KITS / IoP / CECAM Workshop & Discussion Meeting
Beijing, August 57 2018
Date: July 29  Aug. 5, 2018
Venue: Rm. S102, Kavli ITS, UCAS [View Map]
Organizers
Daan Frenkel, University of Cambridge, UK
Ignacio Pagonabarraga Mora, CECAM, Switzerland
Rudi Podgornik, UCAS CAS, Beijing
Jure Dobnikar, IoP CAS Beijing
Description
The link between entropy and heat is one of the cornerstones of thermodynamics. Yet, thermodynamics provides no intuitive physical picture of entropy. In contrast, Statistical Mechanics provides a framework that highlights the relation between entropy and probability: it provides an unambiguous microscopic interpretation of entropy, although not necessarily one that is intuitively simple. The probabilistic framework of Statistical Mechanics can also be used to describe systems that are not in equilibrium, nor even material systems. For this reason, the term `entropy’ has spread beyond `BoltzmannGibbs’ statistical mechanics. Examples of such nonthermal entropies are Information Entropy and Granular Entropy. However, in the absence of a link between entropy and heat, different definitions of these entropies may be inequivalent and there is no a priori criterion to decide which ones (if any) are preferable.
In general, the `predictability’ of a system or, more precisely the minimal information that is needed to characterize it, is a measure for the (negative of the) `information entropy’. Interestingly, the information entropy of manybody systems appears to provide a good estimate of the thermal entropy (at least in systems where this comparison could be made). As this entropy can also be computed for nonthermal systems, this suggests that we now have another quantity (in addition to the probabilistic Gibbs entropy) that works in equilibrium but is also defined for nonthermal systems. But what does this entropy mean?
Moreover, other entropy definitions exist that are also computable, such as the `granular’ entropy and the pair entropy that is directly related to the structure of the system under consideration (provided that an appropriate metric exists). In addition, there are physical properties (such as e.g. structural hyperuniformity) that seem to correlate with at least some of the entropy definitions. Other information measures may exist, and we will explore to what extent machine learning can be used to identify such entropy descriptors.
The aim of the discussion meeting is to assess the current stateoftheart in this (still fragmented) field and identify possible fruitful topics for a fullscale workshop.
Schedule (Booklet Download)
DATE  ACTIVITY  LECTURER  
Sunday July 29^{th} 
AM 
ARRIVAL / REGISTRATION Python & Programming Basics 
James Farrell (IoP) 
PM  
Monday July 30^{th} 
AM 
Introduction to Statistical Thermodynamics; Basic Simulation Techniques & Ensembles Monte Carlo, Parallel Tempering 
Daan Frenkel (U. Cambridge) 
PM 
Molecular Dynamics Exercise Class 

Tuesday July 31^{st} 
AM 
Linear response theory, diffusion… Computing observables: pressure, radial distribution function Other ensembles 
Daan Frenkel (U. Cambridge) 
PM  Exercise Classes  
Wednesday August 1^{st} 
AM 
Free energy calculations: Thermodynamic integration, Umbrella sampling, acceptance ratio, Widom insertion Phase coexistence 
Daan Frenkel (U. Cambridge) 
PM  Exercise Classes  
Thursday August 2^{nd} 
AM  Advanced MD: Constraints, Rare events  Erik Luijten (Northwestern U.) 
PM 
Modelling long range interactions: Electrostatics Exercise Classes 

Friday August 3^{rd} 
AM 
Mesoscopic modelling of fluid flow Dissipative Particle Dynamics, Multi Particle Collision Dynamics 
Ignacio Pagonabarraga (CECAM)
Mincheng Yang (IoP) 
PM 
Lattice Boltzmann Exercise Classes 

Saturday August 4^{th} 
Machine learning Exercise class: big data set, extract correlations; train simple neuron net 
Alpha Lee (U. Cambridge) 

Sunday August 5^{th} 
SOCIAL PROGRAM / DEPARTURE 
Invited participants
Roy BeckBarkai
Alpha Lee
Erik Luijten
Sri Sastry
Ali Naji
Limei Xu
Masao Doi
Rafi Blumenfeld
Ke Chen
Jiajia Zhou
Mincheng Yang
Xianren Zhang