arXiv:2603. 11249v4 Announce Type: replace Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering.
By Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig, Alexander Mitsos
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
arXiv:2607. 10887v1 Announce Type: cross Abstract: Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT).
By Jan Eckwert, Julija Zavadlav
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
By Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang, Kerstin Klaeser, Cristian Gabellini, Nikhil Shenoy, Francesco Di Giovanni, Prudencio Tossou
Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). However, inference time of MLPs is orders of magnitude slower than that of classical force fields, hindering real-world applications for biomolecular systems that require timescales of microseconds and beyond.
arXiv:2606. 14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve.
By Yunhong Lou, Xihang Yue, Xinran Wei, Tianqi Deng, Linchao Zhu