Chemistry and Mathematics in Phase Space (CHAMPS)
Lead Research Organisation:
University of Bristol
Department Name: Mathematics
Abstract
The 6 year CHAMPS Programme Grant (PG) addresses the urgent need to provide a framework for understanding and exploiting the explosion in dynamical information coming out of modern experiments and simulations in chemistry and chemical biology. By developing methods in nonlinear dynamics to replace the ubiquitous configuration-space projections of these inherently multidimensional datasets with phase-space representations, it will be possible to provide comprehensible models that capture the key dynamics of complex systems. These new models will revolutionise our understanding of chemical transformation, with impacts on all industries that rely on understanding chemical change, from the pharmaceutical industries to those in the rapidly developing energy sector. By its very nature, the research will have impact on a wide variety of EPSRC research themes, including health care, energy, and environmental change. And because the work requires an unprecedented partnership between mathematicians and chemists, it will bridge several themes that EPSRC has highlighted as critical to its strategy. The young scientists trained in this programme will have unmatched interdisciplinary skills, and provide UK research leadership for decades to come. In particular, we expect that they will be creating their own new fields of study, and will thereby carry on attracting the best young scientists from around the world to come to the UK. A PG is vital to provide the critical mass, flexibility and scope needed to tackle the major scientific cross-disciplinary challenges set out in this proposal. The alternative funding model of individual grants to the PI and CoIs would leave the PDRAs localised in particular research groups and thereby maintain the traditional disciplinary separation, which we are seeking to break down.
Planned Impact
We are seeking to correct deficiencies in the very foundation of the science of chemistry. Success in this enterprise will have impact across all of chemistry and allied disciplines. Some practical consequences of this work may be forthcoming quickly - and we have highlighted one that has already emerged - but we anticipate that the full effects of this work are likely to be felt only once we have made the results known and comprehensible to the vast array of scientists for whom they are relevant. Certainly high-impact publications in journals such as Science and Nature would go some way to achieving that goal, but any time one requires people to "unlearn" things that they think they already know, there is resistance and inertia to change. For that reason, we anticipate hosting a number of conferences and workshops to discuss our work, and to receive feedback from potential stakeholders on how we might address their specific needs. These gatherings will involve key individuals from both academia and private industry. Complementing this effort will be the placement of the PDRAs coming out of our research programme into academic and industrial positions where they can influence how chemistry is understood and conducted from the inside.
Organisations
Publications
Agaoglou M
(2022)
The Influence of a Parameter that Controls the Asymmetry of a Potential Energy Surface with an Entrance Channel and Two Potential Wells
in Regular and Chaotic Dynamics
Agaoglou M
(2021)
Visualizing the phase space of the HeI 2 van der Waals complex using Lagrangian descriptors
in Communications in Nonlinear Science and Numerical Simulation
Agaoglou M
(2020)
The phase space mechanism for selectivity in a symmetric potential energy surface with a post-transition-state bifurcation
in Chemical Physics Letters
Aguilar-Sanjuan B
(2021)
LDDS: Python package for computing and visualizing Lagrangian Descriptors for Dynamical Systems
in Journal of Open Source Software
Alessandri R
(2021)
Martini 3 Coarse-Grained Force Field: Small Molecules
Alessandri R
(2021)
Martini 3 Coarse-Grained Force Field: Small Molecules
Alessandri R
(2021)
Martini 3 Coarse-Grained Force Field: Small Molecules
in Advanced Theory and Simulations
| Description | The CHAMPS Programme grant has had a "step change" in our understanding of the geometrical structure of reaction dynamics in phase space. The concept of a dividing surface with the "no-recrossing property has played a central role in reaction dynamics since the work of Wigner in the 30's. Research in CHAMPS has shown how this idea can extended to many degrees-of-freedom through a generalization of the periodic orbit dividing surface idea of Pechukas and Pollak from the 70's and 80's. This work has also supplied efficient algorithms for their computation. CHAMPS research has also generalized the reactive island reaction rate formalism to many degrees-of-freedom using the ideas of Lagrangian descriptors. This mathematical research has been applied to paragimen potential energy surfaces such as the caldera potential energy surface and the valley ridge inflection point potential energy surface resulting in a new explanations for the mechanism of dynamical matching and the role of valley ridge inflection points for chemical reactivity. CHAMPS is also the leading theoretical group for studying the phase space manifestations of the roaming mechanism for chemical reactions. The CHAMPS project has allowed us to develop new computational methods of Quantum Dynamics in chemistry, based on the idea of using quantum basis sets guided by trajectories in phase space. The methods work in a manner similar to classical Molecular Dynamics with the difference that Quantum dynamics is described by an ensemble of coupled trajectories in phase space not by a single classical trajectory. Our new methods and algorithms treat the motion of nuclei in molecules taking into account several electronic states and transitions between them. Multiconfigurational Ehrenfest (MCE) and Ab Initio Multiple Cloning (AIMC) method developed within CHAMPS have been shown to be very efficient comparably to other existing techniques, which use the same general idea of trajectory guided basis. MCE AIMC combines the best features of surface hoping and Ehrenfest dynamics. Also AIMC treats correctly quantum geometric phase. MCE AIMC algorithm owes its efficiency to its mathematical structure and phase space nature, which could only be developed in collaboration between chemists and mathematicians. |
| Exploitation Route | The theoretical work achieved by CHAMPS forms an essential framework fo studying phase space reaction dynamics. The MCE AIMC code developed in Leeds has been successfully used to explain and interpret ultrafast photochemistry experiments in which photo-dissociation of small molecules, which represent building blocks of larger biological molecules, have been investigated. MCE AIMC approach has also been implemented in the NEXMD code developed in Los Alamos National Laboratory. It has been used for simulations of energy transfer in conjugated molecules. This problem is important for understanding how light can be harvested and how its energy can be transported within large molecules, such as dendrimers, and converted into chemical energy. Recently MCE AIMC NEXMD code also has been used to predict signals in the cutting edge time resolved spectroscopy experiments. In the new EPSRC "Coherent States for Molecular Simulations (COSMOS)" project MCE AIMC will be implemented in the UCL Quantic code. This followup of CHAMPS will make Quantum Dynamics simulations available for broad community of chemists and molecular physicists, who will be using it in a way similar to broadly used Molecular Dynamics and Electronic Structure codes. We have also developed a collaboration with industry in which we are using MCE AIMC to predict dissociation pattern of flouroorganic molecules. These molecules are used in plasma etching technologies in microelectronic industry. Microchip manufacturers want to know chemical composition of the plasma they use and they are now in search of new fluoroorganic molecules, which would have desired properties and dissociation pattern. A Knowledge Transfer Partnership has recently been awarded to the Leeds group to develop collaboration with industrial partner. |
| Sectors | Chemicals Digital/Communication/Information Technologies (including Software) Education Energy |
| Description | We have also developed a collaboration with industry in which we are using MCE AIMC to predict dissociation pattern of flouroorganic molecules. These molecules are used in plasma etching technologies in microelectronic industry. Microchip manufacturers want to know chemical composition of the plasma they use and they are now in search of new fluoroorganic molecules, which would have desired properties and dissociation pattern. A Knowledge Transfer Partnership has recently been awarded to the Leeds group to develop collaboration with the industrial partner quantemol (https://quantemol.com/ ). |
| First Year Of Impact | 2022 |
| Sector | Chemicals,Creative Economy,Digital/Communication/Information Technologies (including Software),Education |
| Impact Types | Societal Economic Policy & public services |
| Title | Computational data for the simulation of cases of the Spin Boson Model using the Multiconfigurational Ehrenfest method - dataset |
| Description | This data gives input and output files for simulations carried out using the Multiconfigurational Ehrenfest method to show the importance of sampling methods such as cloning and trains, and how the use of these techniques in the "Multiple Cloning" scheme can allow the MCEv2 method to generate results which are converged to benchmark calculations where previously this was not possible. Included also is the source code of the program used to generate these data files. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | Data associated with 'Floquet Hamiltonian for incorporating electronic excitation by a laser pulse into simulations of non-adiabatic dynamics' |
| Description | The dataset contains the spreadsheets, hi-resolution images and raw research data associated with our paper: 'Floquet Hamiltonian for incorporating electronic excitation by a laser pulse into simulations of non-adiabatic dynamics', Chem. Phys., 515 (2018) 46-51, DOI: https://doi.org/10.1016/j.chemphys.2018.07.048 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | Data associated with 'Ultrafast Photodissociation Dynamics of 2-Ethylpyrrole: Adding Insight to Experiment With Ab Initio Multiple Cloning.' |
| Description | Ab initio multiple cloning calculated and experimental total kinetic energy release spectra, dissociation times, velocity map images and electronic state populations for the ultrafast photodissociation of 2-ethylpyrrole. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Title | Data from CHAMPS (02-2018) |
| Description | The data in this deposit were used in the paper "Empirical Classification of Trajectory Data: An Opportunity for the Use of Machine Learning in Molecular Dynamics." Carpenter, B. K.; Ezra, G. S.; Farantos, S. C.; Kramer, Z. C.; Wiggins, S. J. Phys. Chem. B, 2017, 121, in press, doi: 10.1021/acs.jpcb.7b08707. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Title | Data to support 'Simulation of protein pulling dynamics on second time scale with boxed molecular dynamics' |
| Description | This data set contains the all the figure and supporting data relating to the paper 'Simulation of protein pulling dynamics on second time scale with boxed molecular dynamics' (doi: 10.1063/5.0059321). Each data set, titled by the PMF used to generate it, is split into subdirectories for the velocities the code was run at (in Angstrom/ns) each of which contain the population, free energy and force outputs for the simulation. The simulations can be created again using the files in the code folder and each arrays2.cm contained in each PFM folder in which it is contained. Compile with ifort compiler for fortran 77. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://archive.researchdata.leeds.ac.uk/894/ |
| Title | LDDS: Python package for computing and visualizing Lagrangian Descriptors for Dynamical Systems |
| Description | Nonlinear dynamical systems are ubiquitous in natural and engineering sciences, such as fluid mechanics, theoretical chemistry, ship dynamics, rigid body dynamics, atomic physics, solid mechanics, condensed matter physics, mathematical biology, oceanography, meteorology and celestial mechanics (Wiggins, 1994 and references therein). There have been many advances in understanding phenomena across these disciplines using the geometric viewpoint of the solutions and the underlying structures in the phase space; for example (MacKay et al., 1984), (V. Rom-Kedar et al., 1990), (Ozorio de Almeida et al., 1990), (V. Rom-Kedar & Wiggins, 1990), (J. D. Meiss, 1992), (Koon et al., 2000), (Waalkens et al., 2005), (J. D. Meiss, 2015), (Wiggins, 2016), (Zhong et al., 2018), (Zhong & Ross, 2020). Chief among these phase space structures are the invariant manifolds that form a barrier between dynamically distinct solutions. In most nonlinear systems, the invariant manifolds are computed using numerical techniques that rely on some form of linearization around equilibrium points followed by continuation and globalization. However, these methods become computationally expensive and challenging when applied to the high-dimensional phase space of vector fields defined analytically, from numerical simulations or experimental data. This points to the need for techniques that can be paired with trajectory calculations, without the excessive computational overhead and at the same time can allow visualization along with trajectory data. The Python package, LDDS, serves this need for analyzing deterministic and stochastic, continuous and discrete high-dimensional nonlinear dynamical systems described either by an analytical vector field or from data obtained from numerical simulations or experiments. To the best of our knowledge, no other software for calculating Lagrangian descriptors exists. A variety of computational tools is available for competing approaches popular in fluid mechanics, such as the identification of Lagrangian coherent structures via finite-time Lyapunov exponents (Briol & d'Ovidio, 2011), (Nelson & Jacobs, 2016), (Onu et al., 2015), (Finn & Apte, 2013), (Dabiri Lab, 2009), (Haller et al., 2020) and finite-size Lyapunov exponents (Briol & d'Ovidio, 2011) or Eulerian coherent structures (Katsanoulis & Haller, 2018). |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| URL | https://zenodo.org/record/5519579 |
| Title | LDDS: Python package for computing and visualizing Lagrangian Descriptors for Dynamical Systems |
| Description | The LDDS software is a Python-based module that provides the user with the capability of analyzing the phase space structures of both continuous and discrete nonlinear dynamical systems in the deterministic and stochastic settings using Lagrangian descriptors (LDs). |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | None yet to report. |
| URL | https://joss.theoj.org/papers/10.21105/joss.03482.pdf |
| Title | UPOsHam: A Python package for computing unstable periodic orbits in two-degree-of-freedom Hamiltonian systems |
| Description | A Python package for computing unstable periodic orbits in two-degree-of-freedom Hamiltonian systems. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | None yet to report. |
| URL | https://joss.theoj.org/papers/10.21105/joss.01684.pdf |