Preprint scanner project / Issue 2026-09-02

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 / 111 reviewed Aug 26, 2026–Sep 1, 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 2026-09-02

Highlights from this week

#01 arXiv v2 / preprint

Preprint—not peer reviewed

uMOF: A Universal Database, Benchmark, and Machine Learning Interatomic Potentials for Metal-Organic Frameworks

Theo Jaffrelot Inizan, Prathami Divakar Kamath, Alin Marin Elena, Kristin A. Persson

The authors release uMOF, combining a density functional theory dataset, a literature-mined benchmark, and universal metal-organic framework potentials. They report that uMOF models outperform tested baselines for dynamics-sensitive adsorption properties and reduce error by more than 80% to within experimental uncertainty. The package links broad physically diverse training data to experimental benchmarks for transferable metal-organic framework simulation.

AI–materials Datasets + benchmarksNeural potentials
Abstract brief CC0 Preprint
#02 arXiv v2 / preprint

Preprint—not peer reviewed

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, Philippe Schwaller

The authors formulate reaction prediction as discrete flow matching over graph-structured electron occupation vectors in a continuous-time Markov chain. On USPTO-480K and the stated out-of-distribution settings, they report competitive prediction, retained performance, mechanism-like trajectories, and side-product prediction. Electron redistribution offers a mechanistically legible alternative to product generation and direct graph edits.

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

Preprint—not peer reviewed

Cartesian tensor equivariant machine-learning force field for spin-dependent atomistic simulations

Junjie Wang, Yijie Zhu, Zhongwei Zhang, Zhiyue Guo, Xudong Zhu, Lixin He, Chi Ding,, Jian Sun

The authors build HotPP-Spin with Cartesian tensor equivariant message passing and explicit axial-vector magnetic moments. For H-phase monolayer VSe2, they report a finite-size ordering crossover at 415--435 K, close to the reported experimental value. This provides one representation for connecting first-principles magnetic energetics to coupled spin and structural simulations.

AI–materials Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#04 arXiv v1 / preprint

Preprint—not peer reviewed

A Generalized Approach for Incorporating Geometry and Directionality into Coarse-Grained Machine-Learned Potentials

Arthur Y. Lin, Tejas Dahiya, Rose K. Cersonsky

The authors add molecular geometry and directionality to coarse-grained potentials through anisotropic descriptors and symmetry-adapted message passing. Using Gay-Berne particles and benzene, formamide, and water representations, they report improved energy, force, and torque prediction. The work identifies retained symmetry and geometry as part of the information budget in coarse-grained models.

AI–chemistry Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#05 arXiv v1 / preprint

Preprint—not peer reviewed

AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

Prajwal Ananth, Shuwen Yue

The authors introduce AdaptNTK, which measures uncertainty as a regularized Mahalanobis distance in empirical neural tangent kernel feature space. On held-out rMD17 data, they report force-error correlations of 0.68 and 0.71 and a 2.6-fold speedup per Transition-1X cycle. Sequential uncertainty updates offer a route to less redundant acquisition without retraining after each selection.

AI–chemistry Neural potentials
Abstract brief CC0 Preprint
#06 arXiv v1 / preprint

Preprint—not peer reviewed

Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning

Yunzhe Jia, Fankai Xie, Yunfei Bai, Miao Liu, Cui Zhang, Sheng Meng

The authors develop excited-state machine-learning molecular dynamics calibrated against real-time time-dependent density functional theory benchmarks. They report that large-scale simulations resolve phonon competition in bismuth phase transition and structural rearrangement in selenium photoamorphization. This extends learned dynamics toward complex photoexcited materials where electron-nuclear evolution matters.

AI–materials Atomistic modeling
Abstract brief CC0 Preprint
#07 arXiv v2 / preprint

Preprint—not peer reviewed

Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling

Yifan Feng, Guanjie Cheng, Shihui Ying, Shaoyi Du, Yue Gao

The authors introduce Hyper-Fold, a rank-K separable convolutional backbone that organizes each radius neighborhood into sequence and contact hyperedges. Across enzyme function, fold classification, and binding-site tasks, they report leading protein-encoder results and lower parameter count and latency for Hyper-Fold-Pocket. The work argues that expressive content-geometry interactions can recover information often attributed to large evolutionary pretraining.

AI–biochemistry Biomolecular structure
Abstract brief CC0 Preprint
#08 arXiv v1 / preprint

Preprint—not peer reviewed

Fourier Neural Operators for Composition-Driven Crystal Structure Discovery

Zhijie Yu, Jingyu Li, Yang Huang, Jingrun Chen

The authors introduce a Fourier Neural Operator crystal-field solver that maps chemical formulae and lattice parameters to periodic density fields. They report novel structures across 104 chemical formulae with competitive reconstruction accuracy, generative diversity, and structural validity. This couples composition-conditioned generation to a reconstruction and screening path for crystal discovery.

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

Preprint—not peer reviewed

Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

Weize Mao Yuan Chen, Dongbin Xiu

The authors approximate the finite-time transition kernel of a stochastic reaction-network Markov chain with a conditional normalizing flow. Their numerical examples report statistically consistent coarse-step trajectories with reduced computational cost. A learned stochastic propagator can decouple useful simulation steps from microscopic reaction-event resolution.

AI–chemistry Surrogate modeling
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning

Wei-Jian Jiang, Ye-Nan Sha, Hui Guo, Jie Chen, Yu-Cai Liang, Ke Zhou, Qi-Long Gao, Dong-Lin Han, Xin-Gao Gong, Wan-Jian Yin

The authors develop PIRAG-LM, which retrieves synthesis precedents by chemical, structural, and thermodynamic similarity before structured route reasoning. They report 91.4% synthesis-method prediction accuracy, compared with 72.1% for the language model alone, and experimental synthesis of five new compounds. Retrieval gives the planning system an interpretable path from materials discovery to experimental realization.

AI–materials Scientific agentsSynthesis planning
Abstract brief CC0 Preprint
#11 arXiv v1 / preprint

Preprint—not peer reviewed

Compiling Chemical Knowledge into Executable Descriptors for Materials Prediction

Jaehwan Choi, Kunik Jang, Seongmin Kim, Shuan Chen, Kyungju Nam, Seung Hyo Noh, Donghwi Kim, Yousung Jung

The authors introduce CRISP, which samples chemical rules, consolidates them, and compiles each into an executable scalar descriptor for a conventional learner. For inorganic-crystal synthesizability, they report stronger results than expert-curated and generic representations under a shared learner, especially under structural-size and chemical-family shifts. The method makes chemical heuristics auditable computational features without relying on structures, labels, or data splits during rule construction.

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

Preprint—not peer reviewed

Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation

Tianyu Gao, Zhikai Su, Jiashu Li, Wenjun Gao, Zichuan Ying, Zhe Zhao, Fei Zhang, Ye Wei

The authors use a language-informed flow-matching framework in which target-aware SMILES supply semantic priors to geometric molecular generation. On Cross-Docked2020, they report competitive distribution matching with improved medicinal chemistry metrics and competitive structural validity under task steering. The approach tests whether language-derived chemical context can guide three-dimensional design without further generator fine-tuning.

AI–chemistry Foundation modelsGenerative design
Abstract brief CC0 Preprint
#13 arXiv v1 / preprint

Preprint—not peer reviewed

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu

The authors introduce S3C-LLM, which retrieves spectroscopy skills, runs analysis code, and integrates peak-level evidence before generating SMILES. Across diverse benchmarks, they report that S3C-LLM outperforms general and spectrum-specific models while using less than 1/10th of SpectraLLM training data. The workflow places analytical constraints inside spectrum-to-structure prediction rather than treating it as direct generation.

AI–chemistry Molecular representationScientific agents
Abstract brief CC0 Preprint
#14 arXiv v1 / preprint

Preprint—not peer reviewed

RegimeFormer: A Large Protein Model of Global Perturbation Regimes

Siyuan Ma, Yi Chai, Yi Wu, Qixin Zhang, Yajing Yuan, Kanglu Zhao, Zhikang Chen, Haowei Wang, Shuying Cao, Xiaolei Yu, Xiangfei Han, Yun Liu, Yang Liu, Tingting Zhu, Dacheng Tao

The authors couple RegimeFormer with RegimeAtlas to model protein perturbation regimes across a harmonized global sequence collection. Across deep mutational scanning and molecular benchmarks, they report reproducible regimes and improved substitution-specific prediction under unseen-protein, unseen-family, and low-homology evaluation. The framework offers a scalable way to map and query mutation response across protein sequence space.

AI–biochemistry Foundation modelsProtein engineering
Abstract brief CC0 Preprint
#15 arXiv v1 / preprint

Preprint—not peer reviewed

Accelerating Chemical Kinetics for Exoplanet Atmospheres using Neural Networks

Isaac Malsky, Xi Zhang, Tiffany Kataria, Matthew Graham, Ziyu Huang, Boris Bonev, Shang-Min Tsai,, Elspeth K.H. Lee

The authors develop a residual flow-map neural solver for local-box chemical kinetics in exoplanet atmospheres. They report microsecond-scale inference, percent-level accuracy, and robustness under the extreme stiffness of atmospheric chemistry. A fast surrogate could let atmospheric models retain kinetic effects that equilibrium approximations miss.

AI–chemistry Surrogate modeling
Abstract brief CC0 Preprint
#16 arXiv v1 / preprint

Preprint—not peer reviewed

SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang

The authors use a symmetric dual-path architecture that iteratively combines protein language and multimodal protein language knowledge for sequence generation. Across standard inverse-folding benchmarks, they report state-of-the-art performance and ablations supporting the symmetric design. The design directly addresses the limits of post-hoc sequence refinement in inverse folding.

AI–biochemistry Biomolecular designProtein engineering
Abstract brief CC0 Preprint
#17 arXiv v2 / preprint

Preprint—not peer reviewed

Text-guided flow matching enables sample-efficient crystal structure generation

Wentao Li

The authors introduce TFMat, which conditions a CrystalFlow generator on structured materials language as a semantic prior. Across the stated crystal benchmarks, they report a 92.04% MP-20 match rate with 20 candidates and improved alignment in de novo generation. The result makes materials language an inspectable control layer before downstream simulation and validation.

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

Preprint—not peer reviewed

GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion

Kwanyoung Kim

The authors introduce GRAS, which lowers guided-proposal variance and uses an adaptive resampling temperature for training-free discrete diffusion steering. Across regulatory DNA and protein design, they report the best training-free reward and performance that matches or exceeds a reward-fine-tuned model. The analysis isolates a compact change to inference-time search that remains effective for non-differentiable rewards.

AI–biochemistry Protein engineering
Abstract brief CC0 Preprint
#19 arXiv v1 / preprint

Preprint—not peer reviewed

Learning Interpretable Tumor Microenvironment Representations by Fitting Pan-Cancer Cell State-Niche Correlation

Xiao Xiao, Jiashu He, Shiyang Zhang, Meiyi Mao

The authors introduce GITIII-scale, a hierarchical interpretable spatial transcriptomics model that decomposes cell state-niche associations and ligand-receptor pathways. On cancer types unseen in training, they report embeddings that recover niche-associated state changes more accurately than existing spatial transcriptomics foundation models. The model ties pan-cancer representation learning to biological mechanisms that can be inspected for drug-target hypotheses.

AI–biochemistry Foundation models
Abstract brief CC0 Preprint
#20 arXiv v1 / preprint

Preprint—not peer reviewed

Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures

Manasa Kaniselvan, Mauro Dossena, Denghui Lu, Alexander Maeder, Nicolas Vetsch, Alexandros Nikolaos Ziogas,, Mathieu Luisier

The authors integrate machine-learned electronic structures with a quantum transport solver for semiconductor device simulation. They report 10,000X speedups over density functional theory for devices above 20,000 atoms and identify an effect of undercoordinated Hf or Al atoms on MoS2 current. Explicit oxide layers can therefore enter transport calculations at device-relevant scales.

AI–materials Electronic structure
Abstract brief CC0 Preprint