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RESEARCH PAPER · ChemPlusChem · 2024

Paving the road towards automated homogeneous catalyst design

Adarsh V. Kalikadien, Adrian Mirza, Aydin Najl Hossaini, A. Sreenithya, Evgeny A. Pidko

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Paving the road towards automated homogeneous catalyst designOriginalPenta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism0Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation9Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States18Catalysis in the digital age: Unlocking the power of data with machine learning75Revisiting the Reviewed: A Meta‐Analysis of Computational Studies on Transition Metal‐Catalyzed Hydrogenation Reactions2Topological graphs: a review of some of our achievements and perspectives in physical chemistry and homogeneous catalysis5Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis4A retrospective 9-years overview of sulfated polyborate as a robust catalyst for organic transformations1Artificial intelligence-driven advances in photocatalytic hydrogen production19Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes3Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis9Microkinetic Assessment of Ligand-Exchanging Catalytic Cycles2Future Challenges of Homogeneous Catalysis0Advancements in zeolite-based catalysts for the isomerization of n-alkanes: mechanistic insights and future directions16Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes4AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity2Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis8Understanding DFT Uncertainties for More Reliable Reactivity Predictions by Advancing the Analysis of Error Sources10Exploring beyond experiment: generating high-quality datasets of transition metal complexes with quantum chemistry and machine learning8Reaction-conditioned generative model for catalyst design and optimization with CatDRX9Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices15Performance of Meta’s Universal Model for Atoms across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts6Computational, Data Science, and Experimental Evaluation and Design of Homogeneous Transfer Hydroformylation Catalysts0From data to insight: computational tools for rationalizing organometallic and organocatalytic reactivity03DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table0A Virtual Model for Describing Organophosphorus Reactivity: Validation and Application to Virtual Molecule‐Assisted Optimization0Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p ‐Block Metal Catalysts for Borrowing Hydrogen0Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p ‐Block Metal Catalysts for Borrowing Hydrogen1Automated Reaction Network Enumeration and Profiling Platform for Mechanism-Guided Catalyst Design and Reactivity Optimization0Quantum-mechanical descriptors and machine learning for energy materials: a proof-of-concept computational framework0

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  1. Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism

    Catalysis Science & Technology · 2024-01-01

    Hao Lin, Longfei Li, Lanbo Liu, Zhihui Li, Thi Mo Nguyen, Matthieu Jouffroy, Rafael Gramage‐Doria

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  2. Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

    Chemical Science · 2024-01-01

    Adarsh V. Kalikadien, Cecile Valsecchi, Robbert van Putten, Tor Maes, Mikko Muuronen, Natalia Dyubankova, Laurent Lefort, Evgeny A. Pidko

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  3. Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation

    The Journal of Physical Chemistry C · 2024-05-02

    Margareth S. Baidun, Adarsh V. Kalikadien, Laurent Lefort, Evgeny A. Pidko

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  4. Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States

    The Journal of Physical Chemistry Letters · 2024-07-11

    Rubén Laplaza, Matthew D. Wodrich, Clémence Corminbœuf

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  5. Catalysis in the digital age: Unlocking the power of data with machine learning

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2024-09-01

    Bokinala Moses Abraham, Mullapudi V. Jyothirmai, Priyanka Sinha, Francesc Viñes, Jayant Kumar Singh, Francesc Illas

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  6. Revisiting the Reviewed: A Meta‐Analysis of Computational Studies on Transition Metal‐Catalyzed Hydrogenation Reactions

    ChemCatChem · 2024-09-02

    Shahbaz Ahmad, Michæl Bühl

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  7. Topological graphs: a review of some of our achievements and perspectives in physical chemistry and homogeneous catalysis

    Comptes Rendus Chimie · 2024-09-18

    Sana Bougueroua, Ylène Aboulfath, Álvaro Cimas, Ali Hashemi, Evgeny A. Pidko, Dominique Barth, Marie‐Pierre Gaigeot

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  8. Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis

    ACS Catalysis · 2024-10-30

    Jonas B. Ekeli, Marco Foscato, Christian O. Blanco, Giovanni Occhipinti, Deryn E. Fogg, Vidar Remi Jensen

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  9. A retrospective 9-years overview of sulfated polyborate as a robust catalyst for organic transformations

    RSC Advances · 2025-01-01

    Pratibha Chhaperwal, Shivani Soni, Sunita Teli, Hemant Kumar Rundla, Shikha Agarwal

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  10. Artificial intelligence-driven advances in photocatalytic hydrogen production

    New Journal of Chemistry · 2025-01-01

    Leandro Goulart de Araujo, David Farrusseng

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  11. Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes

    Digital Discovery · 2025-01-01

    Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort, Evgeny A. Pidko

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  12. Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis

    iScience · 2025-01-23

    Eduardo Aguilar-Bejarano, Ender Özcan, Raja K. Rit, Hongyi Li, Hon Wai Lam, Jonathan C. Moore, Simon Woodward, Grazziela Patrocinio Figueredo

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  13. Microkinetic Assessment of Ligand-Exchanging Catalytic Cycles

    ACS Catalysis · 2025-03-06

    Orkhan Abdullayev, Diego Garay Ruiz, Berta Bori-Bru, Carles Bó

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  14. Future Challenges of Homogeneous Catalysis

    Unknown journal · 2025-04-04

    Arno Behr, Thomas Seidensticker, Dieter Vogt

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  15. Advancements in zeolite-based catalysts for the isomerization of n-alkanes: mechanistic insights and future directions

    Discover Applied Sciences · 2025-04-28

    Nastaran Parsafard, Zaidoon M. Shakor

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  16. Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes

    Journal of Chemical Theory and Computation · 2025-05-09

    Sára Finta, Adarsh V. Kalikadien, Evgeny A. Pidko

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  17. AI Approaches to Homogeneous Catalysis with Transition Metal Complexes

    ACS Catalysis · 2025-05-14

    Lucía Morán‐González, Arron L. Burnage, Ainara Nova, David Balcells

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  18. Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity

    Journal of Chemical Information and Modeling · 2025-06-11

    Marcello Costamagna, Marco Foscato, D. Grellscheid, Vidar Remi Jensen

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  19. Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

    Chemistry - An Asian Journal · 2025-08-28

    Rizvi Syed Aal E Ali, Jiaolong Meng, Xuefeng Jiang

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  20. Understanding DFT Uncertainties for More Reliable Reactivity Predictions by Advancing the Analysis of Error Sources

    Journal of Chemical Theory and Computation · 2025-09-18

    Gergely Laczkó, Imre Pápai, Péter R. Nagy

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  21. Exploring beyond experiment: generating high-quality datasets of transition metal complexes with quantum chemistry and machine learning

    Current Opinion in Chemical Engineering · 2025-10-05

    Jacob Toney, Aaron G. Garrison, Weiliang Luo, Roland G. St. Michel, Sukrit Mukhopadhyay, Heather J. Kulik

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  22. Reaction-conditioned generative model for catalyst design and optimization with CatDRX

    Communications Chemistry · 2025-10-23

    Apakorn Kengkanna, Yuta Kikuchi, Takashi Niwa, Masahito Ohue

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  23. Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices

    ACS Catalysis · 2025-12-19

    David Dalmau, Susana García-Abellán, Juan V. Alegre-Requena

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  24. Performance of Meta’s Universal Model for Atoms across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts

    The Journal of Physical Chemistry A · 2026-02-18

    Adarsh V. Kalikadien, Evgeny A. Pidko

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  25. Computational, Data Science, and Experimental Evaluation and Design of Homogeneous Transfer Hydroformylation Catalysts

    Organometallics · 2026-03-23

    Jyothish Joy, Daniel A. Odogwu, Ian K. Atkins, Joshua A. Goldscheitter, Gregorio H. Villarreal-Quiroga, Anjaneyulu Koppaka, Stacey J. Smith, Brooke L. Small, Julie A. Hopkins Leseberg, Steven M. Bischof, Michael S. Webster-Gardiner, David J. Michaelis, Daniel H. Ess

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  26. From data to insight: computational tools for rationalizing organometallic and organocatalytic reactivity

    RENDICONTI LINCEI · 2026-05-04

    Antonello De Sio, Laura Falivene

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  27. 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table

    Journal of Chemical Information and Modeling · 2026-05-19

    Marcello Costamagna, Morgan Thomas, Marco Foscato, Vidar Remi Jensen

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  28. A Virtual Model for Describing Organophosphorus Reactivity: Validation and Application to Virtual Molecule‐Assisted Optimization

    Journal of Computational Chemistry · 2026-05-22

    Takayuki Furukawa, Ren Yamada, Satoshi Maeda, Wataru Matsuoka

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  29. Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p ‐Block Metal Catalysts for Borrowing Hydrogen

    Angewandte Chemie · 2026-06-11

    Zhe Chen, Xiaoyu Zhou, 熊传艺, Yubang Liu, Ruzhao Chen, Fuyi Yang, Huayu Liang, Jiaxin Lin, J A K S Su, Yinwu Li, Zhuofeng Ke

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  30. Overcoming Data Starvation: Automated Virtual Reaction Exploration and Machine Learning Discovery of p ‐Block Metal Catalysts for Borrowing Hydrogen

    Angewandte Chemie International Edition · 2026-06-11

    Zhe Chen, Xiaoyu Zhou, 熊传艺, Yubang Liu, Ruzhao Chen, Fuyi Yang, Huayu Liang, Jiaxin Lin, J A K S Su, Yinwu Li, Zhuofeng Ke

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  31. Automated Reaction Network Enumeration and Profiling Platform for Mechanism-Guided Catalyst Design and Reactivity Optimization

    Journal of Chemical Information and Modeling · 2026-08-01

    Pavel A. Dub, Thomas F. Hughes, Thomas J. L. Mustard

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  32. Quantum-mechanical descriptors and machine learning for energy materials: a proof-of-concept computational framework

    Interactions · 2026-09-02

    Rohit Ravindra Nikam, Prashant V. Thokal, Yogesh A. Bhavsar, Hrushikesh N. Kedar, Amol A. Wable, Nilesh D. Sadaphal

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