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.

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.

 

 

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.

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.

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.

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.

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.

Dr. Lars Andersen Bratholm.

CHAMPS Workshop – ‘Chaos Indicators, Phase Space and Chemical Reaction Dynamics’
|  4th – 6th May 2020  |

Organisers | Dr Matthaios Katsanikas, Dr Makrina Agaoglou + Dr Francisco Gonzalez Montoya

*REGISTRATION IS NOW OPEN – please register here*

Schedule of talks
Workshop link – https://bluejeans.com/233350403

Day 1 – Monday 4th May 12pm – 2pm (UK Time)
12.00 – 12.10 Introduction
12.10 – 12.40 R. MacKay – (University of Warwick, Coventry)
12.40 – 13.10 T. Komatsuzaki – (Hokkaido University, Japan)
13.10 – 13.40 S. Keshavamurthy – (Indian Inst of Technology Kanpur, India)

Day 2 – Tuesday 5th May 12pm – 2pm (UK Time)
12.00 – 12.10 Introduction
12.10 – 12.40 A. Mancho – (Instituto de Ciencias Matemáticas, CSIC, Madrid)
12.40 – 13.10 V. J. Garcia Garrido – (Universidad de Alcala, Spain)
13.10 – 13.40 Ch. Skokos – (University of Cape Town, South Africa)

Day 3 – Wednesday 6th May 12pm – 2pm (UK Time)
12.00 – 12.10 Introduction
12.10 – 12.40 S. Farantos – (University of Crete, Greece)
12.40 – 13.10 P. Patsis – (RCAAM of Academy of Athens, Greece)
13.10 – 13.40 P. Cincotta – (Universidad Nacional de La Plata, Argentina)

Schedule + Title of talks PDF

Symposium – Title: Crowd-sourcing Machine Learning in NMR

Following on from the success of the Kaggle competition that Champs PDRA Lars Anderson Bratholm ran last year he is now organising a Symposium to be held on Thursday 5th March 2020.

Title, abstract and schedule below (subject to change):

Crowd-sourcing Machine Learning in NMR

Abstract:
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, we 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. Over 3 months, we received 47,800 ML model submissions from 2700 teams in 84 countries, with the top models outperforming our own previously published methods. The success this strategy has cultivated highlights the potential of crowd-sourced ML approaches across a range of scientific domains.

This symposium will introduce the background and main findings of the competition, including the context of computational NMR, the Kaggle platform and presentations from the top performing teams of the competition.

Location: Lecture Theater 2.41, Fry Building, Bristol BS8 1TH

Schedule – Thursday 5th March:

10:00 – 11:15: Registration
11:15 – 11:50: Craig Butts and Will Gerrard
11:50 – 12:25: Addison Howard and Walter Reade
12:25 – 13:15: Lunch
13:15 – 13:50: Lars Andersen Bratholm
13:50 – 14:25: Brandon Anderson
14:25 – 14:40: Break
14:40 – 15:15: Luka Stojanovic and Milos Popovic
15:15 – 15:50: Youhan Lee and Sunghwan Choi
15:50 – 16:05: Break
16:05 – 16:40: Andres Torrubia
16:40 – 17:15: Devin Wilmott
17:15 – 18:15: Drinks reception
An Eventbrite registration, link below, has been set up. Numbers are limited (due to lecture theatre capacity) and will be allocated on a first come, first served basis.  If you wish to attend please can you complete the form so we can plan for numbers and catering on the day.

Eventbrite Registration Form

CHAMPS Book Sprint

On December 6-9, 2019, CHAMPS PDRAs Makrina Agaoglou, Broncio Aguilar Sanjuan, Rafael Garcia-Meseguer, Francisco Gonzalez-Montoya, Matthaios Katsanikas, Vladimir Krajnak, Shibabrat Naik, PI Stephen Wiggins, and collaborator Victor Jose Garcia-Garrido of the Universidad de Alcala in Spain held a book sprint.

A book sprint is a method of creating a book collaboratively in a short period of time. The book sprint was held at Engineers House in Bristol which afforded an atmosphere promoting intensive collaboration with minimal distractions. The book is an account of our results, approach, and future directions on the phase space approach to chemical reaction dynamics. The book was produced using Jupyter Book and is available at www.chemicalreactions.io. The result is the book entitled “Chemical Reactions: A Journey into Phase Space”. It is our intention that the book will appeal both to mathematicians and to chemists and will serve as an entry into the field. Indeed, the GitHub framework provides a mechanism for scientific collaboration and we invite the interested scientific community to contribute to the further development of the book. Instructions for this are on the GitHub site for the book (https://github.com/champsproject/chem_react_dyn).

On Wednesday the 18th of September a joint CHAMPS and Leeds Physical Chemistry seminar “Quantum Trajectories in Phase Space” took place in Leeds . The talk was given by Prof Craig Martens from University of California, Irvine. Although it is a physical/computational chemistry seminar it was of general interest, as it was focused on unusual formulation of quantum mechanics. We almost always think about quantum mechanics in terms of wave function and waves of probability, but there are other ways of looking at it. But Prof Martens presented one an approach, in which quantum mechanics is formulated very similar to classical mechanics, with the difference that neighbouring classical trajectories are not independent. They are entangled and push each other in a very specific way, as if there were many parallel worlds interacting with each other. The talk was attended not only by physicist, mathematicians and computational chemists, but also by a number of experimental organic and inorganic chemists.

 

University of Leeds Physical Chemistry seminar “Quantum Trajectories in Phase Space” by Prof. Craig Martens from University of California Irvine Wednesday 18th September 2019.

https://physicalsciences.leeds.ac.uk/events/event/4/school-of-chemistry/911/quantum-trajectories-in-phase-space

Although it is a physical/computational chemistry seminar it can be of general interest, as it will be about an unusual formulation of quantum mechanics. We almost always think about quantum mechanics in terms of wave function and waves of probability, but there are other ways of looking at it. Craig Martens will present one of those approaches, in which quantum mechanics is just like classical, with the difference that neighbouring classical trajectories are not independent. They are entangled and push each other in a very specific way, a bit like there were many parallel worlds interacting with each other.