CD

Chenru Duan

0000-0003-2592-4237

physics.chem-phcs.LGcond-mat.mtrl-scics.AIMachine Learningcond-mat.stat-mechcs.NEphysics.comp-phAlgorithmsartificial intelligence

On Valency

published · living versions

Science Prediction Universe

with Zengji Tu, Xiang Liu, Jiarui Sun, Ruoyu Wu +31

1 version

W_xgqyup7dv1 · current

Preprints & journals

68 papers in the corpus · 2017–2026

Revealing Excited-State Electrochemical Reaction Pathways at Solid Electrolyte Interphases by Ab Initio Nonadiabatic Molecular Dynamics.

Shi, Zhan, Chen, Linjie, Li, Zhi et al.

0 citations

426775852026Journal of the American Chemical Society. 2026;148(33):36175-36183

Measuring AI Scientists: From Exams to Discovery

Yuanqi Du, Steven Dillmann, Jon Laurent et al.

0 citations

10.26434/chemrxiv.15007582/v12026ChemRxiv

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents

Yuanqi Du, Botao Yu, Tianyu Liu et al.

1 citation

2512.21782v22025arXiv

Evaluating Large Language Models in Scientific Discovery

Zhangde Song, Jieyu Lu, Yuanqi Du et al.

3 citations

2512.15567v22025arXiv

A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.

Cui, Taoyong, Han, Yunhong, Jia, Haojun et al.

0 citations

413454022026Scientific data. 2025;13(1):37

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price et al.

0 citations

2511.20976v12025arXiv

The Rise of Generative AI for Metal-Organic Framework Design and Synthesis

Chenru Duan, Aditya Nandy, Shyam Chand Pal et al.

22 citations

2508.13197v12025arXiv

Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.

Zhao, Qiyuan, Han, Yunhong, Zhang, Duo et al.

16 citations

406525152026Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2025;12(34):e06240

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models.

Lu, Jieyu, Song, Zhangde, Zhao, Qiyuan et al.

24 citations

406079902025Journal of the American Chemical Society. 2025;147(36):32377-32388

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Taoyong Cui, Yunhong Han, Haojun Jia et al.

1 citation

2505.12447v12025arXiv

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks

Chenru Duan, Aditya Nandy, Sizhan Liu et al.

2 citations

2505.08531v12025arXiv

MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models

Jingru Gan, Peichen Zhong, Yuanqi Du et al.

5 citations

2502.20933v22025arXiv

AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential

Bangchen Yin, Jiaao Wang, Weitao Du et al.

3 citations

2501.07155v42025arXiv

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models

Jieyu Lu, Zhangde Song, Qiyuan Zhao et al.

6 citations

2410.18136v12024arXiv

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

Yuanqi Du, Michael Plainer, Rob Brekelmans et al.

2 citations

2410.07974v42024arXiv

Large Language Models are Catalyzing Chemistry Education

Yuanqi Du, Chenru Duan, Andres Bran et al.

10 citations

10.26434/chemrxiv-2024-h722v2024ChemRxiv

Efficient Evolutionary Search Over Chemical Space with Large Language Models

Haorui Wang, Marta Skreta, Cher-Tian Ser et al.

6 citations

2406.16976v32024arXiv

Navigating Chemical Space with Latent Flows

Guanghao Wei, Yining Huang, Chenru Duan et al.

1 citation

2405.03987v32024arXiv

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions

Chenru Duan, Guan-Horng Liu, Yuanqi Du et al.

4 citations

2404.13430v22024arXiv

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes.

Vennelakanti, Vyshnavi, Kilic, Irem B, Terrones, Gianmarco G et al.

17 citations

381485252024The journal of physical chemistry. A. 2024;128(1):204-216

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets.

Rasmussen, Maria H, Duan, Chenru, Kulik, Heather J et al.

36 citations

381110202026Journal of cheminformatics. 2023;15(1):121

Classification of Hemilabile Ligands Using Machine Learning.

Kevlishvili, Ilia, Duan, Chenru, Kulik, Heather J

15 citations

380519822023The journal of physical chemistry letters. 2023;14(49):11100-11109

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.

Duan, Chenru, Du, Yuanqi, Jia, Haojun et al.

90 citations

381777242024Nature computational science. 2023;3(12):1045-1055

Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion

Haojun Jia, Chenru Duan, Ilia Kevlishvili et al.

11 citations

10.26434/chemrxiv-2023-3fk6j2023ChemRxiv

Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO.

Ariyarathna, Isuru R, Cho, Yeongsu, Duan, Chenru et al.

10 citations

377678412026Physical chemistry chemical physics : PCCP. 2023;25(39):26632-26639

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes.

Vennelakanti, Vyshnavi, Taylor, Michael G, Nandy, Aditya et al.

30 citations

374319142023The Journal of chemical physics. 2023;159(2)

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Shengchao Liu, Weitao Du, Yanjing Li et al.

9 citations

2306.09375v12023arXiv

M $^2$ Hub: Unlocking the Potential of Machine Learning for Materials Discovery

Yuanqi Du, Yingheng Wang, Yining Huang et al.

6 citations

2307.05378v12023arXiv

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models.

Cytter, Yael, Nandy, Aditya, Duan, Chenru et al.

5 citations

368769032023Physical chemistry chemical physics : PCCP. 2023;25(11):8103-8116

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors.

Terrones, Gianmarco G, Duan, Chenru, Nandy, Aditya et al.

40 citations

367941852024Chemical science. 2023;14(6):1419-1433

DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks.

Cho, Yeongsu, Nandy, Aditya, Duan, Chenru et al.

10 citations

365481162023Journal of chemical theory and computation. 2023;19(1):190-197

A transferable recommender approach for selecting the best density functional approximations in chemical discovery.

Duan, Chenru, Nandy, Aditya, Meyer, Ralf et al.

31 citations

381779512024Nature computational science. 2023;3(1):38-47

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores.

Duan, Chenru, Nandy, Aditya, Terrones, Gianmarco G et al.

39 citations

368737002023JACS Au. 2023;3(2):391-401

Ligand additivity relationships enable efficient exploration of transition metal chemical space.

Arunachalam, Naveen, Gugler, Stefan, Taylor, Michael G et al.

22 citations

363797902022The Journal of chemical physics. 2022;157(18):184112

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

Aditya Nandy, Shuwen Yue, Changhwan Oh et al.

94 citations

2210.14191v12022arXiv

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

Gianmarco Terrones, Chenru Duan, Aditya Nandy et al.

0 citations

2209.08595v12022arXiv

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands.

Duan, Chenru, Ladera, Adriana J, Liu, Julian C-L et al.

18 citations

358347422022Journal of chemical theory and computation. 2022;18(8):4836-4845

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis.

Duan, Chenru, Nandy, Aditya, Adamji, Husain et al.

18 citations

357375872022Journal of chemical theory and computation. 2022;18(7):4282-4292

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations.

Ariyarathna, Isuru R, Duan, Chenru, Kulik, Heather J

20 citations

355685362022The Journal of chemical physics. 2022;156(18):184113

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry.

Bajaj, Akash, Duan, Chenru, Nandy, Aditya et al.

10 citations

355685422022The Journal of chemical physics. 2022;156(18):184112

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts.

Nandy, Aditya, Duan, Chenru, Goffinet, Conrad et al.

75 citations

356475892023JACS Au. 2022;2(5):1200-1213

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost.

Duan, Chenru, Chu, Daniel B K, Nandy, Aditya et al.

24 citations

356558822024Chemical science. 2022;13(17):4962-4971

Machine Learning for the Discovery, Design, and Engineering of Materials.

Duan, Chenru, Nandy, Aditya, Kulik, Heather J

68 citations

353206982022Annual review of chemical and biomolecular engineering. 2022;13:405-429

MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks.

Nandy, Aditya, Terrones, Gianmarco, Arunachalam, Naveen et al.

136 citations

352775332022Scientific data. 2022;9(1):74

Representations and strategies for transferable machine learning improve model performance in chemical discovery.

Harper, Daniel R, Nandy, Aditya, Arunachalam, Naveen et al.

20 citations

351830862022The Journal of chemical physics. 2022;156(7):074101

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy

Chenru Duan, Daniel B. K. Chu, Aditya Nandy et al.

1 citation

2201.04243v12022arXiv

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Aditya Nandy, Chenru Duan, Heather J. Kulik

99 citations

2111.01905v12021arXiv

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks.

Nandy, Aditya, Duan, Chenru, Kulik, Heather J

199 citations

346433742022Journal of the American Chemical Society. 2021;143(42):17535-17547

Understanding the Role of Geometric and Electronic Structure in Bioinspired Catalyst Design: the Case of Formate Dehydrogenase

Mingjie Liu, Azadeh Nazemi, Michael Taylor et al.

0 citations

10.26434/chemrxiv-2021-397ph2021ChemRxiv

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.

Duan, Chenru, Chen, Shuxin, Taylor, Michael G et al.

41 citations

347455332021Chemical science. 2021;12(39):13021-13036

Career total: 115 works. 68 are in this corpus.Showing the 50 most recent.

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