Predictive Deconstruction

view profile for Kenneth Sale

Kenneth Sale

Director of Predictive Deconstruction

view profile for Seema Singh

Seema Singh

Director of Biomass Pretreatment and Process Development and Deputy VP of Deconstruction

view profile for John Gladden

John Gladden

Director of POPI

The landscape of potential ionic liquids (IL) and deep eutectic solvents (DES) available for biomass pretreatment and deconstruction approaches is vast and when combined with the large variety of potential process configurations (e.g., one-pot, lignin-first, lignin-last) experimentally investigating the space of possibilities is inaccessible. The ability to predict the efficacy of a proposed new integrated IL-based deconstruction process will dramatically expand the landscape of ILs that can be investigated for biomass pretreatment by conducting the bulk of the initial IL screening computationally. Predictive models also open the possibility to go beyond simple IL compatibility into engineering conversion technologies where models of monosaccharide and lignin fragment yields as a function of properties of ILs and enzymes and of pretreatment conditions are used to identify combinations of ILs, enzymes and process conditions that maximize product yields that are enabled by the JBEI Feedstocks-to-Fuels Pipeline.


Research in predictive deconstruction is aimed at developing fundamental understandings of how ILs and DESs interact with biomass to promote dissolution of cellulose, hemicellulose, and lignin and to use this information to predict how new ILs or DESs will perform under different pretreatment process scenarios. An additional focus is understanding how ILs and DESs interact with enzymes and other biological entities where it is important to understand and mitigate how the solvent system might negatively impact enzyme activity or microbial growth and productivity. Ultimately, we would like to use the framework to design new biomass and pretreatment process specific ILs and DESs.

Atomistic simulations of biomass-solvent systems

We use molecular dynamics simulations and quantum mechanics calculations such as COnductor like Screening MOdel for Real Solvents (COSMO-RS) to both explain how ILs and DES solubilize biomass and to the calculate properties of ionic liquids correlated with efficient biomass deconstruction such as solubility parameters.

Machine learning models

Machine learning models of biomass deconstruction using IL and DES solvent systems will be constructed using features of the solvent system extracted from atomistic simulations, features extracted from biomass characterization (e.g., composition, FT-IR, NMR) and data on experimental conditions. Hypotheses such as whether a particular solvent system is agnostic to the feedstock phenotype will be tested by comparing models generated using different subsets of features (e.g., models including feedstock phenotype differences vs models excluding biomass phenotype differences). Predictive models are validated using JBEI’s feedstock to fuels pipeline, which will also be used to generate data designed to improve existing models.

Featured Publications

Ionic Liquid Databases