arXiv Machine Learning
Jun 2
Latent Diffusion Pretraining for Crystal Property Prediction
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density
arXiv:2608. 03260v1 Announce Type: new Abstract: Pretraining has shown strong potential for learning transferable representations, yet it remains underexplored for electron-density-based molecular learning.
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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.
arXiv Machine Learning
Jul 9
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
arXiv:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.
arXiv Machine Learning
Jul 1
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
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.
arXiv AI
Aug 7
Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation
arXiv:2605. 26540v2 Announce Type: replace-cross Abstract: Energetic materials power mining, demolition, propulsion and airbags, yet today's compounds were designed decades ago.
arXiv AI
Jul 14
The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
arXiv:2607. 10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization.
arXiv Machine Learning
Jul 22
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
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.
arXiv AI
Aug 10
ED-CSP: Crystal Structure Prediction from Electron Diffraction
arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.
arXiv Machine Learning
Jun 30
Shoot from the HIP: Hessian Interatomic Potentials without derivatives
arXiv:2509. 21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy.
arXiv Machine Learning
Jun 11
Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction
arXiv:2606. 11508v1 Announce Type: new Abstract: Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging because ADME endpoints are noisy, interdependent, and often data-limited.
arXiv AI
Jun 15
An interpretable unsupervised representation learning for high precision measurement in particle physics
arXiv:2511. 22246v2 Announce Type: replace-cross Abstract: Unsupervised learning has been widely applied to various tasks in particle physics.