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

ATLAS trains an equivariant diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. The authors report less than 0.2% free-energy error in a low-temperature glass regime with more than 500-fold fewer energy evaluations than parallel tempering. A sampler that transfers across size, temperature, and composition could make amorphous-material thermodynamics and inverse design substantially more tractable.

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 builds a provenance-aware resource linking publications, patient single-cell RNA sequencing, and clinical metadata, then uses specialized language-model agents to generate, critique, and prioritize falsifiable hypotheses for computational and experimental validation. Applied to CAR-T patient evidence, the authors report that the workflow prioritized KLRC1/NKG2A and that in vitro and in vivo chronic-stimulation models linked NKG2A to exhaustion-associated CD8 CAR-T cells while blockade improved antitumor activity and persistence readouts. The platform connects fragmented clinical molecular evidence to testable engineering hypotheses and carries them through virtual perturbation, expert selection, and experimental validation.

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 combines a shared descriptor-free equivariant committor with a basin-restricted bias that leaves the reactive region unbiased. The authors report rates consistent with references and experiments across roughly 17 orders of magnitude, together with unbinding mechanisms reconstructed without additional sampling. Minimal system-specific setup makes the approach a plausible foundation for comparing structure-kinetics relationships across ligand series.

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 extends an equivariant graph network to predict Born effective charges alongside energy, forces, and stress. The authors report multitask errors of 1.0 meV per atom for energy, 12 meV per angstrom for forces, 0.05 GPa for stress, and 0.0029 e for charge. Fast charge-aware potentials make large-scale molecular dynamics under electric fields accessible without separating polarization from the underlying mechanics.

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 treats vector magnetic moments as continuous geometric degrees of freedom and couples a central spin representation to its local spin-lattice environment through an equivariant tensor product. The authors report competitive or improved accuracy on FeAl, CrN, and Fe benchmarks, high-fidelity phonon and magnon spectra for CrI3, and Curie temperatures for CrI3 and bcc Fe in good agreement with experiment. Encoding spin-lattice symmetry directly into the potential makes data-efficient simulation of coupled magnetic excitations and finite-temperature behavior possible within one framework.

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 maps language instructions, crystal and molecular encodings, protein sequences, spectra, and images into a shared representation, aligns that space with scientific laws and expert knowledge, and decodes it for domain-specific tasks. After training across 200 scientific tasks and evaluation on more than 60 benchmarks, the authors report that S1-Omni outperformed general-purpose comparison models on most tests and matched or exceeded specialized models on several. The common representation connects scientific understanding, prediction, and native generation across molecules, proteins, crystals, spectra, and images in one model.

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 unify independent protein-ensemble sampling, exact likelihoods, and differentiable coarse-grained energies and forces. The authors report close agreement with reference molecular dynamics, transfer beyond the training proteins, and substantially faster sampling than diffusion-based ensemble models. Using one learned density for both equilibrium samples and stable dynamics closes a longstanding divide between generative modeling 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 cheap per-configuration uncertainty estimate for machine-learning force fields. The authors report that LLPR-selected subsets recovered full-data accuracy across molecular, condensed-phase, and electrolyte systems using only a small fraction of the electronic-structure labels. A forward-pass uncertainty measure avoids the separate fine-tuning runs that make model committees impractical for foundation force fields.

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

The framework combines Adaptive Multilevel Splitting with Girsanov reweighting to propagate interatomic-potential parameter uncertainty into averaged committor probabilities without resampling reactive trajectories for each parameter realization. Across a Muller-Brown potential, a solvated dimer, and a butane conformational transition, the authors report recovery of reference rare-event probabilities and, under mild basin-accuracy assumptions, uncertainty bounds on reaction rates. Path-space reweighting provides uncertainty-aware committors and rate bounds from an existing rare-event trajectory ensemble, avoiding a new simulation for every potential realization.

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

PANO and CANO map full-field displacement measurements and net reaction forces directly to hyperelastic strain-energy density functions, using Laplacian eigenfunctions and physically admissible output constraints. The authors report near-instantaneous constitutive inference in one forward pass and evaluate the operators on unseen, noisy, incomplete, differently discretized data and geometries of different sizes. The operator formulation makes constitutive discovery fast, discretization-independent, and constrained to physically admissible material responses.

AI–materials Surrogate modeling
Abstract brief CC0 Preprint