A computational framework for rational materials design and development.
The intellectual focus of this RIMSE Summer School is on understanding how nanoscale building blocks can be assembled into functional, tunable materials that operate at the meso- to macroscales. Such materials are broadly relevant to energy, environmental sustainability, and human health problems. Led by Professor Tod Pascal, the course provides training on the computational aspects of the self-assembly problem—students will learn how to deploy high-performance computing to build predictive models that capture the chemical and physical complexities of mesoscale materials and the dynamics of their formation.  Students will be employ a multiscale theoritical framework to solving these problems, combining first-principles electronic structure calculations, atomistic and coarse-grained (CG) models. The structure and dynamics of these system will be explored by means of molecular dynamics (MD) simulations, while Monte-Carlo (MC) simulations will be used to determine the long-term, thermodynamic behavior. Trainees will learn how to: (1) simulate assembly at atomistic, nanoscopic, and mesostructural scales; and (2) how to predict the properties of the resulting structures.

2023 Applications are now closed. Please visit the website back in Winter 2023 for next year's RIMSE 2024 program details. 

For questions, please contact mrsec@ucsd.edu


2023 SSPA Participants

Crook, Racquel

ORCID: 0009-0007-6385-8986
BIO: Racquel is an enthusiastic high school student (class of 2024) who is training for a future STEM career by studying Calculus, Chemistry and Biomedical Sciences. She is participating in research experiments at the UC San Diego MRSEC RIMSE 2023 Summer School on Predictive Assembly.

Huang, Evan

ORCID: 0009-0006-3759-4692
BIO: Evan is an undergraduate student at UC San Diego majoring in chemistry and works with Dr. Shaowei Li for undergraduate research. Evan is participating in the UC San Diego MRSEC 2023 RIMSE Summer School on Predictive Assembly (SSPA).

John, Doe

John, Doe

2023 SSPA Mentor Participants

Young, James

ORCID:   0000-0002-7250-0966
BIO: James is an undergraduate student at UC San Diego (class of 2025) majoring in Computer Science with a specialization in Bioinformatics. He participated in the Summer School on Predictive Assembly in 2022 and is returning this year as a mentor and to continue his ongoing work as a MRSEC research assistant. James currently works in the Tao Group on the development of inorganic nanoscale materials and focuses on the programmable assembly of gold nanoparticles with binary shape and size. Outside of academics, he practices Chinese martial arts and can be found performing for events with the wushu club at UCSD

John, Doe