 'Academic Beneficiaries' above, the prospects for adoption of new methodology are excellent as the applicant is well embedded in the materials simulation community and has strong links with the numerical analysis community through project collaborator Prof. Christoph Ortner (Warwick Mathematics). Knowledge gained through developing and applying the techniques will feed into algorithm development. Two-way interaction with local experimental researchers in Engineering and Physics at Warwick is expected to expand the impact beyond the modelling community, leading to interdisciplinary academic impact. Moreover, the novel methodology to be developed here is not limited to modelling failure processes in metals and could provide QM-based insight for other localised non-equilibrium processes.
Economic impact. The project is expected to generate fundamental new insights which in the long term will help rationalise and guide future design and processing developments. This is consistent with the applicant's long term goal of providing "bottom up" effective criteria for continuum finite element analysis codes, which is of great interest to industrial partners concerned by the current lack of truly predictive continuum models. Looking beyond the lifetime of this project, it is envisaged that technology transfer will allow accurate, predictive, fracture simulations without any user-adjustable parameters to be carried out directly by industrial partners. Incorporating probabilistic error bars into this framework so that uncertainties due to incomplete data and insufficient models could be computed from the information lost during coarse graining could eventually lead to significantly enhanced adoption of materials modelling in industry (e.g. in partnership with the Innovate UK/KTN special interest group on uncertainty quantification and management, of which the applicant is an active member).
Societal impact. Scarcity of resources and the tremendous energy requirements of traditional materials processing techniques raise ever-increasing sustainability concerns. Limitations on the fuel efficiency of jet engines and the difficulties of designing materials suitable for use as radiation shields for fusion power stations are just two examples reflecting the social and economic cost of our incomplete knowledge of the fundamentals of how metals fail. The need to understand/control complex chemistry to support and guide applications is just as urgent here as in other high-priority materials problems. The high costs of laboratory investigations mean that theory must come in support of experiment to produce new knowledge. However, the complex combination of local chemistry and long range stress fields characteristic of materials failure processes means that it has so far been impossible to address these problems with quantum mechanical precision. This project aims to develop new methodology that will help scientists and engineers to model these kinds of issues, in the long term helping to inform the design of improved materials where failure processes can be better controlled.
Economic impact. The project is expected to generate fundamental new insights which in the long term will help rationalise and guide future design and processing developments. This is consistent with the applicant's long term goal of providing "bottom up" effective criteria for continuum finite element analysis codes, which is of great interest to industrial partners concerned by the current lack of truly predictive continuum models. Looking beyond the lifetime of this project, it is envisaged that technology transfer will allow accurate, predictive, fracture simulations without any user-adjustable parameters to be carried out directly by industrial partners. Incorporating probabilistic error bars into this framework so that uncertainties due to incomplete data and insufficient models could be computed from the information lost during coarse graining could eventually lead to significantly enhanced adoption of materials modelling in industry (e.g. in partnership with the Innovate UK/KTN special interest group on uncertainty quantification and management, of which the applicant is an active member).
Societal impact. Scarcity of resources and the tremendous energy requirements of traditional materials processing techniques raise ever-increasing sustainability concerns. Limitations on the fuel efficiency of jet engines and the difficulties of designing materials suitable for use as radiation shields for fusion power stations are just two examples reflecting the social and economic cost of our incomplete knowledge of the fundamentals of how metals fail. The need to understand/control complex chemistry to support and guide applications is just as urgent here as in other high-priority materials problems. The high costs of laboratory investigations mean that theory must come in support of experiment to produce new knowledge. However, the complex combination of local chemistry and long range stress fields characteristic of materials failure processes means that it has so far been impossible to address these problems with quantum mechanical precision. This project aims to develop new methodology that will help scientists and engineers to model these kinds of issues, in the long term helping to inform the design of improved materials where failure processes can be better controlled.
People |
ORCID iD |
James Kermode (Principal Investigator) |
Publications
Anand G
(2022)
Exploiting Machine Learning in Multiscale Modelling of Materials
in Journal of The Institution of Engineers (India): Series D
Barrera O
(2018)
Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum.
in Journal of materials science
Bartók A
(2018)
Machine Learning a General-Purpose Interatomic Potential for Silicon
in Physical Review X
Bartók AP
(2017)
Machine learning unifies the modeling of materials and molecules.
in Science advances
Bianchini F
(2019)
Enabling QM-accurate simulation of dislocation motion in ? - Ni and a - Fe using a hybrid multiscale approach
in Physical Review Materials
Darby J
(2022)
Compressing local atomic neighbourhood descriptors
in npj Computational Materials
Ghiringhelli LM
(2023)
Shared metadata for data-centric materials science.
in Scientific data
Golebiowski JR
(2018)
Multiscale simulations of critical interfacial failure in carbon nanotube-polymer composites.
in The Journal of chemical physics
Golebiowski JR
(2020)
Atomistic QM/MM simulations of the strength of covalent interfaces in carbon nanotube-polymer composites.
in Physical chemistry chemical physics : PCCP
Goryaeva A
(2021)
Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W
in Physical Review Materials
Grigorev P
(2024)
matscipy: materials science at the atomic scale with Python
in Journal of Open Source Software
Grigorev P
(2023)
Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods
in Acta Materialia
Grigorev P
(2020)
Hybrid quantum/classical study of hydrogen-decorated screw dislocations in tungsten: Ultrafast pipe diffusion, core reconstruction, and effects on glide mechanism
in Physical Review Materials
Hjorth Larsen A
(2017)
The atomic simulation environment-a Python library for working with atoms.
in Journal of physics. Condensed matter : an Institute of Physics journal
Kermode JR
(2020)
f90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codes.
in Journal of physics. Condensed matter : an Institute of Physics journal
Khosrownejad S
(2021)
Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations
in Physical Review Materials
Klawohn S
(2023)
Massively parallel fitting of Gaussian approximation potentials
in Machine Learning: Science and Technology
Klawohn S
(2023)
Gaussian approximation potentials: Theory, software implementation and application examples
in The Journal of Chemical Physics
Lambert H
(2018)
Imeall: A computational framework for the calculation of the atomistic properties of grain boundaries
in Computer Physics Communications
Makri S
(2019)
A preconditioning scheme for minimum energy path finding methods.
in The Journal of chemical physics
Onat B
(2020)
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials.
in The Journal of chemical physics
Podgurschi V
(2022)
Atomistic modelling of iodine-oxygen interactions in strained sub-oxides of zirconium
in Journal of Nuclear Materials
Stephenson D
(2018)
Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression.
in Microfluidics and nanofluidics
Swinburne T
(2017)
Computing energy barriers for rare events from hybrid quantum/classical simulations through the virtual work principle
in Physical Review B
Description | The primary objective of the project was to develop methodology that, for the first time, allows energy barriers for rare events to be computed with quantum mechanical precision in systems containing millions of atoms using a multiscale framework. This has been achieved and the details were reported in the article T. D. Swinburne and J. R. Kermode, Phys. Rev. B 96, 144102 (2017). Secondary objectives included applying the new methodology to predict rates for activated materials failure processes in representative systems, which was reported in the same article and more recently in P. Grigorev, T. Swinburne and J. R. Kermode, QM/MM study of hydrogen-decorated screw dislocations in tungsten: ultrafast pipe diffusion, core reconstruction and effects on glide mechanism, Phys. Rev. Materials 4 023601 (2020). Significant speed-up of the computational calculations has been demonstrated using a novel preconditioning scheme, reported in S. Makri, C. Ortner and J. R. Kermode, A preconditioning scheme for Minimum Energy Path finding methods, J. Chem. Phys 150, 094109 (2019). The final objective of disseminating the results of the new methodology through publications, conferences, open source software and a publicly available database of QM calculations has also been achieved as demonstrated through the outputs reported here. |
Exploitation Route | A case study has also been carried out to investigate how the methods developed in this project could be extended to provide input to crack growth criteria in continuum finite-element models developed by others in both academic and industrial R&D, supporting the long term aim of providing ``bottom up'' predictive failure criteria including explicit uncertainty quantification; this feeds into an ongoing area of active research which has recently been supported by follow-on funding (detailed in this submission). |
Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology |
URL | https://warwick.ac.uk/fac/sci/eng/staff/jrk |
Description | Two PhD researchers who's work was aligned with the project have now graduated and moved into R&D in the private sector, where they are continuing to use the skills developed during the project. A PDRA associated with the project has moved to a new research position elsewhere in Europe, consolidating an international collaboration and improving cultural links. |
First Year Of Impact | 2018 |
Sector | Chemicals,Education |
Impact Types | Cultural,Societal,Economic |
Description | (NOMAD CoE) - Novel Materials Discovery |
Amount | € 5,045,821 (EUR) |
Funding ID | 951786 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 10/2020 |
End | 09/2023 |
Description | ARCHER eCSE |
Amount | £43,747 (GBP) |
Funding ID | eCSE11-7 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2017 |
End | 02/2018 |
Description | Data-driven modelling of irradiation induced defects in fusion materials |
Amount | £50,000 (GBP) |
Funding ID | 2729506 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 10/2022 |
End | 10/2026 |
Description | EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems |
Amount | £5,752,474 (GBP) |
Funding ID | EP/S022848/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 09/2027 |
Description | EPSRC Standard Mode |
Amount | £739,715 (GBP) |
Funding ID | EP/P022065/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 04/2017 |
End | 03/2021 |
Description | EPSRC standard grant |
Amount | £442,958 (GBP) |
Funding ID | EP/R043612/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 10/2018 |
End | 03/2022 |
Description | European Database for Multiscale Modelling of Radiation Damage (ENTENTE) |
Amount | € 4,000,000 (EUR) |
Funding ID | 900018 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 09/2020 |
End | 08/2024 |
Description | How Semi-Conductor Lasers Fail - Modelling Defect Effects |
Amount | £50,000 (GBP) |
Funding ID | 2588444 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 10/2021 |
End | 10/2025 |
Description | Hydrogen diffusion and trapping in multi-principal component alloys |
Amount | £46,000 (GBP) |
Organisation | Henry Royce Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 12/2022 |
End | 03/2023 |
Description | Industrial CASE award |
Amount | £83,296 (GBP) |
Funding ID | 17000185 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 10/2017 |
End | 09/2021 |
Description | Leverhulme Research Project Grant |
Amount | £385,476 (GBP) |
Organisation | The Leverhulme Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 04/2018 |
End | 03/2022 |
Description | Multiscale Simulation of Rarefied Gas Flow for Engineering Design |
Amount | £449,193 (GBP) |
Funding ID | EP/V012002/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 07/2021 |
End | 06/2024 |
Description | Royal Society Research Grant |
Amount | £14,764 (GBP) |
Funding ID | RG160691 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 04/2017 |
End | 03/2018 |
Description | Spanning the Scales: Insights into Dislocation Mobility Provided by Machine Learning and Coarse-Grained Models |
Amount | £50,000 (GBP) |
Funding ID | 2588438 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 10/2021 |
End | 09/2025 |
Description | Standard grant |
Amount | £823,000 (GBP) |
Funding ID | EP/R012474/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2018 |
End | 02/2021 |
Description | Support for advanced transition state search techniques in CASTEP |
Amount | £29,000 (GBP) |
Funding ID | 2nd ARCHER2 eCSE call |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2021 |
End | 08/2021 |
Description | The UK Car-Parrinello HEC Consortium |
Amount | £563,229 (GBP) |
Funding ID | EP/X035891/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2023 |
End | 12/2026 |
Description | UKIERI-DSF Partnership Development Workshop |
Amount | £15,000 (GBP) |
Organisation | British Council |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2020 |
End | 12/2020 |