Engineering topics

Chaudhuri, Santanu

Publications and source records attributed to Chaudhuri, Santanu.

5 records

DOE OSTI2024

Structure, Bonding, and Vibrational Dynamics of a Triamine High Energy Density Material under Pressure

High energy density materials have complex intermolecular interactions which influence their stability and performance. We used a combination of synchrotron X-ray diffraction, synchrotron infrared spectroscopy, and Raman vibrational spectroscopy, supplemented by density functional theory calculations, to probe pressure-induced changes in structure and intermolecular interactions of 1H,4'H-[3,3'-bis(1,2,4-triazole)]-4',5,5'-triamine as a model high energy density material up to 40 GPa. We find that compression of the triamine is accompanied by increased intermolecular interactions that give rise to an interesting evolution of the structure and bonding with pressure. Analysis of the equation of state determined from the X-ray diffraction indicates a change in compression mechanism near 19 GPa consistent with changes in vibrational spectra that provide evidence for a structural rearrangement associated with changes in hydrogen bonding near that pressure. As a result, the overall compressional behavior calculated theoretically agrees with that observed experimentally though differences are found that indicate the need for improved treatment of the intermolecular interactions including hydrogen bonding under pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Environmentally sustainable lithium-ion battery cathode binders based on cellulose nanocrystals

Aqueous binders as environmentally sustainable alternatives to conventional polyvinylidene difluoride (PVDF) binders have not yet been successful for cathodes in lithium-ion batteries (LIBs). Here, carboxylic acid functionalized cellulose nanocrystals (CNC-COOHs) have been obtained from Miscanthus × giganteus (M×G) biomass and evaluated as aqueous binders for LIB cathodes.

Cellulose nanocrystal↗

DOE OSTI2024

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO 2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO 2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO 2 adsorption capacities. We present the top six AI-generated MOFs with CO 2 capacities greater than 2m mol g -1 , i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

End-to-end AI framework for interpretable prediction of molecular and crystal properties

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

36 MATERIALS SCIENCE↗

DOE OSTI2023

How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

42 ENGINEERING↗