Update README.md

README.md

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 This dataset contains computationally generated atomic structures of amorphous boron nitride (aBN) with various configurations containing 2, 3, 8, 32, and 64 atoms per unit. Each structure is described by its atomic positions, lattice properties, and associated Hamiltonian (H) and overlap (S) matrices, which are commonly used in quantum mechanical simulations and electronic structure calculations.
 
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+**Abstract** 
+
+---
+We introduce HForge, a machine learning (ML) framework for predicting Hamiltonian (H) and Overlap (S) matrices directly from atomic structures, with a focus on amorphous boron nitride (aBN) and hexagonal boron nitride (hBN). Leveraging graph-based descriptors derived from the MACE [1] model and reference Hamiltonians computed via Siesta, HForge enables efficient electronic structure predictions.
+In this poster, we present how the choice of training structures impacts model performance and demonstrate that incorporating a diverse set of smaller structures significantly enhances the model’s ability to generalize to larger systems—a key strategy, given that training is 3–4 times more expensive than inference.
+We evaluate both equivariant and non-equivariant ML architectures, showing that equivariant models better preserve the physical symmetries of quantum interactions and outperform their non-equivariant counterparts in extrapolation tasks.
+Building on recent advancements in equivariant [2] graph-based atomic environment representations and universal message passing, our findings underscore the potential of scalable, ML-driven Hamiltonian prediction to accelerate classical DFT computations and enable quantum simulations of materials like aBN and hBN.
+
+---
+**Poster:** 
+
+![Uploading a.png…]()
+
+___
 
 **Features:**
 1. **nr_atoms:** Number of atoms in the structure (2, 3, 8, 32, 64).