The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2503. 19847v2 Announce Type: replace-cross Abstract: The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods.
arXiv:2507. 03853v2 Announce Type: replace Abstract: We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information.
arXiv:2607. 13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective.
arXiv:2607. 20585v1 Announce Type: cross Abstract: Sample-based Quantum Diagonalization (SQD), an extension of Quantum Selected Configuration Interaction (QSCI), has emerged as a promising hybrid quantum-classical paradigm for computing molecular ground state energies.
arXiv:2607. 29158v1 Announce Type: cross Abstract: We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations.
arXiv:2606. 02662v1 Announce Type: cross Abstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data.