arXiv Machine Learning

MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

arXiv:2606. 28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales.

arXiv Machine Learning

Jun 2

Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations

arXiv:2606. 00401v1 Announce Type: cross Abstract: Simulating large molecular systems comprising thousands of atoms requires highly scalable methodologies.

By Abhiram Badrinarayanan, Davor Davidovic, Edoardo Di Napoli, Jurica Novak, Luigi Genovese, Gustavo Ramirez-Hidalgo, Xinzhe Wu

arXiv Machine Learning

Jun 3

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames

arXiv:2506. 09398v4 Announce Type: replace Abstract: We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science.

By Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian, Shuiwang Ji

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.

By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik

benchmarks

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

arXiv Machine Learning

Jul 16

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction

arXiv:2607. 13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective.

By James T. Pegg, Hubert Okadome Valencia, Ronin Wu

benchmarks safety

arXiv Machine Learning

Jun 26

Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy

arXiv:2606. 26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation.

By Snehal Raj, Brian Coyle, L\'eo Monbroussou, Andr\'e J. Ferreira-Martins, Renato M. S. Farias, Elham Kashefi

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.

By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik

llms benchmarks

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.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

llms diffusion fine-tuning

arXiv AI

Aug 3

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

arXiv:2607. 29158v1 Announce Type: cross Abstract: We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations.

By Johannes Mae{\ss}, Leon Werner, J. Thorben Frank, Winfried Ripken, Martin Michajlow, Joshua Futterer, Klaus-Robert M\"uller, Stefan Chmiela

efficiency

arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma

rag diffusion efficiency multimodal benchmarks safety

arXiv AI

Jul 1

CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

arXiv:2606. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.

By Minzhang Li, Mingrui Li, Weichen Qin, Qihe Chen, Sixian Shen, Yuan Pei, Jiakai Zhang, Jingyi Yu

multimodal benchmarks

arXiv AI

Jun 12

An LLM System for Autonomous Variational Quantum Circuit Design

arXiv:2606. 13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise.

By Kenya Sakka, Wataru Mizukami, Kosuke Mitarai

llms agents computer-vision benchmarks

Related stories