Statistical Thermodynamics & Molecular Simulations (STMS) Seminar Series: Prof. Marjolein Dijkstra (Utrecht University)
These seminar series are aimed at providing a virtual platform for sharing scientific research in the area of statistical mechanics, molecular simulations, and computational materials science. Since early 2020, the coronavirus pandemic has disrupted many large in-person scientific gatherings, including conferences and department seminars. STMS is aimed at filling this gap, and provide a venue for dissemination of research findings and exchange of ideas in the age of COVID. This model is being currently used by several other scientific communities, and can potentially continue even beyond the pandemic if successful.
Each seminar will be a 60-minute event and will comprise of a long-form (30-minute) talk by a principal investigator or a senior research scientists from academia or industry and a short-form (15-minute) presentation by a graduate student or a postdoc. The remainder of the event will be dedicated to Q&A (10 minutes for the PI, 5 minutes for the student/postdoc). Long-form speakers will be chosen by the STMS Organizing Committee, while we encourage suggestions from the community at large. Student and postdoctoral speakers can either be nominated by their advisors or can self-nominate themselves by sending a CV to the organizers. During 2022 we expect to hold two seminar per month, and the events will take place in the last two Fridays of each month, from 10:45 AM-12:00 PM Eastern Standard Time (EST):
This event's talks:
Machine learning and Inverse design of soft materials
Prof. Marjolein Dijkstra (Utrecht University)
Abstract: Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and design of materials. However, this approach for the design of soft materials is highly non-trivial. The main difficulty stems from the importance of entropy, the ubiquity of multi-scale and many-body interactions, and the prevalence of non-equilibrium and active matter systems. The abundance of exotic soft-matter phases with (partial) orientation and positional 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 autonomously identify local structures [1,2], detect phase transitions [1], classify phases and find the corresponding order parameters [2], 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 in this lecture how one can use machine learning to reverse-engineer the particle interactions to stabilize nature’s impossible phase of matter, namely quasicrystals [5]?
[1] An artificial neural network reveals the nucleation mechanism of a binary colloidal AB13 crystal, G.M. Coli and M. Dijkstra, ASC Nano 15, 4335-4346 (2021).
[2] Classifying crystals of rounded tetrahedra and determining their order parameters using dimensionality reduction, R. van Damme, G.M. Coli, R. van Roij, and M. Dijkstra, ACS Nano 14, 15144-15153 (2020).
[3] Machine learning many-body potentials for colloidal systems, G. Campos-Villalobos, E. Boattini, L. Filion and M. Dijkstra, The Journal of Chemical Physics 155 (17), 174902 (2021).
[4] Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions, G. Campos-Villalobos, G. Giunta, S. Marín-Aguilar and M. Dijkstra, The Journal of Chemical Physics 157 (2), 024902 (2022).
[5]Inverse design of soft materials via a deep learning–based evolutionary strategy,G.M. Coli, E. Boattini, L. Filion, and M. Dijkstra, Science Advances 8 (3), eabj6731 (2022).
Speaker Bio: Marjolein Dijkstra is full professor (2007) in the Debye Institute for Nanomaterials Science at Utrecht University. She received an MSc degree in Molecular Sciences at Wageningen University as well as an MSc degree in Physics at Utrecht University. She obtained her PhD degree from Utrecht University in 1994 under the supervision of Daan Frenkel, and was awarded twice a prestigious EU Marie Curie Individual Fellowship to join the Physical and Theoretical Chemistry group at Oxford University to work with Paul Madden and Jean-Pierre Hansen and the H.H. Wills Physics Laboratory at Bristol University to work with Bob Evans. She was a research associate at Shell Research in Amsterdam in 1995. In 1999, she started her own research group at Utrecht University, focused on obtaining fundamental understanding on the self-assembly behavior of soft materials, and how the self-assembly process can be manipulated by external fields such as gravity, templates, air-liquid or liquid-liquid interfaces, and electric fields. Her group employs theory, computer simulations, and machine learning to study physical phenomena in soft-matter systems like self-assembly in colloidal dispersions (crystals, quasicrystals, and exotic liquid crystals of odd-shaped particles), glasses 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, Aspasia grant (2006), and an ERC advanced grant (2020), and is elected as member of the Royal Netherlands Academy of Arts and Sciences (KNAW) in 2020.
Our student/postdoc talk was scheduled to be given by Dr. Rahul Misra from MIT. Unfortunately, were were asked to postpone it at the last minute due to a family emergency.