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
Anand G
(2020)
Electron spin mediated distortion in metallic systems
in Scripta Materialia
Baldwin WJ
(2024)
Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.
in Small (Weinheim an der Bergstrasse, Germany)
Bianchini F
(2019)
Enabling QM-accurate simulation of dislocation motion in ? - Ni and a - Fe using a hybrid multiscale approach
in Physical Review Materials
Carnio E
(2019)
Resolution of the exponent puzzle for the Anderson transition in doped semiconductors
in Physical Review B
Darby J
(2022)
Compressing local atomic neighbourhood descriptors
in npj Computational Materials
Gelžinyte E
(2023)
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows
in The Journal of Chemical Physics
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
(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
Grigorev P
(2023)
Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods
in Acta Materialia
Horbury MD
(2020)
Exploring the Photochemistry of an Ethyl Sinapate Dimer: An Attempt Toward a Better Ultraviolet Filter.
in Frontiers in chemistry
Klawohn S
(2023)
Gaussian approximation potentials: Theory, software implementation and application examples.
in The Journal of chemical physics
Klawohn S
(2023)
Massively parallel fitting of Gaussian approximation potentials
in Machine Learning: Science and Technology
Linscott EB
(2020)
ONETEP + TOSCAM: Uniting Dynamical Mean Field Theory and Linear-Scaling Density Functional Theory.
in Journal of chemical theory and computation
Loh S
(2021)
Strong in-plane anisotropy in the electronic properties of doped transition metal dichalcogenides exhibited in W 1 - x Nb x S 2
in Physical Review B
Magdau I
(2023)
Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent
in npj Computational Materials
Nguyen PV
(2019)
Visualizing electrostatic gating effects in two-dimensional heterostructures.
in Nature
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
Schaaf L
(2023)
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields
in npj Computational Materials
Witt WC
(2023)
ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.
in The Journal of chemical physics
Xia X
(2021)
Atomic and electronic structure of two-dimensional Mo (1- x )W x S 2 alloys
in Journal of Physics: Materials
Zhang L
(2022)
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
in npj Computational Materials
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 one paper (the secondary species determination paper), and will soon be used in at least two more. It has been publically released at the end of the project. |
URL | https://bitbucket.org/ndmhine/esteem |
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 |