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BEGIN:VEVENT
DTSTAMP:20260506T111557Z
DTSTART;VALUE=DATE-TIME:20230206T130000
DTEND;VALUE=DATE-TIME:20230206T140000
SUMMARY:WCPM: Marjolien Dijkstra (Utrecht University)
TZID:Europe/London
UID:20230206-8a17841b85589381018577880ec808b3@warwick.ac.uk
CREATED:20230118T092900Z
DESCRIPTION:Abstract Predicting the emergent properties of a material fro
 m a microscopic description is a scientific challenge. Machine learning 
 and reverse-engineering have opened new paradigms in the understanding a
 nd design of materials. However\, this approach for the design of soft m
 aterials is highly non-trivial. The main difficulty stems from the impor
 tance of entropy\, the ubiquity of multi-scale and many-body interaction
 s\, and the prevalence of non-equilibrium and active matter systems. The
  abundance of exotic soft-matter phases with (partial) orientation and p
 ositional order like liquid crystals\, quasicrystals\, plastic crystals\
 , along with the omnipresent thermal noise\, makes the classification of
  these states of matter using ML tools highly non-trivial. In this talk\
 , I will address questions like: Can we use machine learning to autonomo
 usly identify local structures [1]\, detect phase transitions\, classify
  phases and find the corresponding order parameters [2] in soft-matter s
 ystems\, can we identify the kinetic pathways for phase transformations 
 [1]\, and can we use machine learning to coarse-grain our models? [3\,4]
  Finally\, I will show how one can use machine learning to reverse-engin
 eer the particle interactions to stabilize natureā€™s impossible phase of 
 matter\, namely quasicrystals? [5] [1] An artificial neural network reve
 als the nucleation mechanism of a binary colloidal AB13 crystal G.M. Col
 i and M. Dijkstra\, ASC Nano 15\, 4335-4346 (2021). [2] Classifying crys
 tals of rounded tetrahedra and determining their order parameters using 
 dimensionality reductionLink opens in a new window R. van Damme\, G.M. C
 oli\, R. van Roij\, and M. Dijkstra\, ACS Nano 14\, 15144-15153 (2020). 
 [3] Machine learning many-body potentials for colloidal systems G. Campo
 s-Villalobos\, E. Boattini\, L. Filion and M. Dijkstra\, The Journal of 
 Chemical Physics 155 (17)\, 174902 (2021). [4] Machine-learning effectiv
 e many-body potentials for anisotropic particles using orientation-depen
 dent symmetry functions G. Campos-Villalobos\, G. Giunta\, S. MarĆ­n-Agui
 lar and M. Dijkstra\, The Journal of Chemical Physics 157 (2)\, 024902 (
 2022). [5] Inverse design of soft materials via a deep learningā€“based ev
 olutionary strategy G.M. Coli\, E. Boattini\, L. Filion\, and M. Dijkstr
 a\, Science Advances 8 (3)\, eabj6731 (2022). Marjolein Dijkstra is full
  professor (2007) in the Debye Institute for Nanomaterials Science at Ut
 recht University. She received an MSc degree in Molecular Sciences at Wa
 geningen University as well as an MSc degree in physics at Utrecht Unive
 rsity. She obtained her PhD degree from Utrecht University in 1994 under
  the supervision of Daan Frenkel\, and was awarded twice a prestigious E
 U Marie Curie Individual Fellowship to join the Physical and Theoretical
  Chemistry group at Oxford University and the H.H. Wills Physics Laborat
 ory at Bristol University. She was a research associate at Shell Researc
 h in Amsterdam in 1995. In 1999\, she started her own research group at 
 Utrecht University\, focused on obtaining fundamental understanding on t
 he self-assembly behavior of soft materials\, and how the self-assembly 
 process can be manipulated by external fields such as gravity\, template
 s\, air-liquid or liquid-liquid interfaces\, and electric fields. Her gr
 oup employs theory\, computer simulations\, and machine learning to stud
 y physical phenomena in soft-matter systems like self-assembly in colloi
 dal dispersions (crystals\, quasicrystals\, and exotic liquid crystals o
 f odd-shaped particles)\, glass and jamming transitions\, active matter\
 , crystal nucleation\, and inverse design of new soft materials. She is 
 recipient of the Minerva Prize (2000)\, a high-potential grant (2004)\, 
 a prestigious NWO VICI and Aspasia grant (2006)\, and an ERC advanced gr
 ant (2020)\, and is elected as member of the Royal Netherlands Academy o
 f Arts and Sciences (KNAW) in 2020. Join online
LOCATION:A2.05/ Teams
URL:/fac/sci/wcpm/seminars/?calendarItem=8a17841a852
 b469b0185351a645e23b0
ATTACH:/fac/sci/wcpm/seminars/?calendarItem=8a17841a
 852b469b0185351a645e23b0
CATEGORIES:WCPM
LAST-MODIFIED:20230118T092900Z
ORGANIZER;CN=Eren Delaney:
END:VEVENT
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