Mini symposium at BMC-BAMC 2021 – Vladimir Krajnak, Shibabrat Naik.

BMC-BAMC Glasgow 2021

We organized a mini-symposium on the theme of “Integrating dynamical systems with data driven methods” (MS09). The invited speakers presented theory and applications of data driven methods in fluid mechanics and chemical reactions. The mini-symposium had the following four talks. The program for Day 1 along with the scheduled talks is available here

[LINK TO THE PDF OF DAY 1 PROGRAMME].

Coming soon: the recorded talks.

Peter Ashwin (Exeter)

Plant ER networks and the dynamics of anchored 2D foams subject to viscous flow

The Endoplasmic Reticulum in plant cells can form a variety of rapidly changing structures including networks of filaments that are anchored to the cell membrane at various points. We discuss progress in biophysical modelling of the interaction of these geometric networks with other processes in play within the cell, in particular actin-driven cross-connections and viscous flow associated with cytoplasmic streaming. We show these processes can maintain an anchored 2D foam of filaments and, maybe more surprisingly the foam retains memory of past streaming speed and direction. (Joint work with Congping Lin, Wuhan).

Stefan Klus (Surrey)

Kernel methods for detecting coherent structures

Over the last years, numerical methods for the analysis of large data sets have gained a lot of attention. Recently, different purely data-driven methods have been proposed which enable the user to extract relevant information about the global behavior of the underlying dynamical system, to identify low-order dynamics, and to compute finite-dimensional approximations of transfer operators associated with the system. However, due to the curse of dimensionality, analyzing high-dimensional systems is often infeasible using conventional methods since the amount of memory required to compute and store the results grows exponentially with the size of the system. We extend transfer operator theory to reproducing kernel Hilbert spaces and show that these operators are related to Hilbert space representations of conditional distributions, known as conditional mean embeddings in the machine learning community. One main benefit of the presented kernel-based approaches is that these methods can be applied to any domain where a similarity measure given by a kernel is available. We illustrate the results with the aid of guiding examples and highlight potential applications in molecular dynamics, fluid dynamics, and quantum mechanics.

Kamyar Azizzadenesheli (Purdue)

A Crash Course on Neural Operators

Neural Operators are a new advancement in machine learning, applied mathematics, and science, that allows for efficiently learning operators from infinite-dimensional spaces, e.g. function spaces. In this talk, we cover the basics of Neural Operators, their properties, architectures, computation powers, limitations, and the theory behind them. We concluded the talk with a few empirical study partial differential equations (PDEs) to elaborate on the broad applicability of these methods.

Cecilia Clementi (Freie Universität Berlin)

Designing molecular models by machine learning and experimental data

The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.

Congratulations to new Champs PDRA Dr Grace Chuang – 2021 Chemistry National Meeting in Taiwan prize winner.

Congratulations to new Champs PDRA Dr Grace Chuang on being a prize winner at the 2021 Chemistry National Meeting in Taiwan. Grace was part of the physical chemistry group and passed the first and second evaluations to get the certification and a prize of £250.

2021化學年會重要時程 研究論文獎(研究生學位論文,大專生專題論文獎)稿截止日:2021 年 01 月 04 日當晚12點前為上傳最後期限Important Dates Online application dea 2021化學年會 2021化學年會 化學年會 2021中國化學年會 2021年化學年會 中央大學化學系 中國化學年會 化學 年會 化學年會地點 化學年會報名 化學年會投稿 …www.2021csnm.tw

Nonadiabatic Excited-State Molecular Dynamics Methodologies: Comparison and Convergence – A perspective article published by JPC Letters

https://doi.org/10.1021/acs.jpclett.1c00266

A perspective article has been published by JPC Letters which reports implementation of the AIMC method in the Los Alamos NEXMD software used for simulation of energy transfer in organic molecules excited by light. The work was done by an international group of scientists from US, Argentina and UK but the method was originally developed by Dmitry Makhov and Dmitry Shalashilin at the University of Leeds, and was supported largely by the CHAMPS project.  The method is based on the idea of using phase space in quantum mechanics  and the role  of CHAMPS  was invaluable.

The paper also made it onto the journal cover:

Champs Project – Summer Students 2020

The Champs team have had a very successful summer with a number of students working closely with the Champs PI and several PDRAs on projects.

Below is a list of names of those who worked on the project over the summer and the titles of their project, a few of which have written papers.

Prof. Stephen Wiggins

Student name – Cate Mandell

Title of Project  – “The Role of Time Dependent Phase Space Structures on Reaction Dynamics and the No-Recrossing Property of Dividing Surfaces”

She wrote a paper on her research that has been accepted in the “International Journal of Bifurcation and Chaos”.

Student name – Zara Graham-Jones and Michael Turner, they worked together on the following project

Title of Project  – “Lagrangian Descriptors and Bohmian Mechanics” they have written a paper on their project

Dr Shibabrat Naik + Prof. Stephen Wiggins co-advised the following students and their respective topics:

Computation and visualization of phase space structures relevant for chemical reaction dynamics

Student name: Sam Spedding

Title: Two Degree of Freedom Hamiltonian Systems in Chemical Reaction Dynamics – A Phase Space Analysis

Student name: Bing En Gan

Title: Computation and visualization of phase space structures in Reaction Dynamics

Student name: Piero Sarti (LMS summer research award)

Title: Visualizing the 2-Well and 3-Well Deleon-Berne Hamiltonian

Student name: Annie Hu

Title: Poincaré sections, gap times, and directional flux in De Leon-Berne Hamiltonian 

Student name: Wenyang Lyu

Title: Classical-quantum correspondence in the saddle-node Hamiltonian

Published paper based on work over winter and spring term: https://link.springer.com/article/10.1134%2FS1560354720050044

There are manuscripts in preparation based on the individual summer project reports.

Dr. Vladimir Krajnak

Student name – Mark Pearson

Title of Project – Phase space structures in the ‘Helium atom’

Dr Francisco Gonzalez Montoya

Student name –  Charlotte Quant

Title of Project  –  The Quantum Inverted Oscillator

Name – Edward Sharp

Title of Project  –  The Tunnel Effect and the Anderson Localization

Dr Makrina Agaoglou + Dr Matthaios Katsanikas

Student name – Yibin Geng.

Title of Project  –  “The dynamical matching as a bifurcation effect in caldera potential energy surfaces”

Student name – Rebecca Crossley

Title of Project  –  “From Poincare Maps to Lagrangian Descriptors: The case of the VRI potential”

Student name – Douglas Haigh

Title of Project  –  “The time evolution of the selectivity in a symmetric potential energy surface with a post-transition-state bifurcation”

The plan is to submit their drafts in scientific journals in the near future. Some of the titles might change for the submission.

3D printed Ozone PES – Role of 3D printing technology in dynamical systems applications

Role of 3D printing technology in dynamical systems applications

Shibabrat Naik, Stephen Wiggins

Understanding chemical reactions using dynamical systems theory is based on using the geometric view of the structures underlying the solutions of the equations of motion. The equations are derived from the potential energy due to the molecular configurations involved in the reaction. In both of these aspects, that is the geometry of the potential energy surfaces and the phase space structures, visualization in the real space of the physical world using 3D printing facility is increasing the educational and hands-on learning experience. In our research project supported by CHAMPS (EPSRC Grant No. EP/P021123/1) we used the 3D-printing facility in the School of Mathematics for prototyping a potential energy surface in the dissociation of ozone available in the literature (dx.doi.org/10.1021/ed500683g). This potential energy function at constant total energy is an 3D volume specified by a function obtained from fitting electronic energies and can be used in dynamical systems analysis of the dissociation.


The image on the left shows the generated prototype using the 3D printer and on the right is the image in the virtual space generated using the Sketchfab Labs (https://sketchfab.com/3d-models/potential-energy-in-dissociation-of-ozone-59a7e742edea4c1bba11ff3123c0312e). The hands-on experience of a 3D prototype makes the geometric methods used in phase space perspective of chemical reaction dynamics accessible to a wider audience. In addition, we are planning to use the 3D printing facility to build some of the many mathematical objects called phase space structures that come out of dynamical systems analysis of chemical reactions. This will be useful for educational and conference presentation purposes.

CHAMPS – Jupyter Book, “Lagrangian Descriptors: Discovery and Quantification of Phase Space Structure and Transport”

The CHAMPS (Chemistry and Mathematics in Phase Space) research group is pleased to announce the availability of our latest Jupyter Book, “Lagrangian Descriptors: Discovery and Quantification of Phase Space Structure and Transport” available at
https://champsproject.github.io/lagrangian_descriptors. This book is a companion to our recent Jupyter Book, Chemical Reactions: A Journey into Phase Space, available at http://www.chemicalreactions.io.
Our new Jupyter book contains substantial “computable content” in the form of Jupyter notebooks for computing the Lagrangian descriptors for a variety of dynamical systems.
We view both of our Jupyter books as community resources and we invite all those interested to participate in their further development through GitHub.

LD book sprint group photo
CHAMPS Project maths cohort L-R: Makrina Agaoglou, Francisco González-Montoya, Broncio Aguilar-Sanjuan, Vladimír Krajňák, Shibabrat Naik, Víctor José García Garrido, Matthaios Katsanikas, Stephen Wiggins.

 

 

Congratulations Dr Katsanikas!

Champs is thrilled to announce that Dr Matthaios Katsanikas, Champs PDRA, has been  elected as researcher C (assistant professor level) in Dynamical Astronomy at Research Center for Astronomy and Applied Mathematics (RCAAM) of Academy of Athens.

Photo manthos9
M. Katsanikas | Bristol

The Academy of Athens (Greek: Ακαδημία Αθηνών, Akadimía Athinón) is Greece’s national academy, and the highest research establishment in the country. It was established in 1926, and operates under the supervision of the Ministry of Education. The Academy’s main building is one of the major landmarks of Athens.

Dr Katsanikas duties in his new role will be the research and the supervision of research programs, postdoctoral researchers and Master and PhD theses and the teaching at postgraduate programs. He expects to take up this position in early 2021.

Dr Katsanikas joined the Champs project in 2017, one of our first PDRA cohort, based in Bristol under Professor Wiggins supervision. He has made great progress during his time working on the project, so this next step in his career is well deserved and recognition of what an excellent researcher he is.

We wish Dr Katsanikas all the best for the future.

Event report: Symposium – ‘Crowd-sourcing Machine Learning in NMR’ Thursday 5th Mar 2020.

The rise of machine learning (ML) has led to an explosion in potential strategies which may be used to learn from data in order to make scientific predictions. For physical scientists who wish to apply ML strategies to a particular domain, the vast number of strategies available has made it difficult to make an a priori assessment of what strategy to adopt. This is further complicated when similar domains have not been previously explored in the literature.

Recently, CHAMPS PDRA Dr. Lars Andersen Bratholm and collaborators worked with Kaggle to design a competition which encouraged data scientists around the world to develop ML models for predicting pairwise nuclear magnetic resonance (NMR) properties for synthetically relevant chemical compounds.
The success this strategy has cultivated highlights the potential of crowd-sourced ML approaches across a range of scientific domains and the CHAMPS symposium “Crowd-sourcing machine learning in NMR” took place on the 5th of March to communicate this message to researchers in the Bristol area.

IMG_8323
The symposium started off with Professor Craig butts and PhD student Will Gerrard from the University of Bristol presenting how modelling of NMR properties with DFT and ML can accelerate the drug design process. This was followed by Addison Howard from Kaggle introducing the Kaggle platform, background to the company and interesting findings from the CHAMPS competition. The early non-technical session ended with Lars Bratholm conveying the main findings of the competition from the organizers point of view, namely what can be gained from combining all the different approaches, and how the collaborative environment of Kaggle is something that we can learn from in academia.

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The afternoon session turned more technical and featured representatives from six of the top performing teams who all presented their approaches to the competition. The talks of Brandon Anderson, Luka Stojanovic, Milos Popovic, Sunghwan Choi, Andres Torrubia and Devin Wilmott all spurred great discussion with audience, which continued the following day with the competition organizers.

IMG_8321

Dr. Lars Andersen Bratholm.