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The Open Catalyst Challenge 2021: Competition Report
P. 1–12.
Lukin R., Grigoriev R., Yarullin A., Faleev M.
In this report, we describe the Open Catalyst Challenge held at NeurIPS 2021, focusing on using machine learning (ML) to accelerate the search for low-cost catalysts that can drive reactions converting renewable energy to storable forms. Specifically, the challenge required participants to develop ML approaches for relaxed energy prediction, i.e. given atomic positions for an adsorbate-catalyst system, the goal was to predict the energy of the system’s relaxed or lowest energy state. To perform well on this task, ML approaches need to approximate the quantum mechanical computations in Density Functional Theory (DFT). By modeling these accurately, the catalyst’s impact on the overall rate of a chemical reaction may be estimated; a key factor in filtering potential electrocatalyst materials. The challenge encouraged community-wide progress on this task and the winning approach improved direct relaxed energy prediction by ∼15% relative over the previous state-of-the-art.
Publication based on the results of:
Mikhail A. Boym, Pototskiy R., Evgeniya S. Podyacheva et al., Dalton Transactions 2025 Vol. 54 No. 14 P. 5655–5658
Natural R-carvone was converted into chiral 1-aryl-2-methyl-5-isopropyl-cyclohexadienes using a cross-coupling reaction as a key step. These dienes react with RuCl3 to give planar-chiral complexes [(arene)RuCl2]2 in 70–75% yields. Complex [(1-Ph-2-Me-5-iPr-C6H3)RuCl2]2, a chiral analogue of the classical catalyst [(cymene)RuCl2]2, promotes C–H activation of N-methoxy-benzamide and intramolecular insertion of a diazo compound into a C–H bond, but gives products with low ...
Added: February 15, 2026
Kolos A., Nelyubina Y., Godovikova M. et al., Chemistry - A European Journal 2025 Vol. 31 No. 41 Article e202501793
Cyclopentadienyl rhodium complexes (C5R5)RhLx are widely used as homogeneous catalysts. Herein we report the synthesis of such complexes with structurally diverse cyclopentadienyl ligands in one step directly from alkynes. Specifically, reactions of bulky terminal alkynes (tert-butylacetylene or trimethylsilylacetylene) with activated internal alkynes (ethyl 2-butynoate or dimethyl acetylenedicarboxylate (DMAD)) and [(cod)RhCl]2 produce either cyclopentadienyl or pentalene-type ...
Added: February 15, 2026
Ankudinov N., Alexeev N., Podyacheva E. et al., Chemical Science 2025 Vol. 16 No. 15 P. 6298–6306
Organic compounds with boron–nitrogen bonds are widely used as fluorescent sensors and semiconducting materials. This paper presents a new approach for the formation of B–N bonds via catalytic insertion of nitrenes into B–H bonds. The reaction proceeds most selectively for cyclic boranes with a 2-phenylpyridine framework and nitrenes generated in situ by the oxidation of ...
Added: February 15, 2026
Arteaga Moreano B. D., Chervov N., Poptsova M., Scientific Reports 2026 Vol. 16 No. 1 Article 4772
Accurate prediction of protein-protein interactions (PPIs) is fundamental to understanding biological processes and disease mechanisms. While deep learning offers a powerful alternative to costly experimental methods, existing approaches often overlook critical protein-surface information and rely on simplistic feature fusion techniques, thereby limiting performance. To address this, we introduce GSMFormer-PPI, a novel multimodal framework that integrates ...
Added: February 4, 2026
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Parakal E. G., Kuznetsov S., Makarov I. et al., IEEE Access 2025 Vol. 13 P. 149657–149678
This paper proposes a novel explainable document classification framework that integrates Concept Whitening (CW) with graph concepts that are derived from stable graph patterns, and extracted via methods based on Formal Concept Analysis (FCA) and pattern structures. Document graphs are constructed using Abstract Meaning Representation (AMR) graphs, from which graph concepts are extracted and aligned ...
Added: October 22, 2025
Yusupov V., Rakhuba M., Frolov E., , in: CIKM '25: Proceedings of the 34rd ACM International Conference on Information and Knowledge Management.: ACM, 2025. Ch. 1 P. 5469–5473.
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the ...
Added: October 3, 2025
Sycheva T., Beketov M., Smolyar I., , in: Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions: 34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9–12, 2025, Proceedings, Part V.: Cham: Springer, 2025. Ch. 4 P. 29–33.
We consider Graph Anisotropic Diffusion (GAD), a recently proposed model of graph neural networks, that can be trained to predict desired properties of the graph by performing learnable diffusion of node features on it. In contrast with similar methods, GAD introduces anisotropy of said diffusion by incorporating filters built from the graph’s Fiedler vector. In ...
Added: September 29, 2025
Cham: Springer, 2025.
This book constitutes the refereed proceedings of 34th International Workshops which were held in conjunction with the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025.
The 20 full papers and 8 abstracts included in this workshop volume were carefully reviewed and selected from 42 submissions. ...
Added: September 29, 2025
Perepelkin A., Sharifov A., Titov D. et al., Energies 2025 Vol. 18 No. 14 Article 3881
In the gas industry, accurate forecasting of gas production is critical for optimizing well operating conditions. Although traditional hydrodynamic models offer high accuracy, they are often computationally intensive and time-consuming, prompting a growing interest in proxy-based alternatives. This study proposes a hybrid methodology based on Spatio-Temporal Graph Neural Networks (ST-GNNs) for gas production forecasting. The ...
Added: July 23, 2025
Nykhrikova E., Kiseleva M., Kalle P. et al., Inorganic Chemistry 2025 Vol. 64 No. 10 P. 5210–5220
Multifunctional compounds with properties that may be triggered by different external stimuli are highly desirable yet challenging in their design and synthesis. Herein, we report a cyclometalated iridium(III) complex based on bulky 1,2-diphenylphenanthroimidazole (C^N) that can easily change its molecular geometry from trigonal-bipyramidal to octahedral or from a monomeric to dimeric state in response to ...
Added: July 2, 2025
Danil Shaikhelislamov, Denis Turdakov, , in: 2022 Ivannikov Ispras Open Conference (ISPRAS).: IEEE, 2022. P. 31–36.
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper ...
Added: February 3, 2025
D. Shaikhelislamov, Lukyanov K., Severin N. et al., Journal of Mathematical Sciences 2024 Vol. 285 P. 234–244
Graph neural networks (GNNs) have shown great promise in a variety of tasks involving graph data, including recommendation systems. However, as GNNs become more widely adopted in practical applications, concerns have arisen about their vulnerability to adversarial attacks. These attacks can lead to biased recommendations, potentially causing economic losses and safety risks. In this work, ...
Added: February 3, 2025
Tsygankov A., Kozlov A., Liao S. et al., Coordination Chemistry Reviews 2024 Vol. 519 Article 216114
Transition metal (TM)-based catalysts are indispensable tools in modern organic synthesis. Although many efficient catalysts based on TMs are already known, further improvement of catalytic system activity remains one of the major goals of transition metal coordination chemistry. A large number of TM-based catalysts contain halide ligands, mostly chlorides and bromides. Transition metal fluorides have ...
Added: January 23, 2025
Mikhail A. Boym, Pototskiy R., Evgeniya S. Podyacheva et al., Chemical Communications 2024 Vol. 60 No. 33 P. 4491–4494
Heating tert-butyl-tetraline with [(p-cymene)RuCl2]2 produces the racemic complex [(arene)RuCl2]2, which can be separated into enantiomers by chromatography of its diastereomeric adducts with chiral phosphine ligand. The resolved chiral complex catalyzes C–H activation of N-methoxy-benzamides and their annulation with N-vinyl-pivaloyl amide giving dihydroisoquinolones in 50–80% yields and with 40–80% enantiomeric excess. ...
Added: January 23, 2025
Fomin D., Ilya Makarov, Voronina M. et al., IEEE Access 2024 Vol. 12 P. 196195–196206
Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We ...
Added: January 19, 2025
Krivonosov M., Nazarenko T., Ushakov V. et al., Technologies 2025 Vol. 13 No. 1 Article 13
This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good forto addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional mMachine lLearning and Artificial Intelligence methods often struggles to find global optima due to the “curse of dimensionality”. To ...
Added: December 23, 2024