Our group’s current research interests include:

Accelerated Functional Materials Discovery

A central goal of our research is to develop new methods for accelerating the discovery of new advanced functional materials. Our current application focus areas are optoelectronic and energy storage materials. From a methodological point of view, we are focusing on modernizing methods for evaluating uncertainty in predictions, developing machine learning models to rapidly explore chemical spaces containing extremely large numbers of molecules (e.g., so large that a library is unwritable on current infrastructure), and quickly discovering the frontiers of “readily-computable” and “easily-synthesizable” chemical space. The last area requires close collaboration with synthetic chemists and materials scientists and employing a modular approach for our chemical space construction.

Recently, we have developed a reinforcement learning scheme that integrates with quantum chemistry calculations and shown that this framework can be used in “inverse design” cases. In other words, we can generate a diverse set of molecules with specified electronic properties without having to do high-throughput brute-force screening. The example below is for finding molecules with excited state triplets that are half of their first excited state singlet. Earlier regions are the dark dots, with later iterations in yellow, showing that the framework is learning to target high-value parts of chemical space.

Group Members Working in this Area
Cheng-Han Li
Daniel Doria
Katie Felde
Hayden Moran
Vijay Sundar

Recent Publications Connected to this Area
1. C.-H. Li and D.P. Tabor,* Generative Organic Electronic Molecular Design Informed by Quantum Chemistry. Chem. Sci. 14, 11045 (2023). ChemRxiv Preprint Available (2023). GitHub Repository.
2. J. Li, B.-J. Peng, S. Li, D.P. Tabor, L. Fang,* and C.M. Schroeder,* Ladder-type conjugated molecules as robust multi-state single-molecule switches, Chem, in press.
3. J. Lee, S. Li, X. Ji, S. Che, Y. Cao, D.P Tabor,* and L. Fang,* Molecular Mechanism of Rigidity- and Planarity-Promoted, State-Dependent Doping of Conjugated Ladder-Type Molecules, Mater. Chem. Front. 6, 3329-3337 (2022).
4. G. Ma, M. Leng, S. Li, Z. Cao, Y. Cao, D.P. Tabor,* L. Fang,* and X. Gu,* Robust Chain Aggregation of Low-Entropy Rigid Ladder Polymer in Solution, J. Mater. Chem. C 10, 13896-13904 (2022).

Developing Representations and Models for Proteins
In collaboration with the Mittal Group, we have recently developed a new representation for intrinsically disordered proteins, called the “Bag of Amino Acid Interactions” representation (BAAI), which weights the interactions in a sequence by type. BAAI is compatible with both classical and deep learning models and, with training, can be leveraged to predict the polymer physics of intrinsically disordered proteins directly from their sequence (saving simulation costs).




This representation draws inspiration from “size insensitive” representations that are often useful in the physical sciences, as we do not need to know in advance what the longest sequence will be.

Group Members Working in this Area
Tzu-Hsuan Chao
David Chi
Curran Watson

Recent Publications Connected to this Area
1. T.-H. Chao, S. Rekhi, J. Mittal, and D.P. Tabor,* Data-Driven Models for Predicting Intrinsically Disordered Protein Polymer Physics Directly from Composition or Sequence, Mol. Syst. Des. Eng. 8, 1146-1155 (2023). ChemRxiv Preprint. Github repository.

Computational Design of Energy Storage Materials

We have recently conducted a targeted virtual screen through low-potential radical chemical space to find a set of lead candidates for the radical component of organic radical polymer batteries. There are still many things left to work out on these modular materials (polymer backbone, electrolyte, etc.), but this work helps identify a new space of radicals that are predicted to have low potential and high dimer-dimer electronic coupling (necessary for high conductivity). See our paper.

Group Members Working in this Area
Cheng-Han Li
Vijay Sundar
Daniel Doria
Hayden Moran

Recent Publications Connected to this Area
1. R. Alessandri, C.-H. Li, S. Keating, K.T. Mohanty, A. Peng, J.L. Lutkenhaus, S. J. Rowan, D.P. Tabor,* and J.J. de Pablo,* Structural, Ionic, and Electronic Properties of Solid-State Phthalimide-Containing Polymers for All-Organic Batteries, ChemRxiv Preprint (2024).
2. T. Ma,  E. Fox,  M. Qi,  C.-H. Li,  K.A.N. Sachithani,  K. Mohanty,  D.P. Tabor,*  E.B. Pentzer,* and J.L. Lutkenhaus,* Charge Transfer in Spatially Defined Organic Radical Polymers. Chem. Mater. 35 (21), 9346–9351 (2023).
3. C.-H. Li and D.P. Tabor,* Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers, J. Phys. Chem. A 127, 3484–3489(2023). ChemRxiv Preprint.Github repository.
4. T. Ma, C.-H. Li, R.M. Thankur, D.P. Tabor, and J.L. Lutkenhaus,* The role of the electrolyte in non-conjugated radical polymers for metal-free aqueous energy storage electrodes, Nat. Mater. 22, 495-502 (2023).
5. C.-H. Li and D.P. Tabor,* Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening, J. Mater. Chem. A 10, 8273-8282 (2022). ChemRxiv Preprint. Github repository.

Developing New Spectroscopy Prediction Methods
This area of our group focuses on the development of new models for spectroscopic simulation. The first area of focus is on developing spectroscopic simulation methods that are compatible with coarse-grained simulations. Currently, we are employing these methods to accelerate the simulation of solvent-polymer interactions, pure liquids, and ionic liquids. Our focus is on maximizing the transferability of our methods.

At the cluster scale, we have focused on the spectroscopic simulation of aerosols. The chemical and physical properties of aerosols are of great importance to the planet’s atmospheric energy balance, public health, weather, and climate.  The effects of aerosols have long had the largest uncertainty in climate predictions. Though their formation is often modeled through simplified nucleation theories, the heterogeneity of aerosol formation and structure is, fundamentally, a chemistry problem spanning from the sub-nanometer (molecular) length scale up to and exceeding the 100 nm length scale of cloud condensation nuclei.

One of the largest unknowns in the understanding of aerosol formation is the structure and growth of secondary organic aerosols. Even with the modest size of the organic parent molecules, the directionality of hydrogen bonding interactions leads to a combinatorial explosion in the number of potential isomers with even a few water molecules.  This number of candidate structures makes the prediction of their actual structures intractable with the highest levels of computational theory.  

The first goal is to model the IR spectroscopy of small hydrogen-bonded clusters to identify the structures of small to medium-sized clusters of secondary organic aerosols. Our second goal is to accelerate our spectroscopic models with a suite of machine-learning approaches.  

Group Members Working in this Area
Abigail Moody
Tzu-Hsuan Chao
Hayden Moran
Vijay Sundar

Recent Publications Connected to this Area
1. R.W. Neisser, J.P. Davis, M.E. Alfieri, H. Harkins, A.S. Petit,* D.P. Tabor,* N.M. Kidwell,* Photophysical Outcomes of Water-Solvated Heterocycles: Single-Conformation Ultraviolet and Infrared Spectroscopy of Microsolvated 2-Phenylpyrrole. J. Phys. Chem. A, 127 (50), 10540–10554 (2023). Github repository.
2. N.P.D. Sawaya,* D. Marti-Dafcik, Y. Ho, D.P. Tabor, D. Bernal, A.B. Magann, S. Premaratne, P. Dubey, A. Matsuura, N. Bishop, W.A. de Jong, S. Benjamin, O.D. Parekh, N. Tubman, K. Klymko,* and D. Camps,* HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware. arXiv preprint: arXiv:2306.13126 (2023).
3. B. Peterson, M. Alfieri, D. Hood, C. Hettwer, D. Constantino, D.P. Tabor,* and N.M. Kidwell,* Solvent-Mediated Charge Transfer Dynamics of a Model Brown Carbon Aerosol Chromophore: Photophysics of 1-Phenylpyrrole Induced by Water Solvation, J. Phys. Chem. A 126, 4313-4325 (2022). ChemRxiv Preprint. Github repository.
4. N.P.D. Sawaya,* F. Paesani, and D.P. Tabor,* Near- and long-term quantum algorithmic approaches for vibrational spectroscopy. Phys. Rev. A 104, 062419 (2021). Preprint: arXiv:2009.05066 (2020).