Classical solution of the FeMo-cofactor model to chemical accuracy and its implications

Huanchen Zhai, Chenghan Li, Xing Zhang, Zhendong Li, Seunghoon Lee, and Garnet Kin-Lic Chan (2026)
Highlighted by Jan Jensen

The FeMo cofactor in nitrogenase enzymes is often mentioned as the killer application of quantum computing (QC) in chemistry. That is due to its complex electronic structure, which has made is difficult to model accurately. However, Chan and co-workers now claim to have computed the electronic energy to, by their estimate, chemical accuracy by conventional means.

They have done so by a series of calculations as indicated in the figure above. The CPU requirements are not given in detail, but the authors point out that no supercomputer was needed. 

Interestingly, the authors found that the ground state wavefunction is not inherently strongly multireference. Rather the main challenge is to identify the correct (mostly) single-reference state.

Where does that leave chemical applications of QC? For one thing, it moves the goalpost further back. The active space is the one typically used to estimate QC requirements, but it may have to be expanded to include MOs from the surrounding protein to accurately capture the chemistry, which would require even larger quantum computer. But that will be even further into the future with plenty of time for conventional approached to get there first. 

In my opinion, the case for QC-based quantum chemistry was never very strong, and this study is just another blow.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks

Yu Harabuchi, Ruben Staub, Min Gao, Nobuya Tsuji, Benjamin List, Alexandre  Varnek, and Satoshi Maeda (2026)
Highlighted by Jan Jensen

The automated predict of chemical reaction networks have thus far been limited to relatively small systems, typically with less than 50 atoms (including Hs) due to computational expense. This study goes significantly beyond this by studying a system with 228 atoms.

This is made possible by three things: 

1. While the system is big, the reaction is relatively simple, so the reaction network is relatively small. 

The reaction is an acid-catalysed cyclisation reaction involving a relatively small and chemically simple molecules. It is the (chiral) acid catalyst that contributes most of the atoms. The reaction itself has three steps: protonation of alkene group, intramolecular C-O bond formation on the activated alkene, deprotonation of the O to regenerate the catalyst. Most of the atoms are chemically inert, and there are 12 chemically active atoms (defined by the user). In all, the study identified 74 possible intermediates/products and only about half of those are chemically distinct if you ignore chirality. 

2. Cheap surrogate energy function

They use a Δ-ML approach that corrects the xTB energy and gradient to obtain better accuracy. The ML model is trained on-the-fly against DFT calculations. 

3. Massive computational resources 

In spite of 1 and 2 they this study required massive computational resources. They don't address this point specifically, other than to mention that it requires millions of gradient evaluations, but Maeda stressed this point during his talk at the WATOC last year. 

So this is not exactly a routine application. 

This work is licensed under a Creative Commons Attribution 4.0 International License.