Preconditioners for Large-Scale Atomistic Simulations
Lead Research Organisation:
University of Warwick
Department Name: Mathematics
Abstract
Atomistic simulations are an indispensible tool of modern materials science, solid state physics and chemistry, as they allow scientists to study individual atoms and molecules in a way that is impossible in laboratory experiments. Understanding atomistic processes opens up avenues for the manipulation of matter at the atomic scale in order to achieve superior material properties (e.g., electrical, chemical, mechanical, etc.) for applications in science, engineering, and technology. This proposal focuses on the development of efficient and robust numerical algorithms for large-scale atomistic simulations.
The main bottleneck in current state of the art algorithms are preconditioners. In the context of this research preconditioners can be understood as operators transforming the space of atomistic configurations in order to give it "better" properties that enable the formulation of more efficient and more reliable computational algorithms. The state of the art molecular modelling software uses general purpose preconditioners that are not specifically targeted at large-scale atomistic systems, and are not particularly effective.
We propose to combine the wide-ranging complementary expertise of the PIs in molecular modelling, numerical optimisation, analysis and numerics of differential equations, and multiscale modelling, to construct novel preconditioners targeted specifically for interatomic potentials used in materials science applications that will achieve significant improvements in efficiency and reliability of state of the art methods.
Similar challenges arise also in phase space sampling techniques such as Markov Chain Monte Carlo methods, Hybrid Monte Carlo methods, or in transition state search. We will modify existing algorithms to take advantage of the hessian information provided by the preconditioners we will develop.
The new algorithms we will develop will enable scientists to study more complex systems and obtain more reliable results from simulations. The applications of the project are primarily in academic and industrial materials science. Our ambitious aim is to apply the new algorithms to the study of nano-particles consisting of hundreds of atoms.
The main bottleneck in current state of the art algorithms are preconditioners. In the context of this research preconditioners can be understood as operators transforming the space of atomistic configurations in order to give it "better" properties that enable the formulation of more efficient and more reliable computational algorithms. The state of the art molecular modelling software uses general purpose preconditioners that are not specifically targeted at large-scale atomistic systems, and are not particularly effective.
We propose to combine the wide-ranging complementary expertise of the PIs in molecular modelling, numerical optimisation, analysis and numerics of differential equations, and multiscale modelling, to construct novel preconditioners targeted specifically for interatomic potentials used in materials science applications that will achieve significant improvements in efficiency and reliability of state of the art methods.
Similar challenges arise also in phase space sampling techniques such as Markov Chain Monte Carlo methods, Hybrid Monte Carlo methods, or in transition state search. We will modify existing algorithms to take advantage of the hessian information provided by the preconditioners we will develop.
The new algorithms we will develop will enable scientists to study more complex systems and obtain more reliable results from simulations. The applications of the project are primarily in academic and industrial materials science. Our ambitious aim is to apply the new algorithms to the study of nano-particles consisting of hundreds of atoms.
Planned Impact
Immediate Beneficiaries
The project proposes substantial improvements to the efficiency and reliability of numerical algorithms for atomistic modelling of materials. The immediate beneficiaries of this work are researchers working in both the methodology and applications of computational molecular modelling. Methodology research will benefit from the new concepts we propose, in particular the targeted development of preconditioners based on analytic insight into the structure of the molecular models, and the methods we will develop for a rigorous analysis of our algorithms. Researchers applying computational molecular modelling techniques for the study of large molecular systems will directly benefit from substantial improvements in performance and reliability of the new algorithms over the current state of the art, which will allow them to study larger and more complex systems.
Broader Impact
The broader impact of the research arises from its applications in academic and industrial materials science and biochemistry research. The new algorithms we will develop will enable scientists to study larger system sizes and obtain more reliable results from simulations. Materials science progress that will be achieved through improved simulation tools leads, for example, to improved manufacturing processes, and the development of new materials with superior properties (e.g., strength, weight, or even entirely new and unexpected properties). For example, such improvements in engineering materials and manufacturing processes have the potential to significantly reduce energy consumption. One of the the most important applications of computational biochemistry is computer-aided drug design, which involves the design of molecules that can interact in specific ways with proteins and other types of macro-molecules and makes heavy use of similar molecular modelling software as computational materials science.
The project proposes substantial improvements to the efficiency and reliability of numerical algorithms for atomistic modelling of materials. The immediate beneficiaries of this work are researchers working in both the methodology and applications of computational molecular modelling. Methodology research will benefit from the new concepts we propose, in particular the targeted development of preconditioners based on analytic insight into the structure of the molecular models, and the methods we will develop for a rigorous analysis of our algorithms. Researchers applying computational molecular modelling techniques for the study of large molecular systems will directly benefit from substantial improvements in performance and reliability of the new algorithms over the current state of the art, which will allow them to study larger and more complex systems.
Broader Impact
The broader impact of the research arises from its applications in academic and industrial materials science and biochemistry research. The new algorithms we will develop will enable scientists to study larger system sizes and obtain more reliable results from simulations. Materials science progress that will be achieved through improved simulation tools leads, for example, to improved manufacturing processes, and the development of new materials with superior properties (e.g., strength, weight, or even entirely new and unexpected properties). For example, such improvements in engineering materials and manufacturing processes have the potential to significantly reduce energy consumption. One of the the most important applications of computational biochemistry is computer-aided drug design, which involves the design of molecules that can interact in specific ways with proteins and other types of macro-molecules and makes heavy use of similar molecular modelling software as computational materials science.
Organisations
People |
ORCID iD |
Christoph Ortner (Principal Investigator) |
Publications
Xingjie Li (Author)
(2013)
Theory-based benchmarking of the blended force-based quasicontinuum method
Li X
(2014)
Theory-based benchmarking of the blended force-based quasicontinuum method
in Computer Methods in Applied Mechanics and Engineering
Description | We have developed new algorithms and numerical software for the efficient simulation of atomistic models of materials. To date, we have 1. produced a highly efficient and robust optimisation scheme to find optimal atomistic configurations 2. an analysis and implementation of a highly efficient and robust saddle search algorithm, used for finding transition between different stable atomistic configurations. |
Exploitation Route | The new algorithms have been made available as part of a general purpose atomistic simulation library (ASE) https://gitlab.com/jameskermode/ase |
Sectors | Electronics,Energy,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
URL | https://gitlab.com/jameskermode/ase |
Description | The primary outcome of the research are new algorithms that have been incorporated into atomistic materials simulation code (ASE). For example, a collaborator has used it to speed-up materials simulations that previously would have taken several weeks, and was able to perform them within a few days. We are continuing to work on introducing the new tools to other researchers. |
First Year Of Impact | 2014 |
Sector | Other |
Title | ASE (new contributions to this library) |
Description | the geometry optimisation routines developed in this project have been incorporated into the ASE (original fork at https://gitlab.com/jameskermode/ase.) The fork has since been merged with main ASE. |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | new collaborations with various physical chemistry groups; concrete impacts likely to be realised over the coming months. |
URL | https://gitlab.com/jameskermode/ase |
Title | JuLIP.jl |
Description | Julia implementation of a range of interatomic potentials and related preconditions. |
Type Of Technology | Software |
Year Produced | 2018 |
Impact | / |
URL | https://github.com/libAtoms/JuLIP.jl |
Title | Preconditioned Geometry Optimisation |
Description | a set of prototype codes, implemented within the QUIP software package (G. Csanyi / Cambridge), for performing highly efficient and robust geometry optimisation for materials simulation. |
Type Of Technology | Physical Model/Kit |
Year Produced | 2013 |
Impact | in progress; we are now working towards introducing other research groups to the new tools we have developed. |
Title | SaddleSearch.jl |
Description | prototype implementation of saddle point and transition path search algorithms |
Type Of Technology | Software |
Year Produced | 2018 |
Open Source License? | Yes |
Impact | / |
URL | https://github.com/cortner/SaddleSearch.jl |