arXiv:2609.37733v1 Announce Type: cross
Abstract: Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a s...
By Lizhong Fu, Jianan Wei, Wenguan Wang, Honghui Shang
arXiv:2509. 21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy.
By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik
arXiv:2509.21624v4 Announce Type: replace
Abstract: Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate...
By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik
arXiv:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.
By Siqi Chen, Zhiqiang Wang, Yili Shen, Xianqi Deng, Xi Cheng, Cheng-Wei Ju, Jun Yi, Guo Ling, Dieaa Alhmoud, Hui Guan, Zhou Lin
The paper introduces a neural network architecture that learns a latent, implicit basis representation of the electronic-state Hamiltonian, enabling a unified treatment of ground and excited states, conical intersections, and non‑adiabatic couplings. The model is trained on realistic photochemical systems—thymine and azobenzene—and accurately reproduces energies, oscillator strengths, and critical geometries such as conical intersections and excited‑state minima. It also captures Berry phase accumulation around conical intersections and can be extended to learn other operators like transition dipole moments.
By David Juergens, Martin St\"ohr, Andreas E. Hillers-Bendtsen, O. Jonathan Fajen, Todd J. Mart\'inez
The paper introduces Orbformer, a transferable wavefunction model that uses deep neural networks to pretrain on 22,000 equilibrium and dissociating molecular structures. Fine‑tuning Orbformer on unseen molecules achieves an accuracy‑cost ratio comparable to classical multireference methods, consistently reaching chemical accuracy (1 kcal/mol) on standard benchmarks, challenging bond dissociations, and Diels‑Alder reactions. This demonstrates that amortizing the cost of solving the Schrödinger equation across many molecules is feasible in quantum chemistry.
By Adam Foster, Zeno Sch\"atzle, P. Bern\'at Szab\'o, Lixue Cheng, Jonas K\"ohler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank No\'e, Jan Hermann