arXiv Machine Learning

Derivative Informed Learning of Exchange-Correlation Functionals

arXiv:2606. 04279v1 Announce Type: new Abstract: Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently outperform traditional $\mathcal{O}(N^4)$-scaling hybrid functionals.

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

arXiv Machine Learning

Jul 8

EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation

arXiv:2607. 05559v1 Announce Type: new Abstract: Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy.

By Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano, Matthias F. Kling, Thomas Linker

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-tuning benchmarks

arXiv Statistics ML

Sep 10

Beyond Conventional Federated Learning via High-Order Regularization

arXiv:2609.09904v1 Announce Type: cross Abstract: Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic re...

By Alireza Kabgani, Masoud Ahookhosh

arXiv Machine Learning

Jul 28

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.

By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo

robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 26

Response Renormalization for Critical Deep Equilibrium Models

arXiv:2608.23725v1 Announce Type: new Abstract: Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update. Training through this equilibrium uses i...

By Jose Luis Lima de Jesus Silva

arXiv Machine Learning

Sep 15

ReLU Neural Network Approximation to Smooth Functional Operator: Dimensional Decay and Error Analysis

arXiv:2609.15355v1 Announce Type: cross Abstract: We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by deep ReLU neural network...

By Shuhao Jiao

arXiv AI

Jun 8

COF26: A new on-top functional for multiconfiguration pair-density functional theory

arXiv:2605. 06215v2 Announce Type: replace-cross Abstract: Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient and accurate framework for computing electronic energies in strongly correlated molecular systems, with the quality of the on-top functional being a key determinant of its predictive accuracy.

By Yuhao Chen, Donald G. Truhlar, Xiao He

benchmarks

arXiv Machine Learning

Jul 28

SPRKD: Effective Knowledge Distillation for Deep Neural Networks via Saddle Region Approximation

arXiv:2607. 23346v1 Announce Type: new Abstract: Modern deep neural networks are potent catalysts for scientific and industrial impact, yet excessive parameter counts impede deployment in low-compute settings such as hospital equipment and energy infrastructure.

By Aditya Dewan, Arjun Yogeswaran, Benjamin Fedoruk

rag fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 20

A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory

arXiv:2607. 15291v1 Announce Type: cross Abstract: We develop a physics-informed neural network (PINN) framework for nonlocal partial differential equations arising in dynamic density functional theory (DDFT).

By Dimitrios Gourzoulidis, Soumaya Elkantassi, Serafim Kalliadasis

arXiv Machine Learning

Jun 25

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors

arXiv:2606. 25971v1 Announce Type: new Abstract: Modern neural network training relies on optimizers such as Adam and Muon which act on each weight matrix as a single object.

By Alexander H\"agele, Alejandro Hern\'andez-Cano, Atli Kosson, Martin Jaggi

arXiv Machine Learning

2d ago

Same Loss, Different Gradients

arXiv:2609.38786v1 Announce Type: new Abstract: Differentiable learning typically assumes that the scalar objective evaluated in the forward pass and the gradient supplied to the optimizer in the bac...

By Ningkang Peng, Xiaoqian Peng, Yifan He, Anjie Hu, Chao Tan, Peirong Ma, Yanhui Gu

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