Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Janet, Jon Paul, Chan, Lydia, Kulik, Heather J.·2018·The Journal of Physical Chemistry Letters

Cited by (200)

Items 101–200 of 200

  1. Data Mining the C−C Cross‐Coupling Genome
    Boodsarin Sawatlon, Matthew D. Wodrich, Benjamin Meyer, Alberto Fabrizio, Clémence Corminbœuf · 2019 · ChemCatChem
  2. Graph neural networks for predicting metal–ligand coordination of transition metal complexes
    Jacob Toney, Roland G. St. Michel, Aaron G. Garrison, Ilia Kevlishvili, Heather J. Kulik · 2025 · Proceedings of the National Academy of Sciences
  3. Representations and strategies for transferable machine learning improve model performance in chemical discovery
    Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik · 2022 · The Journal of Chemical Physics
  4. High-Throughput Discovery of Ferrocene Mechanophores with Enhanced Reactivity and Network Toughening
    Ilia Kevlishvili, Jafer R. Vakil, David W. Kastner, Xiao Huang, Stephen L. Craig, Heather J. Kulik · 2025 · ACS Central Science
  5. The basis for reevaluating the reactivity of pyrite surfaces: spin states and crystal field d-orbital splitting energies of bulk, terrace, edge, and corner Fe( ii ) ions
    Krishnamoorthy Arumugam, Devon Renock, Udo Becker · 2019 · Physical Chemistry Chemical Physics
  6. Charting Regions of Cobalt’s Chemical Space with Maximally Large Magnetic Anisotropy: A Computational High-Throughput Study
    Lorenzo A. Mariano, Nguyen Vu Ha Anh, Valerio Briganti, Alessandro Lunghi · 2024 · Journal of the American Chemical Society
  7. Machine learning corrected alchemical perturbation density functional theory for catalysis applications
    Charles D. Griego, Lingyan Zhao, Karthikeyan Saravanan, John A. Keith · 2020 · AIChE Journal
  8. Importance of Dispersion in the Molecular Geometries of Mn(III) Spin-Crossover Complexes
    Sabyasachi Roy Chowdhury, Ngan Nguyen, Bess Vlaisavljevich · 2023 · The Journal of Physical Chemistry A
  9. Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes
    Vyshnavi Vennelakanti, Irem B. Kilic, Gianmarco Terrones, Chenru Duan, Heather J. Kulik · 2023 · The Journal of Physical Chemistry A
  10. Data-driven ligand field exploration of Fe( iv )–oxo sites for C–H activation
    Grier M. Jones, Brett A. Smith, Justin Kyle Kirkland, Konstantinos D. Vogiatzis · 2022 · Inorganic Chemistry Frontiers
  11. Classification of Hemilabile Ligands Using Machine Learning
    Ilia Kevlishvili, Chenru Duan, Heather J. Kulik · 2023 · The Journal of Physical Chemistry Letters
  12. Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks
    Yang Liu, Lanting Guo, Xiaoyu Hu, Mengjie Zhou · 2025 · Sensors
  13. Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors
    Mengwei Gao, Bo Cai, Gaoyu Liu, Lili Xu, Shengli Zhang, Haibo Zeng · 2023 · Physical Chemistry Chemical Physics
  14. Genetic Algorithms for the Discovery of Homogeneous Catalysts
    Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Rubén Laplaza, Clémence Corminbœuf · 2023 · CHIMIA International Journal for Chemistry
  15. Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning
    Kai Lin Woon, Zhao Xian Chong, Azhar Ariffin, Chee Seng Chan · 2021 · Journal of Molecular Graphics and Modelling
  16. Automated prediction of ground state spin for transition metal complexes
    Yuri Cho, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf · 2024 · Digital Discovery
  17. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie
  18. Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling
    Alexey I. Shinkevich, Tatiana V. Malysheva, Yulia Vladimirovna Vertakova, Vladimir Aleksandrovich Plotnikov · 2021 · Mathematics
  19. Mean Value Ensemble Hubbard-U Correction for Spin-Crossover Molecules
    Angel Albavera-Mata, Samuel B. Trickey, Richard G. Hennig · 2022 · The Journal of Physical Chemistry Letters
  20. Quantum Chemistry Meets Machine Learning
    Alberto Fabrizio, Benjamin Meyer, Raimón Fabregat, Clémence Corminbœuf · 2019 · CHIMIA International Journal for Chemistry
  21. The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes
    David Balcells, Bastian Bjerkem Skjelstad · 2020 · ChemRxiv
  22. A proficient multivariate approach for iron( ii ) spin crossover behaviour modelling in the solid state
    Lorenzo Marchi, Simone Fantuzzi, Andrea Cingolani, Alessandro Messori, Rita Mazzoni, Stefano Zacchini, Marina Cocchi, Luca Rigamonti · 2023 · Dalton Transactions
  23. Accelerating the discovery of N-annulated perylene organic sensitizers via an interpretable machine learning model
    Yu Zhang, Zhichun Zheng, Fengqing Chen, Minjie Li, Peng Ding, Wencong Lu · 2023 · Journal of Molecular Structure
  24. Calculating spin crossover temperatures by a first-principles LDA+U scheme with parameter U evaluated from GW
    Yachao Zhang · 2019 · The Journal of Chemical Physics
  25. What's Left for a Computational Chemist To Do in the Age of Machine Learning?
    Heather J. Kulik · 2021 · Israel Journal of Chemistry
  26. Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation
    Daniel R. Harper, Heather J. Kulik · 2022 · Inorganic Chemistry
  27. Numerical simulation, clustering, and prediction of multicomponent polymer precipitation
    Pavan Inguva, Lachlan R. Mason, Indranil Pan, Miselle Hengardi, Omar K. Matar · 2020 · Data-Centric Engineering
  28. Deep Learning Coordinate-Free Quantum Chemistry
    Matthew K. Matlock, Max Hoffman, Na Le Dang, Dakota L. Folmsbee, Luke A. Langkamp, Geoffrey Hutchison, Neeraj Kumar, Kathryn Sarullo, S. Joshua Swamidass · 2021 · The Journal of Physical Chemistry A
  29. Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule Magnet by Machine Learning
    Sijin Ren, Eric Fonseca, William Perry, Hai‐Ping Cheng, Xiaoguang Zhang, Richard G. Hennig · 2022 · The Journal of Physical Chemistry A
  30. Using experimental data in computationally guided rational design of inorganic materials with machine learning
    Heather J. Kulik · 2025 · Journal of materials research/Pratt's guide to venture capital sources
  31. Inverse design of face-like 3D surfaces via bi-material 4D printing and shape morphing
    Yi-Hung Chiu, Yu‐Ting Huang, Mao-Chuan Chen, Yi-Xian Xu, Yu-Chen Yen, Jia‐Yang Juang · 2025 · Virtual and Physical Prototyping
  32. Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules
    Masato Sumita, Shoichi Ishida, Kazuki Yoshizoe, Ryo Tamura, Kei Terayama, Koji Tsuda · 2026 · Chemical Reviews
  33. Force-Activated Spin-Crossover in Fe 2+ and Co 2+ Transition Metal Mechanophores
    Xiao Huang, Ilia Kevlishvili, Stephen L. Craig, Heather J. Kulik · 2024 · Inorganic Chemistry
  34. Prediction study on critical temperature (C) of different atomic numbers superconductors (both gaseous/solid elements) using machine learning techniques
    G. Revathy, Velayutham Rajendran, P. Sathish Kumar · 2020 · Materials Today Proceedings
  35. Improving the generative performance of chemical autoencoders through transfer learning
    Nicolae C. Iovanac, Brett Matthew Savoie · 2020 · Machine Learning Science and Technology
  36. Machine learning screening and DFT calculations for exploring Cs2Ag0.75X0.25BiCl6 (X=Na, K, Rb) double perovskites as underwater photovoltaic materials
    Changcheng Chen, Han Zhao, Yaxin Xu, Zhengjun Wang, Yali Tuo, Yingge Du, Xiongfei Yun, Shaohang Shi, Jiangzhou Xie, Shuli Gao et al. · 2025 · Solar Energy Materials and Solar Cells
  37. Developing a predictive model for the maximum power conversion efficiency of inorganic perovskites: A combined approach using density functional theory and machine learning
    Yuling Han, Ziyue Zhao, Yijun Zhang, Xiaodong Yang, Baolin Wang, Yang Shen · 2024 · Computational Materials Science
  38. From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks
    Aditya Nandy · 2025 · Journal of Catalysis
  39. Detachment of HCO3– from the Active Site of Carbonic Anhydrase: Molecular Dynamics Simulation and Machine Learning
    Gong Chen, Diannan Lu, Jianzhong Wu, Zheng Liu · 2018 · The Journal of Physical Chemistry C
  40. Redox‐active ligands as a challenge for electronic structure methods
    Ursula Rastetter, Axel Jacobi von Wangelin, Carmen Herrmann · 2022 · Journal of Computational Chemistry
  41. Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning
    Ganesan Raman · 2021 · ChemistrySelect
  42. Advancing the design of gold nanomaterials with machine-learned potentials
    Kithma Sajini, Caroline Desgranges, Jérôme Delhommelle · 2025 · Nano Express
  43. An optimized deep convolutional neural network for yield prediction of Buchwald-Hartwig amination
    Yanan Zhao, Xiaochen Liu, Lu Han, Xuefeng Zhu, Tianhang Wang, Gen Luo, Rencheng Zheng, Yi Luo · 2021 · Chemical Physics
  44. Exploring the computational design of anionic spin-crossover systems
    Laia Navarro, Jordi Cirera · 2022 · Inorganic Chemistry Frontiers
  45. Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
    Yael Cytter, Aditya Nandy, Chenru Duan, Heather J. Kulik · 2023 · Physical Chemistry Chemical Physics
  46. Inverse design of ligands using a deep generative model semi‐supervised by a data‐driven ligand field strength metric
    Zhi-Hang Lee, Po Chuan Lin, Tzuhsiung Yang · 2023 · Journal of the Chinese Chemical Society
  47. CKineticsDB─An Extensible and FAIR Data Management Framework and Datahub for Multiscale Modeling in Heterogeneous Catalysis
    Siddhant M. Lambor, Sashank Kasiraju, Dionisios G. Vlachos · 2023 · Journal of Chemical Information and Modeling
  48. Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design
    J. Terence Blaskovits, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf · 2022 · ChemRxiv
  49. Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie
  50. Deep learning-based quasi-continuum theory for structure of confined fluids
    Haiyi Wu, Narayana R. Aluru · 2022 · The Journal of Chemical Physics
  51. MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes
    Kyung-Hoon Lee, Shinyoung Park, Mi Jeong Park, Woo Youn Kim · 2025 · Journal of Chemical Information and Modeling
  52. Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes
    Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort, Evgeny A. Pidko · 2025 · Digital Discovery
  53. From Subnanometric Clusters toward Single-Atom Catalysts
    Afshan Mohajeri · 2020 · ACS symposium series
  54. Automation of some macromolecular properties using a machine learning approach
    Merjem Hoxha, Hiqmet Kamberaj · 2021 · Machine Learning Science and Technology
  55. Building machine learning assisted phase diagrams: Three chemically relevant examples
    Xabier Telleria‐Allika, Jose Maria Mercero, Xabier López, Jon Mattin Matxain · 2022 · AIP Advances
  56. Zeolite encapsulated organometallic complexes as model catalysts
    Ethan P. Iaia, Ademola Soyemi, Tibor Szilvási, James W. Harris · 2023 · Dalton Transactions
  57. Tailored group contribution methods designed using machine learning to predict enthalpies in carboxylic acids and anhydrides
    Jesús A. Arzola-Flores, José A. Alvarado-García, Miguel Angel García-Castro, Fausto Díaz-Sánchez, Ximena Limón-Aguilar, Ma. Fernanda Saviñón-Flores · 2025 · Thermochimica Acta
  58. Green synthesis of N,N′-(1,2-phenylene)bis(phthalimide-5-carboxilic acid) and the prediction of its properties: Standard enthalpies of formation and sublimation
    Fausto Díaz-Sánchez, Miguel Angel García-Castro, Maura Cárdenas-García, Jesús A. Arzola-Flores, Gabriel Alonso-Gallardo · 2025 · Journal of Molecular Structure
  59. Linear and nonlinear machine learning correlation of transition metal cluster characteristics
    Alireza Kokabi, Zahra Nasiri Mahd, Zohreh Naghibi · 2021 · Journal of Nanoparticle Research
  60. A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes
    Zhi-Hang Lee, Yuting Chen, Hao-Ting Chang, Tzuhsiung Yang · 2023 · ChemRxiv
  61. A Machine Learning Approach to Decipher the Origin of Magnetic Anisotropy in Three-Coordinate Cobalt Single-Ion Magnets
    Gopalan Rajaraman, Rajanikanta Rana, Abinash Swain, Garima Bangar · 2024 · ChemRxiv
  62. Adaptive Generation of Training Data for ML Reduced Model Creation
    Mark R. Cianciosa, Richard K. Archibald, Wael R Elwasif, Ana Gainaru, Jin Myung Park, R. E. Whitfield · 2022 · IEEE International Conference on Big Data (BigData)
  63. Machine Learning Algorithms for Applications in Materials Science I
    Azizeh Abdolmaleki, Fereshteh Shiri, Shahin Ahmadi · 2025 · Challenges and advances in computational chemistry and physics
  64. Synergistic machine learning and DFT driven high-throughput screening strategy for A2BX6-Type inorganic perovskite photovoltaic materials
    Heng Ni, Yuling Han, Yijun Zhang, Xiaodong Yang, Yang Shen · 2025 · Solar Energy Materials and Solar Cells
  65. Precision Chemical Design for Reticular Spintronics
    Yixuan Che, Haifeng Lv, Xiaojun Wu · 2026 · Precision Chemistry
  66. Machine learning-assisted materials design, mechanistic insights, and predictive modeling for high performance lithium-sulfur batteries
    Kaijie Miao, Chengwei Ma, Jiangqi Zhou · 2026 · Energy storage materials
  67. Surface-Induced Electronic and Vibrational Level Shifting of [Fe(py)2bpym(NCS)2] on Al(100)
    Yachao Zhang · 2023 · Materials
  68. Many-body expansion based machine learning models for octahedral transition metal complexes
    Ralf Meyer, Daniel B. K. Chu, Heather J. Kulik · 2024 · Machine Learning Science and Technology
  69. Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network
    Wei Luo, Jiahao Guan, Zuotao Yan · 2024 · Canadian Journal of Chemistry
  70. In Silico Discovery of a Neutral 2π Aromatic Silicon Aluminum Compound
    Himanshu Sharma, Kumar Vanka · 2024 · Organometallics
  71. Application of the Improved PSO-Based Extended Domain Method in Engineering
    Bin Bai, Zhiwei Guo, Qiliang Wu, Junyi Zhang, Yanchao Cui · 2020 · Mathematical Problems in Engineering
  72. A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes
    Zhi-Hang Lee, Yuting Chen, Hao-Ting Chang, Tzuhsiung Yang · 2023 · ChemRxiv
  73. The Discovery of an Aromatic Silicon Aluminium Compound Through A Novel Four-Pronged Computational Approach
    Himanshu Sharma, Kumar Vanka · 2024 · ChemRxiv
  74. Discovery of Spin-Crossover Materials with Equivariant Graph Neural Networks and Relevance-Based Classification
    Angel Albavera-Mata, Pawan Prakash, Jason Gibson, Eric Fonseca, Sijin Ren, Xiaoguang Zhang, Hai‐Ping Cheng, Michael Shatruk, Samuel B. Trickey, Richard G. Hennig · 2025 · Journal of Chemical Theory and Computation
  75. Relating structure, composition, and spin crossover properties in Hofmann complexes
    Matthew G. Robb, Hanna L. B. Boström · 2025 · Dalton Transactions
  76. Leveraging high-spin DFT features for prediction of spin state gaps in 3d transition metal complexes
    Anuj Kumar Ray, Sandeep Nagar, Girish Varma, U. Deva Priyakumar, Ankan Paul · 2025 · Physical Chemistry Chemical Physics
  77. Interfacial mechanisms of 6PPD-quinone adsorption on metal-organic framework functionalized nanocellulose aerogels
    Lei Miao, Wei Guo, Haitao CHEN, Xiaozheng Sun · 2026 · Colloids and Surfaces A Physicochemical and Engineering Aspects
  78. Machine Learning for Accelerated Metal Oxide Thermochemical Predictions
    Nickolas A. Joyner, Sarah Sprouse, Haizley Herndon, Kathleen E. Butler, Elliot F. Kaye, David A. Dixon, Małgorzata Z. Makoś · 2026 · Journal of Chemical Theory and Computation
  79. Artificial Intelligence in Metal Complexes Research: Advances in Synthesis, Characterization, and Applications
    Shimaa Hosny, Qusi K. Alomoush, Leila Magdy Abbass, Mohammad A. Qaderi, Marwa Saeed, Lamiaa Z. Mohamed · 2026 · Arabian Journal for Science and Engineering
  80. Targeted agrivoltaic applications of lead-free double perovskites and plant light responses
    Han Zhao, Changcheng Chen, Songya Wang, Yuxi Du, Yali Tuo, Zhengjun Wang, Yi Xu, Shuli Gao, Wen Chen, Jiangzhou Xie et al. · 2026 · Solar Energy
  81. Autonomous laboratories for sustainable nanomaterials discovery
    Aarti Jathar, Beena R. Nawghare, Ketankumar A. Ganure · 2026 · Next Materials
  82. Intelligent Approaches
    Parikshit Narendra Mahalle, Pravin P. Hujare, Gitanjali R. Shinde · 2023 · SpringerBriefs in applied sciences and technology
  83. Evolutionary Algorithms and Workflows for De Novo Catalyst Design
    Marco Foscato, Jonas B. Ekeli, Marcello Costamagna, D. Grellscheid, Vidar Remi Jensen · 2023 · Elsevier eBooks
  84. Generative chemical transformer: attention makes neural machine learn molecular geometric structures via text
    Hyunseung Kim, Jonggeol Na, Won Bo Lee · 2021 · arXiv (Cornell University)
  85. Computational Scaling Relationships Predict Experimental Activity and Rate Limiting Behavior in Homogenous Water Oxidation
    Daniel R. Harper, Heather J. Kulik · 2021
  86. Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces
    Hannes Kneiding, Ainara Nova, David Balcells · 2023 · ChemRxiv
  87. Discovery of Hemilabile Ligands Using MachineLearning
    Ilia Kevlishvili, Chenru Duan, Heather J. Kulik · 2023 · ChemRxiv
  88. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
    Aditya Nandy, Heather J. Kulik · 2024 · Wiley eBooks
  89. Stark spectral line broadening modeling by machine learning algorithms
    Irinel Tapalaga, Ivan Traparić, Nora Trklja, Jagoš Purić, Ivan P. Dojčinović · 2022 · Neural Computing and Applications
  90. Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next
    Scott E. Denmark, Andrew F. Zahrt, William T. Darrow, Brennan T. Rose, Jeremy J. Henle · 2020 · ChemRxiv
  91. Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
    Jon Paul Janet · 2022 · ACS symposium series
  92. Data-driven approaches enabling the screening and design of promising materials for energy storage batteries
    Qi ZHANG, Chao Peng, Dongfeng Xue · 2023 · Scientia Sinica Technologica
  93. A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes
    Tzuhsiung Yang, T. C. Lee, Yuting Chen, Hao-Ting Chang · 2023 · Research Square
  94. Bibliometric Analysis: Collaboration Networks In Discovery Learning Research
    Andi Suparjo, Edwin Musdi, Yerizon Yerizon, I Made Arnawa · 2024 · Jurnal Penelitian Pendidikan IPA
  95. Automated Prediction of Ground State Spin for Transition Metal Complexes
    Yuri Cho, Rubén Laplaza, Sergi Vela, Clémence Corminbœuf · 2024 · ChemRxiv
  96. High-Throughput Discovery of Ferrocene Mechanophores with Enhanced Reactivity and Network Toughening
    Heather J. Kulik, Ilia Kevlishvili, Jafer R. Vakil, David W. Kastner, Xiao Huang, Stephen L. Craig · 2024 · Research Square
  97. Green Synthesis of N,N′-(1,2-phenylene)bis(phthalimide-5-carboxilic acid) and the prediction of its properties: Standard enthalpies of formation and sublimation
    Fausto Díaz Sánchez, Miguel Angel García-Castro, Maura Cárdenas-García, Jesús Andrés Arzola Flores, Gabriel Alonso-Gallardo · 2025 · SSRN Electronic Journal
  98. Oxidation states in solids from data-driven paradigms
    Yue Yin, Hai Yan Xiao · 2025 · Chemical Science
  99. Maximizing the Magnetic Anisotropy of Dy Complexes by Fine-Tuning Organic Ligands: A Systematic Multireference High-Throughput Exploration of over 25k Molecules
    Lion Frangoulis, Lorenzo A. Mariano, Nguyen Vu Ha Anh, Zahra Khatibi, Alessandro Lunghi · 2026 · Journal of the American Chemical Society
  100. Towards sustainable exploration of chemical spaces with machine learning
    Leonardo Medrano Sandonas, David Balcells, Tom Barnowsky, Anton S. Bochkarev, Jacqueline Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich et al. · 2026 · Digital Discovery