Preprint scanner project / Issue 2026-09-09

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 / 88 reviewed Sep 2, 2026–Sep 8, 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 2026-09-09

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design

Ge Sun, Gervasio Zaldivar, Yuan Tian, Gustavo Perez Lemus, Juhae Park, Daryna Safarian, Ming Han, Juan J. de Pablo

HiPoly processes complete polymer descriptions through a three-level hierarchical graph architecture that encodes connectivity, composition, and molecular weight. The authors report state-of-the-art thermophysical-property prediction for multicomponent polymer systems, with ablations supporting each hierarchical design choice. A shared polymer representation can connect formulation data, prediction, generative design, and simulation-based validation across polymer chemistries.

AI–materials Generative designMaterials representationMultiscale modeling
Abstract brief CC0 Preprint
#02 arXiv v1 / preprint

Preprint—not peer reviewed

La Agente \'Optima: Towards Agentic Self-Driving Laboratories

Marcel Muller, Jiaru Bai, Willi Gottstein, Abhijoy Mandal, Mohammad Nazeri, Elia Savino, Yanlin Fang, Sujoy Das, Sergio Pablo Garcia Carrillo, Yeonghun Kang, Juan B. Perez-Sanchez, Simone Pilon, Martin Fitzner, Timothy Noel, Frank Gu, Varinia Bernales, Alan Aspuru-Guzik

La Agente Optima constructs and supervises Bayesian optimization campaigns while maintaining persistent optimization state and separating reasoning from execution. In a multi-objective flow-chemistry campaign, the authors report increased yield over a sequence of experiments. Keeping routine loops executable and decisions auditable could make long-running optimization campaigns accessible without specialist campaign setup.

AI–chemistry Autonomous labsScientific agents
Abstract brief CC0 Preprint
#03 arXiv v2 / preprint

Preprint—not peer reviewed

Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential

Meng Gao, Armin Shayesteh Zadeh, Aniruddha Seal, Siva Dasetty, Siddarth K. Achar, Misko Dzamba, Benjamin K. Miller, Leif D. Jacobson, C. Lawrence Zitnick, Brandon M. Wood, Zachary W. Ulissi, Daniel S. Levine, Andrew L. Ferguson

The authors use the eSEN-omol machine-learned interatomic potential for complete all-atom enzymes in explicit solvent. They report experimental barrier trends for chorismate mutase and mechanistic alternatives for metal-activated phosphoryl transfer. The result points to a practical route for extending quantum-accurate catalytic simulations beyond system-specific hybrid setups.

AI–biochemistry Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#04 arXiv v2 / preprint

Preprint—not peer reviewed

Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

Weichi Yao, Cameron Gruich, Bryan R. Goldsmith, Yixin Wang

The authors introduce a two-stage generative framework that relies on a fixed-dimensional molecule-level latent representation to generate variable-size molecules. On PCQM4Mv2, the authors report the highest fraction of outputs that were unique, novel, sanitized, and passed PoseBusters checks. The architecture makes molecular size a generated consequence of the representation, which matters for open-ended property-directed design.

AI–chemistry Generative designMolecular representation
Abstract brief CC0 Preprint
#05 arXiv v1 / preprint

Preprint—not peer reviewed

Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space

Luis Itza Vazquez-Salazar, Tristan Bereau

Juniper frames compositional backmapping as discrete denoising diffusion over molecular graphs conditioned on octanol-water partition free energy. The authors report valid and unique molecules for two-bead targets whose partition-free-energy distributions track the coarse-grained targets linearly. The method turns a lossy coarse-grained screening result back into candidate molecules for atomistic study or synthesis.

AI–chemistry Generative designMolecular representation
Abstract brief CC0 Preprint
#06 arXiv v1 / preprint

Preprint—not peer reviewed

Out-of-Distribution Inverse Design of Elastic Networks with Differentiable Graph Neural Network Molecular Dynamics

Sergey A. Shteingolts, Salman N. Salman, Ron Levie, Dan Mendels

This inverse-design framework uses a graph neural network molecular-dynamics simulator, a short dynamical initialization, and physics-based refinement during simulation. A simulator trained on non-auxetic systems designed strongly auxetic networks, and the framework generalizes across system size. The combination of learned dynamics and physical refinement offers a way to optimize mechanical response beyond the distribution that supplied the training data.

AI–materials Generative designSurrogate modeling
Abstract brief CC0 Preprint
#07 arXiv v1 / preprint

Preprint—not peer reviewed

Scalable machine learning framework for multiphase identification from powder X-ray diffraction

Xinyang Tong, Ethan Jin, Jiahan Xu, Aditya Rao, Pengcen Jiang,, Nathan J. Szymanski

GALAXI assigns each candidate crystalline phase an independent binary classifier, then uses Rietveld refinement to choose the combination explaining the full pattern. On curated experimental patterns, the authors report correct phase identification and robustness to common diffraction artifacts. Independent phase models make a large reference library an incremental engineering problem rather than a reason to retrain a monolithic classifier.

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

Preprint—not peer reviewed

Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling

Frank Hu, Shriram Chennakesavalu, Zichen Wang, Patricia Suriana, Bodhi Vani, Kirill Shmilovich, Kangway Chuang, Colin Grambow

The authors train molecular-design language models through a curriculum of synthetic tasks that gradually becomes more challenging. They report that this curriculum surpassed much larger frontier models on structure-based lead optimization. Synthetic tasks could let post-training teach useful search strategies when direct chemical scoring is too costly for online optimization.

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

Preprint—not peer reviewed

TSBench: A physics-grounded benchmark for evaluating LLM understanding of chemical reaction mechanisms

Xiaohu Xu, Tong Zhu

TSBench asks an LLM agent to use structure-editing tools to build three-dimensional transition-state guesses checked by an automated quantum-chemical pipeline. Across 546 evaluations, the authors report that diagnosis-driven revision raised the aggregate success rate. The benchmark ties mechanistic language to a reaction path, giving claims about chemical reasoning a physical pass-or-fail test.

AI–chemistry Datasets + benchmarksReaction modelingScientific agents
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

Rishabh Arora, Lisa Scheunemann, Tim Brepols, Shahed Rezaei

The framework learns a material operator from full strain histories to stress trajectories, with causally masked attention restricting access to past states. Across rate-independent material models, the authors report accurate and robust predictions of irreversible deformation with resolution invariance and parallel efficiency. A full-path operator offers a direct representation of material history when the internal variables needed by classical constitutive models are unavailable.

AI–materials Multiscale modelingSurrogate modeling
Abstract brief CC0 Preprint
#11 arXiv v1 / preprint

Preprint—not peer reviewed

Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization

Dasol Yoon, Poompol Buathong, Chia-Hao Lee, Yujia Zhang, David A. Muller, Peter I. Frazier

SBOCF exploits the composite image-matching objective and intermediate simulated-image information for inverse problems in electron microscopy. Using patch-level summaries and correction terms, the authors preserve the pixel-wise objective while reducing the number of modeled outputs. The approach shows how image structure can reduce simulator demands without substituting a broad pretrained network for the scientific objective.

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

Preprint—not peer reviewed

SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign

Jiarui Lu, Yuyang Wang, Yizhe Zhang, Jiatao Gu, Navdeep Jaitly, Joshua M. Susskind, Miguel Angel Bautista

SimpleDesign trains sequence and structure directly in data space with a single end-to-end objective combining sequence cross-entropy and structural regression. Trained on over 2M sequence-structure pairs, the model achieved strong performance across co-design and unconditional generation benchmarks, the authors report. The result argues that protein co-design need not pass through separately trained latent representations to obtain broad generative performance.

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

Preprint—not peer reviewed

Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

Bumju Kwak, Jeonghee Jo

The authors introduce isotropic and normal-mode-weighted displacement schemes that derive conformation augmentation from Hessian information through Taylor expansions. Across equilibrium and non-equilibrium datasets, they report improved accuracy where the reference forces are large. The augmentation targets force-rich regimes without changing a potential architecture or its training objective, making Hessian-aware training more portable.

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

Preprint—not peer reviewed

NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer

Roxane Axel Jacob, Daniel Rose, Thierry Langer, Johannes Kirchmair

NEAT-POCKET generates molecules atom by atom in protein-pocket environments while preserving atom permutation invariance and modeling hydrogens explicitly. On CrossDocked and SPINDR, the authors report competitive structure-based generation with substantially faster sampling than existing baselines. A pocket-conditioned generator that also completes fragments brings the same representation close to lead optimization and scaffold elaboration.

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

Preprint—not peer reviewed

The energetics of force errors in machine-learned molecular dynamics

Peng Kang, Da Wan, Shulin Bai, Vincent Michaud-Rioux, Zhen Li, Yu Liu, Lei Zheng, Li-Dong Zhao

The study defines a directional residual-work coefficient that combines curvature mismatch with the spatial distribution of residual force response. At a lithium-electrolyte interface, predictions fixed before future reference evaluations differ from measurements of predicted work. By resolving force error along motion, the framework gives potential assessment and adaptive reference allocation an energetic basis.

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

Preprint—not peer reviewed

Embedded Graph Flows for Categorical Graph Generation

Ethan Ma, Zihan Wang, Chris Siu Yeung Chow, Xinguo Feng, Qingqing Li, Rui Jiang, Naipeng Dong, Guangdong Bai

Embedded Graph Flows learns continuous node and unordered-edge embeddings, then transports Gaussian noise toward them with a permutation-equivariant graph transformer. On QM9, the authors report the best result among the compared methods on every reported metric. Learning category geometry instead of fixing one-hot distances gives graph generation a representation that can respect order-invariant molecular structure.

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

Preprint—not peer reviewed

MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials

Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama, Yuta Tsuboi

MLIP Detective turns benchmark evidence into falsifiable physics-informed failure hypotheses, then screens inexpensive simulations before escalation to experts. Without issue-specific prompting, the authors report a systematic anomaly for some oxygen- or fluorine-containing adsorbates on surfaces. That makes evaluation an active search for consequential failures, rather than a scorecard limited to the configurations already in a benchmark.

AI–materials Atomistic modelingScientific agents
Abstract brief CC0 Preprint
#18 arXiv v1 / preprint

Preprint—not peer reviewed

Molecular D\'ej\`a Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Matthias Busch, Marius Tacke, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Christian J. Cyron, Roland C. Aydin, Christian Feiler

The study audits frontier language models on molecular regression benchmarks for verbatim retrieval of published values. The authors report that changing the reasoning level changes retrieval even for the same molecules and prompts. This separates apparent property prediction from memorized numerical recall, a necessary distinction when using language models as scientific regressors.

AI–chemistry Datasets + benchmarksFoundation models
Abstract brief CC0 Preprint
#19 arXiv v1 / preprint

Preprint—not peer reviewed

ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert Routing

Mingrui Li, Sixian Shen, Minzhang Li, Ruiyi Zhang, Kexin Zhang, Jiakai Zhang, Jingyi Yu

ProtLingo augments a pretrained single-sequence protein backbone with conditional local memory and sparse expert routing. Experiments across fitness, FLIP, and contact-prediction tests report competitive performance with a 150M-scale backbone. The design tests whether reusing local sequence context and selectively activating parameters can preserve mutation-sensitive and long-range representations at modest scale.

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

Preprint—not peer reviewed

Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators

Han Zhang, Alexander Ogren, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson

The work combines Fourier neural operators with wavelet encodings to learn multiple eigenmodes of the elastic wave equation. For metamaterial design, the authors report a three-order-of-magnitude simulation acceleration relative to finite element analysis while retaining high fidelity. It identifies input encoding as part of the solution to mode selection in spectral neural operators, a recurring obstacle in multi-mode PDE problems.

AI–materials Surrogate modeling
Abstract brief CC0 Preprint