Hugging Face Trending Papers
Learning ab initio phase-field models
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arXiv Machine Learning
3d ago
Learning ab initio phase-field models
The paper introduces a method to construct phase‑field models directly from ab initio data by projecting molecular dynamics onto species‑density fields using the Mori‑Zwanzig formalism. Neural networks parameterize the resulting non‑local free energy and mobility, trained on short MD trajectories generated with machine‑learning interatomic potentials. Demonstrations on an iron‑boron melt and hydrogen‑helium mixtures show the approach can predict pressure‑dependent stability, immiscibility boundaries, and large‑scale droplet dynamics beyond conventional atomistic simulations.
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.
arXiv Machine Learning
4d ago
A strategic roadmap for an atomistic machine-learning ecosystem
arXiv:2609.39090v1 Announce Type: cross Abstract: Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of...
Hugging Face Trending Papers
6d ago
A strategic roadmap for an atomistic machine-learning ecosystem
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. Th...
arXiv Machine Learning
1d ago
Coupled reaction and diffusion governing interface evolution in solid-state batteries
The study uses large‑scale, quantum‑accurate reactive simulations powered by active learning and deep equivariant neural network potentials to investigate the coupled reactions and diffusion at the interface of a symmetric solid‑state battery cell. Unsupervised clustering of local atomic environments reveals a previously unreported crystalline disordered phase, Li₂S₀.₇₂P₀.₁₄Cl₀.₁₄, in the solid‑electrolyte interphase (SEI), and explains experimental observations of SEI formation and lithium creep mechanisms that drive dendrite initiation. The work demonstrates a parameter‑free digital twin capable of providing atomistic insights into complex heterogeneous processes in solid‑state electrochemistry.
arXiv Machine Learning
Sep 21
Transformers Discover Molecular Structure Without Graph Priors
The paper investigates whether machine learning models can uncover physical patterns in atomistic data without relying on traditional physics-based inductive biases such as geometric locality or graph structures. By training a general-purpose architecture on molecular simulation data, the authors demonstrate that the model autonomously learns interatomic interaction strengths resembling classical electrostatics and identifies interaction cutoffs aligned with established physical models. The study also reports predictable neural scaling behavior and competitive accuracy on certain metrics compared to physics-informed architectures, suggesting that explicit priors may only be necessary when empirically justified.