Professor Jackson obtained his B.A. in Physics from Wesleyan University in 2011. He then joined the groups of Prof. Mark Ratner and Prof. Lin Chen at Northwestern University as a NSF GRFP and Northwestern Presidential Fellow. In 2016 he received his Ph.D. in Theoretical and Computational Chemistry studying optoelectronic processes in semiconducting polymers and small molecules. He then began a post-doc with Prof. Juan de Pablo at the University of Chicago, and subsequently accepted a Maria Goeppert Mayer Fellowship at Argonne National Laboratory. In 2019 he was promoted to an Assistant Scientist in the Materials Science Division at Argonne. Professor Jackson will be joining the faculty at UIUC in Spring 2021.
- Theoretical soft materials chemistry, electron and ion transport, machine learning applied to molecular and polymeric systems, multiscale all-atom and coarse-grained simulations
The Jackson Lab’s mission is the integration of molecular quantum mechanics, computational statistical mechanics, and machine learning to solve multiscale morphological and electronic prediction tasks. We are primarily focused on the design of advanced molecular and polymeric materials that exhibit optoelectronic, bioelectronic, and charge conductive functionalities.
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 the mesoscopic length scales relevant to real devices.
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 morphological structure, 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, in collaboration with experimentalists, 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.