Gautam Jha

• Kozdra, Sylwia; Jha, Gautam & Atsumi, Michiko (2026). Structure-energy-transport relationships in PEO–PVDF electrolytes with Li⁺, Na⁺and K⁺: a combined DFT and spectroscopic study. Computational and Theoretical Chemistry. ISSN 2210-271X. 1266. doi: 10.1016/j.comptc.2026.116069. Fulltekst i vitenarkiv
• Jha, Gautam (2026). Convolutional Kernel-Embedded E(3)-Equivariant Networks KEN: Overcoming Architectural Rigidity in Machine Learning Interatomic Potentials. Journal of Chemical Theory and Computation. ISSN 1549-9618. Fulltekst i vitenarkiv Vis sammendrag

  Machine learning interatomic potentials (MLIPs) have emerged as scalable alternatives to first-principles methods such as density functional theory (DFT). Among them, E(3)-equivariant graph neural networks (GNNs) like PACE and MACE are highly accurate but structurally rigid, limiting the incorporation of rich, multi-channel electronic descriptors. We introduce Convolutional Kernel-Embedded E(3)-Equivariant Networks, KEN, a hybrid architecture that combines the local environmental extraction of 2D convolutional kernels with the many-body expressivity of an E(3)-equivariant GNN backbone. This design preserves symmetry while enabling flexible, multi-modal input features without increasing training data requirements. Trained and tested on heavy element 26 (HE26) dataset, KEN was compared with MACE-osaka26 model. Further validation on lattice parameter prediction through Birch–Murnaghan equation of state, magnetic moment prediction and comparison with CHGNet framework, and microcanonical molecular dynamics confirms robust energy conservation and accurate reproduction of the forces, and thus, demonstrating a viable pathway to overcoming architectural rigidity in state-of-the-art MLIPs.

Se alle arbeider i NVA

• Jha, Gautam (2026). Everything, Everywhere, All at Once: Developing chemical lenses from first principles to ML architectures. Fulltekst i vitenarkiv Vis sammendrag

  A molecule does not look the same to every eye, as theoreticians we are not only writing the codes but also developing these lenses. In this talk, I will discuss three chemical lenses probing different scales of chemistry in a molecule. Everything: Looking through a quantum macro lens, we can ask what extra details we are missing. Sen and Tellgren found one such missing piece - kinetic energy anisotropy. Now, along with Dr. Tellgren, I am trying to incorporate anisotropy into designing a new rung of DFT functional. Everywhere: Through a wide angle lens, I wanted to know the chemical phenomenons at a larger scale in a molecule so I developed a framework for generating semi-empirical parameters for studying interaction of actinide fuel salts in a molten salt reactor. All at Once: Machine learning is analogues to a high speed cinematic engine, which have the potential to capture chemical phenomenons happening at once, such as chemical reactions. I have developed KEN, a multi-modal ML architecture to capture local electronic environments for studying chemical reactions in strongly correlated systems.

• Jha, Gautam (2026). DFTB Parameterization Framework for Actinide Molten Salts. Fulltekst i vitenarkiv Vis sammendrag

  Actinide fuel salts are a crucial part of the molten salt reactors. However, the chemistry of these salts under high temperature environments are under explored as complex electronic structure of actinides make it quite difficult to study the evolution of their molten structure for long timescale at Density Functional theory level. Semi-empirical methods such as Density Functional Tight Binding (DFTB) exist which can probe the underlying chemistry with long timescale dynamics but it lacks the required parameters. We have developed a particle swarm optimization based parametrization framework and calculated the DFTB parameter set for actinide and halide interactions which were verified with geometrical optimizations and thermodynamic reaction enthalpies spanning multiple oxidation states of actinides, and radial distribution function verifications from long timescale dynamics simulations of the fuel salts.

• Jha, Gautam (2026). Importance of Kinetic Energy Tensor in Chemistry. Fulltekst i vitenarkiv Vis sammendrag

  Understanding the bonding in chemical systems is one of the prime focus of quantum chemistry and most of the current analyzes are developed on the premise of density functional theory (DFT). DFT relies heavily on the exchange correlation (XC) functionals where the exchange energy is calculated from the Taylor expansion of a spherically averaged exchange hole which eliminates information about the anisotropy of the related properties of the exchange hole. The Taylor expansion expresses the exchange hole curvature in terms of kinetic energy density. Several formulations exist which connect the bonding scenarios to kinetic energy density, such as electron localization functions (ELF). Here, we have investigated bonding in chemical systems by taking the tensor forms of the kinetic energy density rather than the scaler form calculated by spherical averaging. This approach provides us with additional insights such as anisotropy of the bonding modes and opens the path for inclusion of exchange hole anisotropy in kinetic energy based Meta-GGA DFT functionals.

Se alle arbeider i NVA