Preprint scanner project / Issue 2026-09-16

Useful Chemistry

A weekly review of arXiv, ChemRxiv, and bioRxiv for ai-enabled methodological advances. Up to twenty papers reviewed every week; a quiet week may publish none.

20 selected / 96 reviewed Sep 9, 2026–Sep 15, 2026
Groups
Group roll call
96 followed groups Research-group roll call
  • Milad Abolhasani Abolhasani Lab North Carolina State University Site ↗
  • Surl-Hee (Shirley) Ahn Ahn Lab University of California, Davis Site ↗
  • Mohammed AlQuraishi AlQuraishi Laboratory Columbia University Site ↗
  • Sayan Banerjee Banerjee Group University of Tennessee, Knoxville Site ↗
  • Christopher J. Bartel Bartel Research Group University of Minnesota Twin Cities Site ↗
  • Jan-Niklas Boyn Boyn Research Group University of Minnesota Twin Cities Site ↗
  • Ting Cao Cao Group University of Washington Site ↗
  • Timothy Cernak Cernak Lab University of Michigan Site ↗
  • Ming Chen Ming Chen Group Purdue University Site ↗
  • Po-Yen Chen Chen Research Group University of Maryland, College Park Site ↗
  • Bingqing Cheng Cheng Group University of California, Berkeley Site ↗
  • Brian Cleary Algorithmic Lens on Experimental Biology Laboratory Boston University Site ↗
  • Connor W. Coley Coley Research Group Massachusetts Institute of Technology Site ↗
  • Yamil J. Colón Colón Group University of Notre Dame Site ↗
  • César de la Fuente Machine Biology Group University of Pennsylvania Site ↗
  • Julia Dshemuchadse Cape Crystal Cornell University Site ↗
  • Thomas P. Fay Fay Group University of California, Los Angeles Site ↗
  • Kara D. Fong Fong Lab California Institute of Technology Site ↗
  • Thomas E. Gartner III Gartner Group Lehigh University Site ↗
  • Rafael Gómez-Bombarelli Learning Matter Massachusetts Institute of Technology Site ↗
  • Jonathan Gootenberg Gootenberg Laboratory Harvard University Site ↗
  • Prashun Gorai 3D Materials Lab Rensselaer Polytechnic Institute Site ↗
  • Diptarka Hait Hait Group Columbia University Site ↗
  • Brian Hie Laboratory of Evolutionary Design Stanford University Site ↗
  • Guoxiang Hu Hu Group Georgia Institute of Technology Site ↗
  • Yong-Jie Hu Materials Computation and Informatics Group Drexel University Site ↗
  • Yifei Huang Huang Lab Pennsylvania State University Site ↗
  • Yunha Hwang Hwang Lab Massachusetts Institute of Technology Site ↗
  • Nicholas E. Jackson AI for Materials Group University of Illinois Urbana-Champaign Site ↗
  • William M. Jacobs Jacobs Group Princeton University Site ↗
  • Dipti Jasrasaria Jasrasaria Group University of Chicago Site ↗
  • Anupama Jha Jha Lab Yale University Site ↗
  • Kaiyi Jiang Jiang Lab Princeton University Site ↗
  • Adrian Jinich Jinich Lab University of California, San Diego Site ↗
  • Felipe Jornada Jornada Research Group Stanford University Site ↗
  • Kalli Kappel Kappel Lab University of California, Los Angeles Site ↗
  • Joshua Kretchmer Kretchmer Research Group Georgia Institute of Technology Site ↗
  • Aditi S. Krishnapriyan Krishnapriyan Research Group University of California, Berkeley Site ↗
  • Sebastian Kube Kube Lab University of Wisconsin–Madison Site ↗
  • Heather J. Kulik Kulik Research Group Massachusetts Institute of Technology Site ↗
  • Ambarish R. Kulkarni Kulkarni Research Group University of California, Davis Site ↗
  • Joseph S. Kwon Kwon Research Group The Ohio State University Site ↗
  • Joonho Lee Lee Group Harvard University Site ↗
  • Can Li Li Research Group Purdue University Site ↗
  • Wanlu Li Wanlu Li Research Group University of California, San Diego Site ↗
  • Rebecca K. Lindsey Lindsey Lab University of Michigan Site ↗
  • Ge Liu Ge Liu Group University of Illinois Urbana-Champaign Site ↗
  • Yuanyue Liu Yuanyue Liu Group The University of Texas at Austin Site ↗
  • Yang Lu Lu Lab University of Wisconsin–Madison Site ↗
  • Jiankun Lyu Evnin Family Laboratory of Computational Molecular Discovery The Rockefeller University Site ↗
  • Cong Ma Cong Ma Lab University of Michigan Site ↗
  • Arkajit Mandal Mandal Group Texas A&M University Site ↗
  • Andrew J. Medford Medford Research Group Georgia Institute of Technology Site ↗
  • Ilias Mitrai Systems and AI Lab The University of Texas at Austin Site ↗
  • Jeetain Mittal Mittal Group Texas A&M University Site ↗
  • Joel A. Paulson Paulson Lab University of Wisconsin–Madison Site ↗
  • Elisa Pieri Pieri Lab University of North Carolina at Chapel Hill Site ↗
  • Doran Raccah MesoScience Lab The University of Texas at Austin Site ↗
  • Phillip Rauscher Rauscher Group New York University Site ↗
  • Wesley Reinhart Reinhart Group Pennsylvania State University Site ↗
  • Gabriel J. Rocklin Rocklin Lab Northwestern University Site ↗
  • Andrew S. Rosen Rosen Research Group Princeton University Site ↗
  • Grant M. Rotskoff Rotskoff Group Stanford University Site ↗
  • Janani Sampath Sampath Research Group University of Florida Site ↗
  • Elvira Sayfutyarova Sayfutyarova Group Pennsylvania State University Site ↗
  • Martin Seifrid Seifrid Group North Carolina State University Site ↗
  • Thomas P. Senftle Senftle Group Rice University Site ↗
  • Karthik Shekhar Shekhar Lab University of California, Berkeley Site ↗
  • Zachary M. Sherman Z Lab University of Washington Site ↗
  • Krishna Shrinivas Shrinivas Lab Northwestern University Site ↗
  • Rohit Singh Singh Lab Duke University Site ↗
  • Micheline Soley Soley Group University of Wisconsin–Madison Site ↗
  • Kevin V. Solomon Solomon Laboratory University of Delaware Site ↗
  • Jeff Spence Spence Lab University of California, San Francisco Site ↗
  • Kayla G. Sprenger Rational Design of Interfaces Lab University of Colorado Boulder Site ↗
  • Chong Sun Sun Lab Rutgers University–New Brunswick Site ↗
  • Yidan Sun Sun Lab Washington University in St. Louis Site ↗
  • Daniel Tabor Tabor Research Group Texas A&M University Site ↗
  • Ming Tang Mesoscale Materials Science Group Rice University Site ↗
  • Roel Tempelaar Tempelaar Team Northwestern University Site ↗
  • Erik Thiede Thiede Lab Cornell University Site ↗
  • Pratyush Tiwary Artificial Chemical Intelligence@Maryland University of Maryland, College Park Site ↗
  • Brian Trippe Trippe Lab Stanford University Site ↗
  • Alexander Urban Urban Research Group Columbia University Site ↗
  • David Van Valen Van Valen Lab California Institute of Technology Site ↗
  • Vojtech Vlcek Vlcek Group University of California, Santa Barbara Site ↗
  • Allon Wagner Wagner Lab University of California, Berkeley Site ↗
  • Shunzhi Wang Wang Lab New York University Site ↗
  • Michael A. Webb Webb Research Group Princeton University Site ↗
  • Mingjian Wen Wen Research Group University of Houston Site ↗
  • Hong-Zhou Ye Ye Group University of Maryland, College Park Site ↗
  • Shuwen Yue Yue Research Group Cornell University Site ↗
  • Daiwei (David) Zhang Daiwei Zhang Lab University of North Carolina at Chapel Hill Site ↗
  • Hongbo Zhao Zhao Research Group University of California, San Diego Site ↗
  • Jian Zhou Zhou Lab University of Chicago Site ↗
  • Tianyu Zhu Zhu Group Yale University Site ↗

Issue 2026-09-16

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

RS-CIDER: A non-local machine learning model for approximating screened hybrid functionals

Zhuotao Jin, Mohamed S. Abdallah, Boris Kozinsky, Kyle Bystrom

RS-CIDER fits a non-local exchange functional to ground-state energies and single-particle levels, replacing the short-range Hartree-Fock term of HSE06 during self-consistent evaluation. The authors report agreement with HSE06 for reaction energies and band gaps, with a measured per-self-consistent-field-step wall time more than an order of magnitude lower. The work puts a more costly hybrid-functional reference within reach for larger periodic calculations without dropping self-consistency.

AI–chemistryAI–materials Electronic structure
Abstract brief CC0 Preprint
#02 arXiv v1 / preprint

Preprint—not peer reviewed

uFlowCSP: Crystal Structure Prediction using Mean flow generative models

Sourin Dey, Dipannoy Das Gupta, Lai Wei, Sadman Sadeed Omee, Jianjun Hu

uFlowCSP learns an average probability-flow velocity to generate crystal structures in a small number of evaluations. On MP-20, the authors report that one step matches CrystalFlow accuracy with far fewer evaluations, while additional steps improve accuracy further. The result shifts the emphasis in crystal generation from peak accuracy alone to useful accuracy per network evaluation.

AI–materials Generative design
Abstract brief CC0 Preprint
#03 arXiv v1 / preprint

Preprint—not peer reviewed

Dynamic language model representations for multi-objective reaction optimisation

Joshua W. Sin, David Ming Segura, Bojana Rankovic, Siu Lun Chau, Marius D. R. Lutz, Andrea Anelli, Ryan P. Burwood, Kurt Puntener, Maximilian J. Notheis, Raphael Bigler, Philippe Schwaller

The method learns a reaction representation from textual condition descriptions with a fine-tuned language model and Gaussian-process surrogates in multi-objective Bayesian optimization. In prospective studies, the authors report that high-throughput experiments produced conditions translating directly to gram scale with high isolated yields and enantiomeric excess. The approach treats the description of a reaction system as a learned experimental representation rather than a fixed descriptor library.

AI–chemistry Reaction modeling
Abstract brief CC0 Preprint
#04 arXiv v1 / preprint

Preprint—not peer reviewed

Neural-Network Solutions to Real-Space Charge Density and Generalization

Yuxuan Zeng, Taoyuze Lv, Zhicheng Zhong

AIDEN separates element-dependent one-center charge density from environment-induced redistribution. The authors report state-of-the-art accuracy on periodic crystal benchmarks, competitive molecular performance, and zero-shot transfer across structurally distinct out-of-distribution cases. It advances charge-density surrogates by separating transferable local structure from the spatial grid used to evaluate the field.

AI–chemistryAI–materials Electronic structure
Abstract brief CC0 Preprint
#05 arXiv v1 / preprint

Preprint—not peer reviewed

pyeCE: A Python Implementation of the Embedded Cluster Expansion

Yann L. Muller, Claire A. Paetsch, Anirudh Raju Natarajan

pyeCE implements embedded cluster expansion, using machine learning to map many chemical species onto a smaller set of effective species. The authors report that a model spanning the full composition space of a nine-component refractory alloy resolves short-range order and order-disorder behavior. This extends a familiar thermodynamic modeling framework toward alloy spaces whose chemical complexity normally makes conventional cluster expansion impractical.

AI–materials Materials representation
Abstract brief CC0 Preprint
#06 arXiv v1 / preprint

Preprint—not peer reviewed

MIRAGE: Measuring Interpolation and Redundancy in Affinity GEneralization

Mehdi Yazdani-Jahromi, Sanjay Padhi, Ivan Garibay

MIRAGE benchmarks affinity and pose models across an explicit axis of historical protein-family support, using matched strata, family-disjoint controls, and temporal evaluation. The authors report that rankings reverse on novel families, where a family-disjoint random forest leads both co-folders and significantly outperforms Nesso-1. This makes training-set redundancy a measurable part of the claim of generalization, rather than a hidden feature of a pooled benchmark.

AI–biochemistry Datasets + benchmarks
Abstract brief CC0 Preprint
#07 arXiv v1 / preprint

Preprint—not peer reviewed

QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules

Michael Hanna, Julian Cremer, Zekiye Erarslan, Leonardo Medrano Sandonas

QALPA combines an E(3)-equivariant diffusion model, active learning, and efficient quantum-mechanical methods to explore targeted property manifolds for flexible molecules. The authors report that coupling QALPA to EquiDTB augmented alloQM, a dataset of 6,253 allosteric-drug conformers, by filling sparse regions defined by dispersion energy and the HOMO-LUMO gap. This gives generative sampling a physics-based route to extend sparse quantum datasets without treating larger flexible molecules as a separate regime.

AI–chemistry Generative design
Abstract brief CC0 Preprint
#08 arXiv v1 / preprint

Preprint—not peer reviewed

Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials

Bilvin Varughese, Aditya Koneru, Adil Muhammad, Troy D. Loeffler, Sukriti Manna, Jan Michael Y. Carrillo, Orcun Yildiz, Thomas Peterka, Subramanian K.R.S. Sankaranarayanan

The authors train equation-learner neural networks on density-functional data and combine their symbolic forms in a weighted ensemble potential for aluminum. The authors report that all symbolic models reach sub-10 meV per atom accuracy and that their ensemble improves consistency with density-functional benchmarks for phonons, equation-of-state curvature, and melting dynamics. The result keeps a compact analytical form while using diversity among learned models to strengthen prediction beyond equilibrium structures.

AI–materials Neural potentials
Abstract brief CC0 Preprint
#09 arXiv v1 / preprint

Preprint—not peer reviewed

Can Autonomous LLM Agents Execute Multireference Quantum Chemistry Calculations?

Victor Chang Lee, James M. Rondinelli

The autonomous agent selects active spaces, submits ORCA calculations, analyzes outputs, and records its choices in an auditable reasoning log. With a structured decision ladder, the authors report increased coverage of vertical transition energies alongside a lower mean absolute error. It clarifies that reliable autonomy in multireference chemistry depends on making expert workflow decisions explicit and checkable.

AI–chemistry Electronic structureScientific agents
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation

Tianren Zhang

PolyLatentFlow uses continuous-time flow matching in latent space for conditional polymer generation and pairs it with the sequence-and-structure representation LlamaUni. For carbon-dioxide and nitrogen conditioning, the authors report that PolyLatentFlow with LlamaUni achieved the largest per-attempt yield of nonreplayed target hits among the evaluated representations. The comparison shows that representation determines how a polymer generator balances target control against exploration beyond its labeled chemistry.

AI–materials Generative designMaterials representation
Abstract brief CC0 Preprint
#11 arXiv v1 / preprint

Preprint—not peer reviewed

Multi4D: an end-to-end neural network for structural determination at complex material interfaces

Haoran Zhang, Zian Mao, Shufen Chu, Xiaoya He, Yuyan Guan, Antong Yang, Mingze Li, Xiaoqin Zeng, Yujun Xie

Multi4D combines a latent-space Diffusion Transformer with a rotation-invariant convolutional network for diffraction datasets. The authors report high classification accuracy and apply the method to superconducting heterostructures, corroded alloy surfaces, and degraded solid-state battery interfaces at single-nanometer resolution. It joins automated diffraction interpretation to a local measure of structural ambiguity at interfaces where degradation begins.

AI–materials Materials representation
Abstract brief CC0 Preprint
#12 arXiv v1 / preprint

Preprint—not peer reviewed

Multimodal deep learning from spectra for small-molecule structure identification: enhancing robustness with mixed-condition training

Bowen Gao, Lei Zhu, Yiying Wang, Wenjie Yu

The study trains a mixture-of-experts reranker under mixed spectroscopic conditions, including modality-specific perturbations and chemically informed spectrum replacements. Across a large test set spanning predefined conditions, the authors report that mixed-condition training raised the mixture-of-experts mean reciprocal rank. The contribution is a more realistic definition of robustness for structure identification when the available spectra are incomplete or inconsistent.

AI–chemistry Molecular representation
Abstract brief CC0 Preprint
#13 arXiv v1 / preprint

Preprint—not peer reviewed

Are You Learning Biological Signal or Shortcuts? Auditing and Mitigating Bias in Protein-Protein Interaction Datasets

Judith Bernett, Anton Spannagl, Joel \AAs, Markus List, David B. Blumenthal

The authors audit protein-protein interaction datasets with similarity-aware splitting and bias-minimizing negative sampling, both posed as integer linear programs. They report that removing train-test protein overlap still leaves shortcuts from self-interactions, taxonomic identity, and functional relatedness, with prevalence that depends on the data source. The study supplies a general way to quantify and reduce dataset shortcuts before a model’s apparent biological signal is taken at face value.

AI–biochemistry Datasets + benchmarks
Abstract brief CC0 Preprint
#14 arXiv v1 / preprint

Preprint—not peer reviewed

Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion

Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M. Merz, Jr., Joseph A. Morrone

GRACE adapts a pretrained geometry encoder through residual learning against an adduct-aware physical baseline and encoder-level adduct conditioning. Across random, scaffold, and adduct-sensitive splits, the authors report the best mean percentage difference among the evaluated learned models. It identifies residual learning and early adduct integration as concrete design choices for collision-cross-section prediction that must generalize beyond familiar scaffolds.

AI–chemistry Molecular representation
Abstract brief CC0 Preprint
#15 arXiv v1 / preprint

Preprint—not peer reviewed

El Agente Potente: High-Throughput Agentic Atomistic Simulations

Tsz Wai Ko, Jiaru Bai, Thomas Swanick, Yeonghun Kang, Changhyeok Choi, Angelina Qihong Jiang, Aiwei Yin, Varinia Bernales, Alan Aspuru-Guzik

El Agente Potente combines typed execution graphs for standardized interatomic-potential workflows with a coding agent for procedures that require more flexibility. The authors demonstrate the system across materials discovery, molecular energy landscapes, adsorption, and catalytic reaction workflows, alongside reproducibility and token-cost benchmarks. The division of planning from deterministic computation makes agentic atomistic simulation easier to audit while leaving room for custom workflows.

AI–chemistryAI–materials Scientific agents
Abstract brief CC0 Preprint
#16 arXiv v1 / preprint

Preprint—not peer reviewed

Ensemble-Conditioned Molecular Design

Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson

The authors introduce ensemble-conditioned guidance, jointly conditioning a 3D molecular generator on molecular modes and ensemble properties. The authors report that, in dual-target binder and active-state-selective agonist design, adding the additional state improved the desired outcome over single-state conditioning. That makes the conformational ensemble, rather than an isolated conformer, an explicit variable in molecular design.

AI–chemistry Generative design
Abstract brief CC0 Preprint
#17 arXiv v1 / preprint

Preprint—not peer reviewed

Navigating Sparse Singlet Fission Chemical Space: An Intelligent Generative-Predictive Paradigm

Longfei Lv, Li Fu, Si Zhou, Lingzhi Zhao, Jijun Zhao

The authors couple a structure generator, property predictor, and multi-criteria validation workflow for inverse design of singlet-fission molecules. The authors report a high success rate for candidates after evaluating a large generated set and validating a random subset with time-dependent density functional theory. The study offers a route through a sparse excited-state search space while retaining a quantum-chemical check on generated candidates.

AI–materials Generative design
Abstract brief CC0 Preprint
#18 arXiv v1 / preprint

Preprint—not peer reviewed

Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning

Pavlo Golub, Libor Veis

The authors combine density matrix renormalization group calculations as a complete active-space solver with machine learning to evaluate electronic excitations. They demonstrate transferability on challenging polycyclic aromatic examples containing up to 34 pi electrons. This supplies a practical route to excited-state estimates for correlated materials that are expensive for conventional many-body calculations.

AI–chemistryAI–materials Electronic structure
Abstract brief CC0 Preprint
#19 arXiv v1 / preprint

Preprint—not peer reviewed

Automated AFGL quantum number assignment for CO$_2$ isotopologues using a graph neural network

Marco G. Barnfield, Sergei N. Yurchenko, Jonathan Tennyson

The pipeline trains a transductive GraphSAGE network on empirical energy levels to propagate assignment information to unlabelled calculated states. The authors report assignments for previously unlabelled states across carbon-dioxide isotopologues, increasing coverage at low energy. The workflow points to automated annotation of large line lists when symmetry-like constraints can be stated directly in the assignment problem.

AI–chemistry Molecular representation
Abstract brief CC0 Preprint Published DOI
#20 arXiv v1 / preprint

Preprint—not peer reviewed

Scaling LLM Agents for Materials Design through Hierarchical Collective Reasoning

Jaehwan Choi, Yousung Jung

HiMatGen organizes language-model agents into discussion pods and domain representatives that exchange evidence and unresolved questions during crystal design. Across six chemical systems, the authors report more final crystal candidates and more stable, unique, and novel structures than a single-agent baseline. Its value lies in carrying scientific disagreement and computational evidence through an iterative materials-design process.

AI–materials Generative designScientific agents
Abstract brief CC0 Preprint