Nick Jackson

The Jackson Lab integrates quantum mechanics, statistical mechanics, and machine learning to understand the multiscale structure and electronic properties of soft materials. We are focused on the design of electronic properties in soft materials, including conductive, optical, bioelectronic, electrostatic and degradation behavior.

Projects include:

Electronic Predictions at Coarse-Grained Resolutions 

Molecular quantum mechanics has advanced to the point that high-accuracy quantum mechanical observables may be computed for arbitrary chemical structures. However, to fully account for morphological heterogeneity of soft materials (e.g. organic molecules, polymers), one must simulate system sizes on the order of 10 100 nm. This length scale is only accessible via computationally cheaper classical simulation using reduced resolution (coarse-grained) models – in these models one cannot solve the Schrodinger equation directly because the atomic degrees of freedom have been averaged into effective coarse-grained particles. Our research goal is to develop efficient and accurate electronic prediction models that operate only on coarse-grained degrees of freedom. These models will provide access to optical, electronic, and reactive predictions for soft materials at mesoscopic length scales.

Structure  and Charge Transport in Organic Mixed Conductors 

Semiconducting molecules and polymers combine the mechanical and thermophysical advantages of soft materials with optoelectronic functionality common to inorganic semiconductors (e.g. Silicon, GaAs). In recent years, the application of these materials classes in biological systems as signal transducers at neural interfaces has emerged. To design materials for these systems requires the challenging multiscale prediction of morphology, electronic conductivity, and ionic conductivity. Our research goals are to (i) understand the fundamental interplay between molecular structure, morphological structure, electronic conductivity, and ionic conductivity in biological environments and (ii) design and characterize new bioelectronic materials for use in biomedical applications.

Machine Learning and Data-Driven Soft Materials Design

A primary challenge for soft materials design is the strong coupling of disparate length and timescales that mediates materials behavior; molecular degradation is influenced by macroscopic environment, electronic and ionic transport are controlled by mesoscale morphology, and biological functionality is inherently hierarchical. This confluence of length and time scales requires the integration of an array of theoretical, computational, and data-centric techniques to fully characterize material performance. In the past decade, machine-learning (ML) approaches to molecular modeling have transformed the landscape of materials research and engineering. Soft materials are challenging due to i) the qualitative diversity of soft matter systems and the variety of modeling techniques (quantum chemistry to finite element methods) required, (ii) the importance of entropic effects and competing kinetic/thermodynamic states for a given materials, and (iii) emergent many-body effects that are ubiquitous in soft materials. Our research goal is the creative integration of new machine learning and data-driven techniques in soft materials research at both quantum-mechanical and coarse-grained resolutions.