wip: Differentiable Hamiltonian evaluation #52
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ESEberhard
reviewed
Aug 14, 2025
ESEberhard
reviewed
Aug 14, 2025
| basis = basisset(mol, "def2-SVP") | ||
| pos, rest = eqx.partition(mol, lambda x: id(x) == id(mol.position)) | ||
| grad_E = jax.grad(f)(pos, rest, basis) | ||
| assert_allclose(-grad_E.position, scf_grad, atol=1e-1) |
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Do you have an intuition regarding these accuracies? And what is the current relative error? :D
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The current problem is the XC evaluation isn't exactly like-for-like but it should be possible to get them to match much closer. Checkout test/test_autograd_integrals.py which shows that autodiff can match the analytic gradients of the one-electron components. I'm optimistic to have the absolute error at 1e-5 once mess can match the XC mesh generation exactly the same as pyscf's numerical quadrature.
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