Preprint scanner project / Issue 2026-07-22

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.

10 selected / 181 reviewed Jul 15, 2026–Jul 21, 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 2026-07-22

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

ATLAS: A Foundation Neural Sampler for Amorphous Materials

Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du

The authors introduce ATLAS, a diffusion sampler that generates Boltzmann-distributed amorphous structures from a target energy function. In metallic glasses, the authors report recovering observed short-range-order trends while directing structures toward specified order parameters and bulk moduli. This approach could make equilibrium sampling and targeted amorphous-material design more tractable when direct simulation is costly.

AI–materials Foundation modelsGenerative design
Abstract brief CC0 Preprint
#02 bioRxiv v2 / preprint

Preprint—not peer reviewed

BioPathfinder: Evidence-guided multi-agent platform enables hypothesis discovery for CAR-T engineering

Wang, S., Li, Y.-R., Wang, Q., Yang, Y., Shen, X., Li, H., Nan, H., Chen, Z., Zhu, Y., Zhang, B., Ding, H., Soto, J., Park, S., Zheng, Y., Huang, X., Li, D., Li, S.

BioPathfinder links publications, patient single-cell sequencing datasets, and clinical metadata in a provenance-aware resource used by specialized language-model agents. The authors report that blockade of NKG2A enhanced antitumour activity and persistence-associated functional readouts in chronic-stimulation models in vivo. This workflow offers a structured way to turn fragmented clinical evidence into testable therapeutic mechanisms.

AI–biochemistry Scientific agents
Abstract brief CC BY Preprint
#03 arXiv v1 / preprint

Preprint—not peer reviewed

Accelerated descriptor-free path sampling for protein-ligand binding kinetics

Simon M. Lichtinger, Roberto Covino

The method learns a shared descriptor-free equivariant graph network for the committor and applies a static bias only within deep bound-state wells. Across host-guest and protein-ligand systems, the authors report recovering rates consistent with reference and experimental values over roughly 17 orders of magnitude in residence time. Restricting the bias away from the reactive region may help kinetics calculations retain mechanistic information while converging more readily.

AI–biochemistryAI–chemistry Atomistic modelingBiomolecular structure
Abstract brief CC0 Preprint
#04 arXiv v3 / preprint

Preprint—not peer reviewed

SevenNet-Polar for MultiTask Prediction of Energy, Forces, Stress, and Born Effective Charges: Development and Application to ZrO$_2$, Li$_3$PO$_4$, and Perovskites

Anh Khoa Augustin Lu, Shungo Arai, Yutack Park, Seungwu Han, Tsuyoshi Miyazaki, Satoshi Watanabe

SevenNet-Polar is an equivariant graph neural network for predicting Born effective charges rapidly alongside material structure. The authors report robust performance in structural environments absent from training, including transition trajectories and zirconia grain boundaries. Charge-aware predictions at this scale could broaden simulations of materials exposed to electric fields.

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

Preprint—not peer reviewed

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces

Yuanqing Gao, Wen-Hao Luo, Lei Zhang, Kun Cao

STEP represents vector magnetic moments as continuous geometric degrees of freedom within an equivariant magnetic interatomic potential. The authors report that spin-dynamics simulations produced Curie temperatures for monolayer CrI3 and body-centered-cubic iron consistent with experiments. Encoding spin and lattice behavior together could support more economical studies of temperature-dependent magnetic phenomena.

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

Preprint—not peer reviewed

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

Jiahao Zhao, Junyi Liu, Lifeng Xu, Nan Xu, Qingli Wang, Qingxiao Li, Tianle Chen, Xiaoyu Wu, Yawen Zheng, Zikai Wang, Guanming Liu, Hequn Zhou, Jingyi Wang, Jingyuan Shu, Keqi Wang, Li He, Songyang Diao, Wenhui Xu, Xinyu Ren, Yaqin Fan, Yujin Zhou, Zhanao Yao

S1-Omni places scientific instructions and objects such as molecular strings, protein sequences, spectra, and images in a shared representation space. The authors report training on a corpus spanning 200 scientific tasks and evaluating the model on more than 60 benchmarks. A common representation may be useful where scientific questions require several kinds of data rather than one specialized input.

AI–biochemistryAI–chemistryAI–materials Foundation modelsMolecular representation
Abstract brief CC0 Preprint
#07 bioRxiv v1 / preprint

Preprint—not peer reviewed

UniFlow: Unifying protein conformational ensemble generation and machine-learned force fields with a scalable normalizing Flow

Liu, Y., Chen, M., Lin, G.

UniFlow uses an internal-coordinate normalizing flow to sample protein ensembles, evaluate likelihoods, and calculate differentiable energies and forces. Across varied protein systems, the authors report ensembles close to molecular-dynamics references and faster sampling than diffusion-based baselines. Combining these functions in one learned density may simplify the connection between ensemble generation and molecular simulation.

AI–biochemistry Biomolecular structureNeural potentials
Abstract brief CC BY Preprint
#08 arXiv v1 / preprint

Preprint—not peer reviewed

Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

Sheng Bi, Yi-Ze Wang, Jun Cheng

The active-learning workflow uses last-layer-projection regression as a low-cost uncertainty estimate for each configuration. Across molecular, condensed-phase, and electrolyte systems, the authors report compact training sets that recovered full-data accuracy with far fewer electronic-structure labels. Efficient label selection could lower the cost of training and adapting machine-learning force fields while retaining a check on uncertain configurations.

AI–chemistryAI–materials Neural potentials
Abstract brief CC0 Preprint
#09 arXiv v2 / preprint

Preprint—not peer reviewed

Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics

Leonard Moracchini, Thomas Pigeon, Morgane Menz, Thibault Faney, Thomas D. Swinburne, Mihai-Cosmin Marinica

This framework combines rare-event sampling with Girsanov reweighting to assess how interatomic-potential parameters affect committor probabilities. The authors report recovery of reference rare-event probabilities from uncertain surrogate models across benchmark systems. Propagating uncertainty in trajectory space could make kinetic predictions more informative without requiring new reactive trajectories for every parameter choice.

AI–chemistry Atomistic modelingNeural potentialsReaction modeling
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

Neural operators solve inverse problems for constitutive model discovery

Moritz Flaschel, Burigede Liu, Ellen Kuhl

The authors report neural operators that infer a strain-energy density function from an experimental dataset in a single forward pass. They report tests on unseen, noisy, incomplete, differently discretized data and geometries of different sizes. This could make material characterization near-instantaneous after training.

AI–materials Surrogate modeling
Abstract brief CC0 Preprint