A framework that combines diffusion-based materials generation with hierarchical thermodynamic and electronic structure screening accelerates discovery of inorganic electrides.

[PRX Intelligence 1, 013016] Published Tue Sep 01, 2026

A Symmetry-adapted Gaussian process regression model is employed for electronic structure prediction of twisted bilayer moiré materials, unraveling the impact of long-range interactions.

[PRX Intelligence 1, 013015] Published Thu Aug 27, 2026

Graph neural networks enable efficient prediction of the electronic density of states, making it possible to identify application-specific, structurally diverse compounds with similar electronic properties across vast chemical spaces.

[PRX Intelligence 1, 013014] Published Tue Aug 25, 2026

Resolving the angular content of internal representations, layer by layer, reveals how unconstrained machine-learning models learn near-exact equivariance, and which minimal inductive biases push symmetry breaking down to harmless levels.

[PRX Intelligence 1, 013013] Published Thu Aug 20, 2026

Joint diffusion is leveraged as a multimodal generative surrogate model for inertial confinement fusion (ICF), enabling post-shot inference, diagnostic optimization, and accelerated ICF design.

[PRX Intelligence 1, 013012] Published Tue Aug 18, 2026

Broken scaling laws are observed across three multiobjective graph neural network architectures trained to predict the optical properties of solids.

[PRX Intelligence 1, 013011] Published Thu Aug 13, 2026

A self-attention neural-network variational wavefunction is capable of describing both fractional quantum Hall liquids and electron crystals within the same architecture.

[PRX Intelligence 1, 013010] Published Tue Aug 11, 2026

A quantum circuit realization of flow matching enables efficient interpolation between density matrices, and can be implemented in existing quantum computing architectures without costly redesigns.

[PRX Intelligence 1, 013009] Published Thu Aug 06, 2026

Neural decoders can serve as a robust and accurate foundation for fault-tolerant quantum algorithms under realistic conditions.

[PRX Intelligence 1, 013008] Published Tue Aug 04, 2026

An efficient reciprocal space based predictor of crystal properties achieves improved accuracy in predicting various crystal properties compared to existing crystal graph convolutional neural network methods, at a much lower computational cost.

[PRX Intelligence 1, 013007] Published Thu Jul 30, 2026

Chief Editor Anatole von Lilienfeld and the PRX Intelligence editorial team outline the newest APS journal’s vision, scope and philosophy in the context of the rapidly evolving landscape of AI and scientific discovery.

[PRX Intelligence 1, 010001] Published Tue Jul 28, 2026

A data-driven linear foundation model identifies a compact variational subspace for quantum embedding, substantially reducing the cost of solving embedding Hamiltonians and enabling faster simulations of strongly correlated systems.

[PRX Intelligence 1, 013001] Published Tue Jul 28, 2026

A hybrid framework combining parameterized quantum circuits with transformer-based neural quantum states is proposed to construct variational wavefunctions for quantum many-body systems.

[PRX Intelligence 1, 013002] Published Tue Jul 28, 2026

A message-passing neural network method is presented that enables accurate many-body dispersion calculations spanning the periodic table for organic and inorganic molecules as well as organic solids using only the molecular structure as input, providing a streamlined tool for incorporating van der Waals interactions into molecular simulations on top of semiempirical methods and machine learning force fields.

[PRX Intelligence 1, 013003] Published Tue Jul 28, 2026

A novel clustering approach demonstrates that similarity-based representation learning combined with density-based aggregation is a promising strategy for point cloud segmentation in highly granular particle detectors.

[PRX Intelligence 1, 013004] Published Tue Jul 28, 2026

A convolutional neural network is utilized to autonomously tune a minimal realization of a Kitaev chain toward a Poor Man’s Majorana sweet spot.

[PRX Intelligence 1, 013005] Published Tue Jul 28, 2026

A field-aware equivariant interatomic potential integrates electric bias into foundation-model simulations across chemical space, learning a differentiable electric enthalpy for polarization, polarizability, and Born effective charges.

[PRX Intelligence 1, 013006] Published Tue Jul 28, 2026