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arXiv Machine Learning

Complete Neural Electronic Initialization Accelerates Materials DFT

The paper introduces AugNet, a complete neural electronic initializer that satisfies seven criteria for accelerating plane‑wave density functional theory (DFT) under the projector augmented wave (PAW) formalism. AugNet provides general equivariant predictions for PAW augmentation occupancies and spin densities, filling gaps left by previous models that omitted structure‑dependent components. When combined with existing valence density models, the approach achieves up to ~25% reduction in end‑to‑end DFT wall time on unseen structures while maintaining converged energies.

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arXiv AI
Aug 26

Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory

The paper presents a neural operator that learns the Kohn–Sham map, directly predicting electron density from the Kohn–Sham potential without orbital diagonalization. Using a domain‑invariant SE(3)‑equivariant Fourier neural operator trained on 8,504 molecules and solids, the model achieves quasi‑linear scaling self‑consistent field (SCF) convergence across diverse systems—including organic molecules, insulators, and metals—while reproducing Kohn–Sham DFT accuracy for densities, spectra, and structural observables. This enables large‑scale simulations, such as magnesium dislocation densities with 82,500 valence electrons, on a single GPU.

By Danish Khan, Maurice D. Hanisch, Nikolai Argatoff, Evan Xie, Sandeep Sharma, Anima Anandkumar
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arXiv Machine Learning
Jun 2

Benchmark Dataset for Catalysis on 2D MXenes

arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.

By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork
benchmarks
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arXiv Machine Learning
Jul 23

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems

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
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arXiv AI
Jun 9

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.

By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu
llmsdiffusionreinforcement-learning
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arXiv Machine Learning
Sep 7

Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

The paper introduces two Hessian-based data augmentation techniques—UniAug and ModeAug—to improve machine‑learning interatomic potentials (MLIPs). These methods use simple Taylor expansions to generate augmented configurations without modifying training objectives or increasing computational overhead. Experiments on both non‑equilibrium and equilibrium datasets show that the augmentations enhance model accuracy and provide practical guidelines for specific tasks.

By Bumju Kwak, Jeonghee Jo
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arXiv Machine Learning
Jun 18

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning

arXiv:2606. 18691v1 Announce Type: new Abstract: Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces.

By Youngwoo Cho, Seunghoon Yi, Wooil Yang, Sungmo Kang, Young-woo Son, Jaegul Choo, Joonseok Lee, Soo Kyung Kim, Hongkee Yoon
fine-tuningefficiencybenchmarks
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arXiv AI
Jun 15

A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

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
fine-tuningbenchmarks
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