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  • 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 ↗

Search results

Archive matches

151 arXiv v1 / preprint

Preprint—not peer reviewed

Diagnosing and narrowing the simulation-to-real gap in powder X-ray diffraction with a wet-dry agentic loop

Shaoguang Wang, Weiyu Guo, Ben Fei, Xiaohong Shao, Zhihui Wang, Wanli Ouyang

Xtalyst combines real-spectrum fine-tuning, peak-aligned reranking, and recalibration in an agent-orchestrated PXRD analysis system for phase identification, refinement, and property prediction. The authors report that correcting a small peak-position drift more than doubled median retrieval correlation and that a wet-dry recommend-rescan-reanalyze loop flipped a blinded silicon standard to a gated PASS. The work treats the simulation-to-real gap as a structural calibration problem inside the experimental loop rather than a generic denoising exercise.

AI–materials Scientific agents
Issue 2026-08-26 →
152 bioRxiv v2 / preprint

Preprint—not peer reviewed

A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models

Li, L., Duan, S., Zha, X., Ye, F., Zhang, Y., Zhang, X., Cao, Y., Liu, C., Fang, B.

scDrugPerturb-Bench links matched control and drug-treated single-cell profiles to literature-curated directional key-gene evidence, then measures mechanism fidelity with a composite score. The authors report that expression-similarity metrics aligned weakly with this score across 12 models, 3 baselines, and 10 data splits, while mechanism-aware selection improved early drug retrieval. The benchmark directs evaluation toward whether a model preserves drug-response signatures rather than only reconstructing expression.

AI–biochemistry Datasets + benchmarks
Issue 2026-08-26 →
153 ChemRxiv v1 / preprint

Preprint—not peer reviewed

Agentic AI in Process Analytical Technology: LLM Assistants for Chemometric Workflows

Jan G. Rittig, Luise F. Kaven, Philippe Schwaller

The PAT agents combine LLMs, chemometric software, and a multi-agent orchestrator in a workflow that translates user requests into reviewable analyses. The authors report 93–96% pass rates across nine Raman tasks while grounding numerical claims in the outputs of the analytical tools. This capability could make advanced process analytics more accessible while retaining structured logs and the expert oversight needed for industrial use.

AI–chemistry Scientific agents
Issue 2026-08-19 →
154 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
Issue 2026-09-09 →
155 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
Issue 2026-09-09 →
156 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
Issue 2026-09-09 →
157 bioRxiv v1 / preprint

Preprint—not peer reviewed

Coevolution-informed Bayesian optimization for sample-efficient protein design

Prasanna, D., Shukla, D., Potoyan, D. A.

ALSEBO couples Bayesian optimization to a generative protein-sequence landscape using direct-coupling-analysis coevolutionary features. The authors report reaching the avGFP optimum in about forty evaluations and transferring the approach to divergent GFPs and a non-GFP enzyme. This matters because a better low-data fitness representation can reduce the experimental cost of protein design.

AI–biochemistry Biomolecular designGenerative designProtein engineeringSurrogate modeling
Issue 2026-08-12 →
158 arXiv v1 / preprint

Preprint—not peer reviewed

Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference

Blazej Banaszewski, Andrew W. Fitzgibbon

Monroe is a molecular foundation model pretrained on more than 81 million PM6 molecules, with stereochemistry-aware graph representation, multi-task learning, and TabPFN-based downstream prediction. The authors report that it matches or exceeds existing molecular foundation models on Polaris benchmarks and significantly improves on activity-cliff benchmarks. The combination of pretraining and adaptable downstream prediction may be especially useful where structure–activity changes are sharp and data remain limited.

AI–chemistry Foundation modelsMolecular representation
Issue 2026-08-26 →
159 arXiv v1 / preprint

Preprint—not peer reviewed

Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules

Florian Rottach, Sebastian Schieferdecker, William Rudman, Randall Balestriero, Carsten Eickhoff

Mol-JEPA learns molecular representations with modality masking across structures, cellular phenotypes, binding affinities, ADMET profiles, quantum-chemistry simulations, and other drug-discovery data. The authors report strong representation performance across multiple benchmarks. Incorporating biochemical context through latent-space prediction could make molecular models less dependent on a single data modality.

AI–chemistry Foundation modelsMolecular representation
Issue 2026-08-26 →
160 arXiv v1 / preprint

Preprint—not peer reviewed

Structure-Agnostic Prediction of the Electronic Density of States with a Chemical Language Model

Ivan D. Rubtsov, Ivan V. Dudakov, Vadim V. Korolev

DOSSIER is a chemical language model that predicts electronic density of states directly from elemental composition, with an encoder pretrained by distillation from an interatomic potential. The authors report a mean absolute error of 3.76 states eV−1 on Mat2Spec, close to the best structure-aware model at 3.64, and favorable placement of known oxygen-reduction electrocatalysts in a composition screen. Predicting spectra before crystal structures are known could broaden electronic-structure screening across unsynthesized compositions.

AI–materials Electronic structure
Issue 2026-08-26 →
161 arXiv v1 / preprint

Preprint—not peer reviewed

Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric

Barbara Sumic, Ria Vasdev, Sudheesh Kumar Ethirajan, Jing Yang, Clotilde S. Cucinotta, Richard G. Hennig, Karsten Reuter, Stefan Ringe, Mira Todorova, Christoph Freysoldt, Jorg Neugebauer

EPhEct is a focused test suite that probes machine-learned interatomic potentials for electrochemically relevant phenomena beyond aggregate energy and force errors. The authors report that its cases test image-charge attraction, screening through optical-phonon splitting, interfacial-water dipoles, and Fermi-level pinning during ion discharge. Such diagnostics can expose physically consequential failures that an otherwise favorable error summary would leave hidden.

AI–materials Datasets + benchmarksNeural potentials
Issue 2026-08-19 →
162 arXiv v1 / preprint

Preprint—not peer reviewed

FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design

Guofeng Zhang, Rong Han, Xiaoyu Wang, Zhiyun Li, Zongbo Han, Xiaohong Liu, Guangyu Wang

FAR-DPO steers cyclic-peptide generators with feasibility-gated preference pairs and difficulty-aware group-robust optimization. The authors report fixed-budget success-rate gains from 46.89% to 57.79% on PepGLAD and from 47.96% to 49.57% on PepFlow. Putting feasibility directly into optimization could improve cyclic-peptide yield without relying primarily on post hoc filtering.

AI–biochemistry Biomolecular designGenerative design
Issue 2026-08-26 →
163 arXiv v1 / preprint

Preprint—not peer reviewed

Crystal-structure design by agentic AI in a language of motifs

Dinh-Khiet Le, Minh-Quyet Ha, Hong-Phuc Vu-Dinh, Takashi Miyake, Hiori Kino, Hieu-Chi Dam

MatEvolve represents crystals as human-readable motif profiles that an agent edits to propose candidates, which are then tested by first-principles calculation. The authors report that its rare-earth-lean magnet designs reached new structural prototypes more than three times as often as generative models under an equal validation budget. Linking proposals to editable structural motifs offers a route to design that is both generative and inspectable at the level of recurring geometry.

AI–materials Generative designMaterials representationScientific agents
Issue 2026-08-19 →
164 bioRxiv v1 / preprint

Preprint—not peer reviewed

De novo Design of Macrocyclic Molecular Glues

Brunner, A., Wierbilowicz, K., Daumiller, D., Bexell, D., Karlsson, K., Sangfelt, O., Bryant, P.

EvoBind-multimer designs macrocyclic peptide molecular glues directly from protein sequences, bridging specified protein pairs without prior interface knowledge or existing ligands. The authors report NanoBRET evidence of design-induced proximity for VHL with KRAS and BRD4, along with ternary complexes that drove ligase-dependent degradation and signaling shutdown. Sequence-only glue design could expand induced-proximity programs beyond retrospective optimization of serendipitous binders.

AI–biochemistry Biomolecular designGenerative design
Issue 2026-08-26 →
165 arXiv v1 / preprint

Preprint—not peer reviewed

Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential

Yucheng Ouyang, Xin Chen, Ying Liu, Lifang Wang, Xingyu Gao, Xiawei Du, Jianierken Habudelihan, Haifeng Song, Huimin Cui, Xiaobing Feng, Jingling Xue

The proposed machine-learned interatomic potential reduces feature-vector dimensionality with physical and chemical knowledge and eliminates intermediate tensors by kernel fusion. The authors report molecular dynamics of a six-component bulk system using 144 NVIDIA A100 GPUs. A substantially smaller memory footprint could make chemically heterogeneous bulk simulations accessible without the supercomputer scale previously associated with unary systems.

AI–materials Atomistic modelingNeural potentials
Issue 2026-08-19 →
166 bioRxiv v1 / preprint

Preprint—not peer reviewed

PhageLysData: an evidence-aware and AI-ready dataset of phage lytic enzymes and depolymerases

Medina-Ortiz, D., Olivera-Nappa, A., Lienqueo, M. E., Opazo, R., Romero, J.

PhageLysData integrates dispersed sequence and annotation records through reproducible multisource integration, provenance tracking, and exact-sequence consolidation. The authors report 807,366 source observations consolidated into 759,105 unique sequences, including an evidence-supported Core of 11,867 entities. Its explicit separation of evidence-supported and prediction-only records gives machine-learning workflows a traceable starting point for phage-enzyme retrieval and comparison.

AI–biochemistry Datasets + benchmarks
Issue 2026-08-26 →
167 arXiv v1 / preprint

Preprint—not peer reviewed

Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries

Henrik Wille, Luis-Finley Schutz, Felix Strieth-Kalthoff

The study benchmarks four molecular language models across six virtual libraries, then fine-tunes their encoders on structures drawn from each target library. The authors report that domain adaptation consistently improves sample efficiency and produces several top-performing representations across the benchmark tasks. This result makes the target library itself a practical part of representation design for adaptive screening and self-driving experimentation.

AI–chemistry Foundation modelsMolecular representation
Issue 2026-08-19 →