Add TorsionNet500CCSDT benchmark visualisation, MAE metric, and docs

docs/source/user_guide/benchmarks/conformers.rst

@@ -40,3 +40,51 @@ Reference data:
 * Same as input data
 * :math:`PNO-LCCSD(T)-F12/ AVQZ` level of theory: a local, explicitly
   correlated coupled cluster method.
+
+TorsionNet500CCSDT
+===================
+
+Summary
+-------
+
+Performance in predicting torsional energy profiles for 500 diverse organic
+molecular fragments. Reference data from DLPNO-CCSD(T)/CBS calculations.
+
+Metrics
+-------
+
+1. RMSE of relative torsional energy profile
+2. MAE of relative torsional energy profile
+
+For each fragment, a torsion scan samples the energy at a series of dihedral
+angles. Both the reference and predicted energies are mean-centered per scan,
+so only the shape of the profile is compared rather than absolute energy
+offsets. RMSE and MAE are calculated between the reference and predicted
+profiles for each scan, then averaged across all 500 fragments.
+
+Computational cost
+------------------
+
+Medium: tests are likely to take minutes to run on GPU, or less than an hour on
+CPU for each model, since each fragment requires a single-point energy
+calculation across ~20 conformers in its torsion scan, repeated for all 500
+fragments.
+
+Data availability
+-----------------
+
+Input structures:
+
+* B. K. Rai, V. Sresht, Q. Yang, R. Unwalla, M. Tu, A. M. Mathiowetz, and
+  G. A. Bakken, TorsionNet: A Deep Neural Network to Rapidly Predict
+  Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum
+  Mechanics, Journal of Chemical Information and Modeling 62 (2022), 785-800.
+  PMID: 35119861.
+
+Reference data:
+
+* J. L. Weber, R. D. Guha, G. Agarwal, Y. Wei, A. A. Fike, X. Xie,
+  J. Stevenson, B. Santra, R. A. Friesner, K. Leswing, M. D. Halls, R. Abel,
+  and L. D. Jacobson, Efficient Long-Range Machine Learning Force Fields for
+  Liquid and Materials Properties, arXiv:2505.06462 (2025).
+* DLPNO-CCSD(T)/CBS level of theory.