Support for the UKCP consortium
Lead Research Organisation:
University of Warwick
Department Name: Physics
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Nguyen PV
(2019)
Visualizing electrostatic gating effects in two-dimensional heterostructures.
in Nature
Carnio E
(2019)
Multifractality of ab initio wave functions in doped semiconductors
in Physica E: Low-dimensional Systems and Nanostructures
Bianchini F
(2019)
Enabling QM-accurate simulation of dislocation motion in ? - Ni and a - Fe using a hybrid multiscale approach
in Physical Review Materials
Anand G
(2019)
Electron spin mediated distortion in metallic systems
Carnio E
(2019)
Resolution of the exponent puzzle for the Anderson transition in doped semiconductors
in Physical Review B
Horbury MD
(2020)
Exploring the Photochemistry of an Ethyl Sinapate Dimer: An Attempt Toward a Better Ultraviolet Filter.
in Frontiers in chemistry
Anand G
(2020)
Electron spin mediated distortion in metallic systems
in Scripta Materialia
Onat B
(2020)
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials
in The Journal of Chemical Physics
Prentice JCA
(2020)
The ONETEP linear-scaling density functional theory program.
in The Journal of chemical physics
Linscott EB
(2020)
ONETEP + TOSCAM: Uniting Dynamical Mean Field Theory and Linear-Scaling Density Functional Theory.
in Journal of chemical theory and computation
| Description | Compute funding associated this project was instrumental in performing large-scale GW calculations in support of ARPES experiments on 2D materials, which were published in Nature, 572, 220-223 (2019). |
| Exploitation Route | Insight into 2D materials device design |
| Sectors | Electronics Energy |
| Description | Our paper on gating effects in two-dimensional heterostructures (Nature 572, 220 (2019)) has been read 22,000 times and cited by 162 papers. As the first clear demonstration of the effect of electrostatic gating in shifting the fermi level in a 2D material heterostructure, it paved the way to subsequent use of such heterostructures in 2D materials devices. |
| First Year Of Impact | 2019 |
| Sector | Electronics,Energy,Manufacturing, including Industrial Biotechology |
| Impact Types | Economic |
| Title | ESTEEM |
| Description | ESTEEM is a python package designed to interface with the Atomic Simulation Environment, and with several advanced Electronic Structure and Molecular Dynamics codes (specifically NWChem, ONETEP, Amber and AMP), which automate and formalise the process of calculating excitations of complex systems and the modelling of potential energy surfaces by Machine Learning. It makes it relatively "black-box" to perform explicit solvent calculations, which otherwise require a high level of expertise. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | This toolkit has already been used in several papers. It has been publically released at the end of the project. |
| URL | https://esteem.readthedocs.io |
| Title | Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields |
| Description | Dataset accompanying the paper: "Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields". Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways. Published in NPJ Computational Materials: https://www.nature.com/articles/s41524-023-01124-2 Formerly on Arxiv: https://arxiv.org/abs/2301.09931 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8268725 |
| Title | Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields |
| Description | Dataset accompanying the paper: "Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields". Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways. Published in NPJ Computational Materials: https://www.nature.com/articles/s41524-023-01124-2 Formerly on Arxiv: https://arxiv.org/abs/2301.09931 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8268726 |
| Title | Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models |
| Description | Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models Liwei Zhang, Berk Onat, Genevieve Dusson, Gautam Anand, Reinhard J. Maurer, Christoph Ortner and James R. Kermode Supporting data for https://arxiv.org/abs/2111.13736. Training data The
training_data folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema: Data Group : aitb/ Datasets : H : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) S : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) energy : Energy. Unit: eV. Type: Float64. Shape: Scalar freeenergy : Free Energy. Unit: eV. Shape: Scalar unitcell : Unit cell vectors. Type: Float64. Shape: Matrix(3,3) positions : Atom positions. Type: Float64. Shape: Array(3) forces : (Optional, if available) Forces. Type: Float64. Shape: Array(3) metadata : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions., The molecular dynamics and FHI-aims parameters are described in the manuscript. On-site models The
onsite_models_ord2 folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the ACE.jl and ACEhamiltonians.jl (not yet publically available) Julia packages. There are separate files for the Hamiltonian (
*_H.json) and overlap (
*_S.json) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript. Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:
offsite_models_ord1 and
offsite_models_ord2 folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:
reference_data folder. Predicted data The
predicted_data/FCC and
predicted_data/BCC folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures.
predicted_data/FCC-to-BCC contains the results of predictions along the Bain path with the optimized model described in the manuscript. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/5796204 |
| Title | Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models |
| Description | Supporting data for https://arxiv.org/abs/2111.13736. ACEhamiltonians.jl code This is an archived copy of the ACEhamiltonians.jl code to accompany the paper arXiv:2111.13736. See https://github.com/ACEsuit/ACEhamiltoniansExamples for examples of how to use this code. The code is written in Julia and requires v1.6 or later. To install the Julia depenendencies:
test/plots.jl,
test/fcc-to-bcc.jl and
test/vacancy.jl which produce all the plots in the paper can then run as, e.g.
training_data folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema: Data Group : aitb/ Datasets : H : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) S : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) energy : Energy. Unit: eV. Type: Float64. Shape: Scalar freeenergy : Free Energy. Unit: eV. Shape: Scalar unitcell : Unit cell vectors. Type: Float64. Shape: Matrix(3,3) positions : Atom positions. Type: Float64. Shape: Array(3) forces : (Optional, if available) Forces. Type: Float64. Shape: Array(3) metadata : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions., The molecular dynamics and FHI-aims parameters are described in the manuscript. On-site models The
onsite_models_ord2 folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the ACE.jl and ACEhamiltonians.jl Julia packages. There are separate files for the Hamiltonian (
*_H.json) and overlap (
*_S.json) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript. Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:
offsite_models_ord1 and
offsite_models_ord2 folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:
16110190062237887798 BCC only Onsite models:
10293566074413000591 FCC+BCC optimised models Onsite models
4570230078043807257 Model errors The
model_errors directory contains summarised model errors for the training and testing errors for the models listed above. Reference data Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the
reference_data folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the
h5dump utility, or read in using the supplied Julia code (or indeed from other languages). Predicted data The
predicted_data/FCC and
predicted_data/BCC folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures.
predicted_data/FCC-to-BCC contains the results of predictions along the Bain path with the optimized model described in the manuscript and
predicted_data/vacancy contains the vacancy calculations. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/5796203 |
| Title | Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models |
| Description | Supporting data for https://arxiv.org/abs/2111.13736. ACEhamiltonians.jl code This is an archived copy of the ACEhamiltonians.jl code to accompany the paper arXiv:2111.13736. See https://github.com/ACEsuit/ACEhamiltoniansExamples for examples of how to use this code. The code is written in Julia and requires v1.6 or later. To install the Julia depenendencies:
test/plots.jl,
test/fcc-to-bcc.jl and
test/vacancy.jl which produce all the plots in the paper can then run as, e.g.
training_data folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema: Data Group : aitb/ Datasets : H : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) S : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns) energy : Energy. Unit: eV. Type: Float64. Shape: Scalar freeenergy : Free Energy. Unit: eV. Shape: Scalar unitcell : Unit cell vectors. Type: Float64. Shape: Matrix(3,3) positions : Atom positions. Type: Float64. Shape: Array(3) forces : (Optional, if available) Forces. Type: Float64. Shape: Array(3) metadata : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions., The molecular dynamics and FHI-aims parameters are described in the manuscript. On-site models The
onsite_models_ord2 folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the ACE.jl and ACEhamiltonians.jl Julia packages. There are separate files for the Hamiltonian (
*_H.json) and overlap (
*_S.json) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript. Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:
offsite_models_ord1 and
offsite_models_ord2 folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:
16110190062237887798 BCC only Onsite models:
10293566074413000591 FCC+BCC optimised models Onsite models
4570230078043807257 Model errors The
model_errors directory contains summarised model errors for the training and testing errors for the models listed above. Reference data Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the
reference_data folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the
h5dump utility, or read in using the supplied Julia code (or indeed from other languages). Predicted data The
predicted_data/FCC and
predicted_data/BCC folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures.
predicted_data/FCC-to-BCC contains the results of predictions along the Bain path with the optimized model described in the manuscript and
predicted_data/vacancy contains the vacancy calculations. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/6561452 |
| Title | ONETEP linear-scaling DFT code |
| Description | Linear-scaling density-functional theory code for understanding and predicting the properties of materials from first-principles quantum mechanics. |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| Impact | ONETEP is continually developed and new, updated versions are released on an annual basis. The developments associated with this grant were released during the period of the grant, between 2020 and 2025. It is one of the leading codes of its kind in the world and unique in being sold commercially: in 2004 it was adopted by Accelrys (now Dassault Systemes BIOVIA), a leading scientific software company, and has been one of the flagship products within the Materials Studio suite of software since 2008. An inexpensive academic license is also available worldwide direct from Cambridge Enterprise Ltd. Total revenue from ONETEP to date exceeds £3M from over 200 organisations worldwide. The current projects have added extensive new functionality in the area of theoretical spectroscopy, leading to the ability to describe the angle resolved photoemission spectrum of systems such as 2D material heterostructures, and much more accurate treatments of spin-polarised systems. |
| URL | http://www.onetep.org |
| Title | ONETEP linear-scaling DFT code |
| Description | Linear-scaling density-functional theory code for understanding and predicting the properties of materials from first-principles quantum mechanics. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Impact | ONETEP is continually developed and new, updated versions are released on an annual basis. The developments associated with this grant were released during the period of the grant, between 2017 and 2020. It is one of the leading codes of its kind in the world and unique in being sold commercially: in 2004 it was adopted by Accelrys (now Dassault Systemes BIOVIA), a leading scientific software company, and has been one of the flagship products within the Materials Studio suite of software since 2008. An inexpensive academic license is also available worldwide direct from Cambridge Enterprise Ltd. Total revenue from ONETEP to date exceeds £3M from over 200 organisations worldwide. The current project has added extensive new functionality in the area of theoretical spectroscopy, leading to the ability to describe uv/vis absorption from first principles in unprecedentedly large systems, such as whole proteins. |
| URL | http://www.onetep.org |