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IRG 1. Designing and Controlling Phase Transitions in Functional Molecular Materials

Lead: Boris Kozinsky
Co-Lead: Jarad Mason

Primary Faculty Participants: Michael P. Brenner, Richard Y. Liu, Vinothan Manoharan, Frans Spaepen, Joost J. Vlassak, David A. Weitz

Three collaborative Interdisciplinary Research Groups: ML molecular design & simulation, functional molecular materials, and automated experimentation
Three collaborative Interdisciplinary Research Groups: ML molecular design & simulation, functional molecular materials, and automated experimentation
Figure 1. IRG 1 establishes new principles for the design of functional molecular and supramolecular materials via a multimodal approach to elucidate its' complex phase transitions.


This IRG is aimed at establishing new principles for the design of functional molecular and supramolecular materials via a multimodal approach that elucidates their complex phase transitions in three classes of functional materials will be studied: barocalorics, metal-organic frameworks (MOF), and proteins.

To carry out this IRG research, we bring together a multidisciplinary research team composed of 8 faculty from applied mathematics, chemistry, materials science, and physics with deep expertise in machine learning/computation (Brenner, Kozinsky), materials synthesis (Mason, R. Liu), droplet microfluidics (Weitz), and nanoscale characterization (Vlassak, Mason, Manoharan, Spaepen) focused on three intertwined themes (Figure 1).

  1. Machine learning models for phase transitions and molecular design will be established to elucidate complex phase transitions and guide the design of functional molecular and supramolecular materials.
  2. Automated experimentation platforms will be developed that integrates high throughput, droplet microfluidics with combinatorial nano-calorimetry to precisely assemble and characterize the phase transitions of molecular and supramolecular materials.
  3. Functional molecular and supramolecular materials will be synthesized with controlled phase behavior guided by inverse design and optimized for targeted applications.