The paper presents a multitask learning approach that incorporates orbitally resolved semiempirical charges into machine learning interatomic potentials (MLIPs). By training with inexpensive GFN1‑xTB orbital charges, the model reduces energy mean absolute error by 58% compared to energy‑only baselines and needs five times less data to achieve similar performance. The method also outperforms or matches models using costly DFT charges and preserves inference efficiency, offering a scalable way to build accurate, data‑efficient foundation models for complex chemical systems.
By Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr Voznyy
The paper presents NuCLR, a multi-task neural network that learns nuclear data representations to predict charge radii and electric‑quadrupole transition strengths across hundreds of nuclides. Using held‑out ensembles, the model achieves a charge‑radius RMS deviation of 0.0147 fm and a B(E2) RMS deviation of 0.192 e²b², comparable to leading nuclear models. The authors provide error bars indicating where additional experimental data could improve predictions, positioning NuCLR as a data‑driven surveyor of nuclear structure.
By Giuliano Giacalone, Sokratis Trifinopoulos, Mike Williams
The paper introduces NEMORA, a neural equivariant extension of the Fast Multipole Method designed to capture long‑range tensorial interactions in atomistic machine learning models. NEMORA replaces the FMM’s fixed multipole expansion with learnable equivariant operators that operate on an adaptive spatial hierarchy, enabling many‑body interactions across multiple length scales while preserving physical inductive biases. The method achieves linear time and memory complexity, allowing it to handle systems with hundreds of thousands of atoms and significantly reducing force and energy errors on non‑local benchmarks compared to short‑range backbones.
By Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo
arXiv:2609.14906v1 Announce Type: cross
Abstract: The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system...
By Yuxuan Zeng, Taoyuze Lv, Zhicheng Zhong
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:2608. 14076v1 Announce Type: cross Abstract: Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations.
By Kaipeng Zeng, Wenxi Zhai, Shengrui Xu, Jie Zhao, Bowen Li, Shiyue Wang, Junchi Yan, Tong Zhu
arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.
By Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk
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
arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.
By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma
The paper introduces Gaussian Splatting for Density Functional Theory (GS‑DFT), a method that represents molecular orbitals as a cloud of Gaussians optimized via gradient descent. GS‑DFT replaces fixed atom‑centered basis sets with an adaptive, differentiable orthogonalization and efficient two‑electron integral evaluation, achieving accuracy comparable to large conventional bases with far fewer parameters. The solver scales quadratically with cloud size, enabling simulations of up to 2,742 atoms on a single four‑GPU node at triple‑zeta precision.
By Andr\'es Guzm\'an-Cordero, Cindy Zhang, Majdi Hassan, Marta Skreta, Kirill Neklyudov, Matija Medvidovi\'c
arXiv:2507. 03853v2 Announce Type: replace Abstract: We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information.
By Beom Seok Kang, Vignesh C. Bhethanabotla, Amin Tavakoli, Maurice D. Hanisch, Arimitsu Horikawa-Strakovsky, Miguel Nouman, Danish Khan, William A. Goddard III, Anima Anandkumar
arXiv:2503. 19847v2 Announce Type: replace-cross Abstract: The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods.
By Zeno Sch\"atzle, P. Bern\'at Szab\'o, Alice Cuzzocrea, Mat\v{e}j Mezera, Frank No\'e