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Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed. 

Molecular systems are analyzedviathe construction of a molecular graph and quantifying the resiliency for charge transport through metrics for graph centrality, in the context of charge pathways between the source and drain electrodes. 

Abstract Charge transport in molecular solids, such as semiconducting polymers, is strongly affected by packing and structural order over several length scales. Conventional approaches to modeling these phenomena range from analytical models to numerical models using quantum mechanical calculations. While analytical approaches cannot account for detailed structural effects, numerical models are expensive for exhaustive (and statistically significant) analysis. Here, we report a computationally scalable methodology using graph theory to explore the influence of molecular ordering on charge mobility. This model accurately reproduces the analytical results for transport in nematic and isotropic systems, as well as experimental results of the dependence of the charge carrier mobility on orientation correlation length for polymers. We further model how defect distribution (correlated and uncorrelated) in semiconducting polymers can modify the mobility, predicting a critical defect density above which the mobility plummets. This work enables rapid (and computationally extensible) evaluation of charge mobility semiconducting polymer devices. 

Abstract Estimating a patient‐specific computational model's parameters relies on data that is often unreliable and ill‐suited for a deterministic approach. We develop an optimization‐based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient‐specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real‐world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient‐specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data. 

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.