jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yigen Wu | B. Yao | Xiang Huang | Yongqiang Chen
Source: Journal of Applied Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Integrating machine learning and molecular dynamics for accelerated discovery of polymers with high thermal conductivity The vast chemical diversity of polymers, compounded with sparse reliable characterization data, fundamentally constrains machine learning (ML)-driven exploration of advanced polymeric materials. To overcome this, we establish an integrated computational framework combining a deep neural network (DNN), Bayesian optimization (BO), and molecular dynamics (MD) simulations for the targeted design of backbone polymers with high thermal conductivity (TC ≥ 0.40 W m−1 K−1). Our workflow begins with a data set of 501 backbone polymers, whose thermal conductivities (TCs) are labeled by MD simulations. We then established a mapping between their force-field-inspired descriptors and TCs using a DNN. While sparse high-TC data limited the DNN's predictive accuracy for TC ≥ 0.40 W m−1 K−1, we leveraged it to screen 2209 high-potential candidates from a 14 706-polymer virtual library generated by small molecules according to chemical reaction rules. Subsequent closed-loop BO-MD validation enabled efficient navigation of this subspace, and 11 synthesizable polymers with high TC were identified in 500 evaluations. Crucially, mechanistic analysis reveals that intra-chain interactions dominate thermal transport, with intra-chain contributions of 79.1%–87.5% on TC for the first six polymers. This work delivers a robust paradigm for ML-driven material discovery under data constraints. Workflow ; Bayesian optimization ; Artificial neural network ; Polymer ; Set (abstract data type) ; Thermal conductivity ; Molecular dynamics ; Data set ; Deep learning ; Chemical space Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly
Authors: R. A. Oshiya | Ayan Datta
Source: Journal of Chemical Sciences (article, 2025-04-07)
Field: Chemistry — Organic Chemistry

Machine learning for predicting enantioselectivity in chiral phosphoric acid-catalyzed naphthyl-indole synthesis Phosphoric acid ; Indole test ; Catalysis ; Chemistry ; Enantioselective synthesis ; Combinatorial chemistry ; Organic chemistry Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Asymmetric Synthesis and Catalysis
Authors: Khan Raqib Mahmud | Lingxiao Wang | Jinyuan Chen | Sunzid Hassan
Source: Polymer (article, 2025-07-16)
Field: Materials Science — Polymers and Plastics

A Graph Neural Network based deep learning framework for predicting the thermomechanical behavior of thermoset shape memory polymers Thermosetting polymer ; Shape-memory polymer ; Artificial neural network ; Materials science ; Polymer ; Polymer science ; Composite material ; Graph ; Artificial intelligence ; Polymer chemistry ; Computer science ; Theoretical computer science Polymer composites and self-healing ; Machine Learning in Materials Science
Authors: C. Rajalakshmi | Siti Salwah Salim | Sherin Susan Cherian | R. Darsana | S. Rosemary | S Kavya | Vibin Ipe Thomas
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Performance assessment of various graph neural network architectures for predicting yields in cross-coupling reactions Machine learning (ML) is revolutionizing various scientific fields, including chemistry. Among different ML techniques, graph neural networks (GNNs) have emerged as a powerful tool for capturing complex relationships in data by representing datapoints as graphs. Applying GNNs for predicting yields in chemical reactions is one of the emerging areas of focus. However, handling heterogeneous datasets remains a key challenge. In this study, we utilized diverse datasets encompassing various transition metal-catalyzed cross-coupling reactions including Suzuki, Sonogashira, Cadiot-Chodkiewicz, Ullmann-type, and Buchwald-Hartwig coupling reactions. To predict reaction yields, we implement multiple GNN architectures, including message passing neural networks (MPNN), residual graph convolutional networks (ResGCN), graph sample and aggregate (GraphSAGE), graph attention networks (GAT and GATv2), graph convolutional networks (GCN), and graph isomorphism networks (GIN). The comparative analysis of these architectures reveals that MPNN achieve the highest predictive performance, with an <i>R</i><sup>2</sup> value of 0.75. Additionally, to enhance the interpretability of our models, we employed the integrated gradients method to determine the contribution of each input descriptor to the model's yield prediction. This study highlights the potential of effective and explainable graph-based models to predict the yields of chemical reactions. This work contributes to the application of machine learning in catalysis, providing valuable insights for optimizing catalytic reactions and contributing to innovations in sustainable chemistry and organic synthesis. Yield (engineering) ; Artificial neural network ; Graph ; Computer science ; Coupling (piping) ; Biological system ; Artificial intelligence ; Chemistry ; Machine learning ; Materials science ; Theoretical computer science ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ziyu Zhang | Xinzheng Yang | Liang Yan | Sungwoo Jung | Wei You | Ting Cao | Xiaosong Li
Source: Journal of the American Chemical Society (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Fusion Deep Learning for Predicting Conductivity in Electron-Doped Organic Polymers The development of efficient and air-stable n-type organic semiconductors suitable for molecular n-doping is critical for advancing high-performance, durable organic electronic devices, including transistors, thermoelectrics, and photovoltaics. Machine learning offers a powerful approach to uncovering hidden relationships between molecular structures and their electronic properties, thereby accelerating the discovery and design of promising materials. To support this effort, we constructed a curated database comprising 84 n-type conductive polymers, each characterized by experimental measurements under n-doping conditions with 4-(1,3-dimethyl-2,3-dihydro-1<i>H</i>-benzoimidazol-2-yl)phenyl dimethylamine (N-DMBI-H), and augmented with density functional theory calculations to provide complementary molecular descriptors. After constructing the database, we developed a fusion deep learning model that integrates convolutional neural networks with fully connected artificial neural networks to capture both structural and property-based features of the polymers. The model was trained on the data set and evaluated using leave-one-out cross-validation. The model was further applied to a test set of n-type polymers bearing oligoethylene glycol (OEG) side chains, enabling the identification of key physical factors that influence their conductivity when doped with N-DMBI-H. Finally, a double-blind experiment was conducted to validate the model's practical utility by predicting the conductivity of four BDPPV-type polymers and one N2200-type polymer doped with N-DMBI-H. For the N2200-type polymer and two of the BDPPV-type polymers, the predicted conductivities agreed with experimental values within the same order of magnitude. These results demonstrate the fusion model's reliability and establish a strong foundation for data-driven property prediction and the design of high-conductivity n-type polymers. Deep learning ; Convolutional neural network ; Artificial neural network ; Polymer ; Reliability (semiconductor) ; Conductive polymer ; Conductivity ; Set (abstract data type) ; Organic semiconductor ; Fusion Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: Guilherme M. Arantes | Jan Řezáč
Source: ChemRxiv (preprint, 2025-04-30)
Field: Materials Science — Materials Chemistry

Benchmark of approximate quantum chemical and machine learning potentials for biochemical proton transfer reactions Proton transfer reactions are among the most common chemical transformations and are central to enzymatic catalysis and bioenergetic processes. Their mechanisms are often investigated using DFT or approximate quantum chemical methods, whose accuracy directly impacts the reliability of the simulations. Here, a comprehensive set of semiempirical molecular-orbital and tight-binding DFT approaches, along with recently developed machine learning (ML) potentials, are benchmarked against high-level MP2 reference data for a curated set of proton transfer reactions representative of biochemical systems. Relative energies, geometries and dipole moments are evaluated for isolated reactions. Microsolvated reactions are also simulated using a hybrid QM/MM partition. Traditional DFT methods offer high accuracy in general but show markedly larger deviations for proton transfers involving nitrogen-containing groups. Among approximate models, RM1, PM6, PM7, DFTB2-NH, DFTB3 and GFN2-xTB show reasonable accuracy across properties, though their performance varies by chemical group. Strikingly, the ML-corrected ($\Delta$-learning) model PM6-ML improves accuracy for all properties and chemical groups, and transfers well to QM/MM simulations. Conversely, standalone ML potentials perform poorly for most reactions. These results provide a basis for evaluating approximate methods and selecting potentials for proton transfer simulations in complex environments. Benchmark (surveying) ; Proton ; Quantum chemical ; Quantum ; Transfer (computing) ; Computer science ; Transfer of learning ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Parallel computing ; Cartography Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Various Chemistry Research Topics
Authors: Chen Li | Rui Zhang | Peijie Ma | Kun Zheng
Source: Advanced Functional Materials (article, 2025-01-19)
Field: Materials Science — Materials Chemistry

In‐Depth Discussion on Electrocatalytic Barrier with Electron Structure of High‐Entropy Alloy Predicted by Transfer Learning and Neural Networks Abstract Alloy electrodes, beneficial from excellent stability, are considered suitable for industrial applications, hence exploring alloy catalysts with low reaction barriers will bring innovative scientific understanding and enormous economic benefits. Recently, material informatics emerges as an efficient method in the research and development of new materials through diverse candidates, however, collecting a large amount of material characterization and simulation data still faces numerous difficulties. To tackle this issue, combining the topological structure of materials, the convolutional neural network framework developed in this article first achieves the density of states prediction of active sites on the alloy surface, based on which the adsorption energy of different reactants is obtained. Benefited by electronic structure, this model exhibits excellent predictive performance with a mean absolute error of 0.124 eV, and transferability with fast convergence under dozens transferred data to complete the extension for high entropy alloys and reactants. Based on this massive predictive data, high entropy alloy catalysts with excellent low reaction barrier have been discovered, and several catalytic theories, like scaling relations, d‐band center theory, high‐entropy effects and synergistic catalysis, have been validated and improved. Materials science ; Alloy ; High entropy alloys ; Electron transfer ; Artificial neural network ; Electron ; Transfer of learning ; Chemical physics ; Statistical physics ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Metallurgy ; Artificial intelligence ; Computer science ; Physical chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Cheng Li | Pan‐Lin Shao | Jiahui Lv | Hongjun Xiao | Yanping Sun | Jingkai Yang | Ziyi Xu | Mingkun Lv | Guanghui Wang | Songlin Zhao | Jiaxin Li | Ziqi Jin | Xuan Tan | Guichuan Xing | Bo Zhang
Source: Nature Computational Science (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation Stereoselectivity ; Asymmetric hydrogenation ; Olefin fiber ; Absolute configuration ; Enantiomer ; Catalysis ; Enantioselective synthesis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Jasper De Landsheere | Alexander Zamyatin | Johannes Karwounopoulos | Esther Heid
Source: ChemRxiv (preprint, 2025-07-16)
Field: Materials Science — Materials Chemistry

ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models The accurate prediction of chemical reaction properties, such as yields and activation energies, is fundamental to chemical synthesis and industrial process optimization. In recent years, deep learning has emerged as a powerful tool for this task, achieving accuracies comparable to traditional quantum mechanical methods at a fraction of the computational cost. Models tackling different tasks, from reaction outcome prediction to retrosynthesis, have been developed, leveraging different representations like molecular graphs or text-based notations. However, this progress has led to a fragmented ecosystem of specialized, incompatible codebases. The lack of a unified framework and standardized evaluation protocols makes it difficult to compare methods fairly, limits the development of new approaches, and hinders the assessment of the true real-world performance of models. Here, we introduce ChemTorch, a modular open-source framework designed to unify and streamline the development and benchmarking of deep learning models for chemical reactions. Using ChemTorch, we conduct a systematic study on reaction barrier height prediction and demonstrate how common but unrealistic benchmarking practices, such as providing ground-truth atom-to-atom mappings or 3D geometries at test time, lead to overly optimistic performance estimates. By re-evaluating models in a setting that withholds such privileged information, we reveal significant performance drops that better reflect the challenges of real-world deployment. We therefore propose a new set of benchmarking guidelines that promote end-to-end evaluation, where models must operate on readily available 2D chemical structures without relying on computationally expensive data. ChemTorch provides the community with a powerful tool to accelerate research and promote reproducibility, and by adopting these more rigorous evaluation standards, the field can shift its focus towards developing models that are not only accurate in benchmarks but are genuinely applicable in practice. Benchmarking ; Computer science ; Artificial intelligence ; Modular design ; Deep learning ; Machine learning ; Task (project management) ; Data science ; Industrial engineering ; Systems engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Ajnabiul Hoque | Taiwei Chang | Jin‐Quan Yu | Raghavan B. Sunoj
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Molecular Machine Learning Approach to Enantioselective C–H Bond Activation Reactions: From Generative AI to Experimental Validation Molecular machine learning (ML) has gained considerable attention in recent years. Developing ML algorithms for chemical reaction prediction is a formidable task, due to the small-sized reaction data it often presents, besides the sparsity and skewed distribution. While previous ML studies offered effective predictions on known reactions, efforts in using deep generative models for guiding new reactions and their prospective validation are rare. We harness both predictive and explorative abilities of deep learning on an important catalytic asymmetric β-C(sp<sup>3</sup>)-H activation reaction, consisting of 220 experimentally reported examples that differs primarily in terms of the substrate, catalyst, and coupling partner. A transfer learning approach using a chemical language model, pretrained on 1 million unlabeled molecules followed by fine-tuning on this reaction data set, is adopted. Our ensemble prediction (EnP) model, where 30 fine-tuned CLMs concurrently predict the %<i>ee</i> of test set reactions, is highly reliable. Another language model, fine-tuned on the 77 known chiral ligands as used in the above reactions, is employed for generating novel ligands of high validity and novelty. A proof of concept wet-lab experimental validation reveals that most of the ML-generated reactions are in excellent agreement with the EnP predictions. Results also caution the prospects of ML-driven reaction development for ligand design and emphasize the importance of domain experts in key decisions. Enantioselective synthesis ; Generative grammar ; Chemistry ; Artificial intelligence ; Computer science ; Machine learning ; Combinatorial chemistry ; Organic chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Machine Learning in Materials Science
Authors: Zhuo Cheng | Qichang Meng | Xiaofeng Jiang | Sudeshna Gun | Liang‐Shih Fan
Source: Discover Energy (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Machine learning-driven predictive design of catalytic oxygen carriers for chemical looping processes Chemical looping is an innovative technology for conversion of fossil fuels to clean energy with integrated carbon dioxide capture. The success of this technology depends on the performance of oxygen carriers which circulate between the reactors. However, traditional trial-and-error methods for oxygen carrier design are time-consuming and resource-intensive. In recent years, machine learning techniques have rapidly evolved and been applied to material discovery. These techniques can efficiently screen a vast number of materials to find the most promising candidates based on their predicted properties. This perspective summarizes recent advances in utilizing machine learning for catalyst development and explores its application in the design of catalytic oxygen carriers for chemical looping processes. By integrating machine learning with high-throughput simulations, we can gain deeper insights into the active sites, oxygen vacancies, and redox reaction mechanisms of catalytic oxygen carriers, which are crucial for the development of these high-performance chemical looping materials. Additionally, we discuss the key challenges and opportunities of this approach, highlighting its potential for accelerating the application of chemical looping technologies. Oxygen ; Catalysis ; Computer science ; Chemical looping combustion ; Process engineering ; Chemical engineering ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Water Quality Monitoring and Analysis ; Fuel Cells and Related Materials
Authors: Holger‐Dietrich Saßnick | Caterina Cocchi
Source: Advanced Theory and Simulations (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction of Cs–Te with Supervised Machine Learning Abstract Crystal structure prediction methods aim to determine the ground‐state crystal structure for a given material. The vast combinatorial space associated with this problem makes conventional methods computationally prohibitive for routine use. To overcome these limitations, a novel approach combining high‐throughput density functional theory calculations with machine learning is proposed. It predicts stable crystal structures within binary and ternary systems by systematically evaluating various structural descriptors and machine learning algorithms. The superiority of models based on atomic coordination environments is shown, with transfer‐learned graph neural networks emerging as a particularly promising technique. By validating the proposed method on Cs–Te crystals, its ability to generate stable crystal structures is proved, suggesting its potential for advancing established computational schemes. Crystal structure prediction ; Artificial intelligence ; Machine learning ; Crystal structure ; Materials science ; Computer science ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Chanseok Kim | Mina Yoon | Jun Hee Lee
Source: Computational Materials Science (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Accelerated discovery of OER catalysts in Pnma perovskites via machine learning with minimal DFT structure relaxation Density functional theory ; Perovskite (structure) ; Relaxation (psychology) ; Convolutional neural network ; Crystal (programming language) ; Crystal structure ; Catalysis ; Metal Catalytic Processes in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Muhammad Asad | Inas A. Ahmed | Farhan Zafar | AshwiniH Archana. M. Hatti S. S. Imran | Muhammad Ali Khan | Naeem Akhtar | Muhammad Hammad Athar | Zahid Shafiq
Source: Electrochimica Acta (article, 2025-09-16)
Field: Chemistry — Organic Chemistry

Machine learning optimized synthesis of Ag/Nitrodopamine-MWCNT hybrid for durable and high-performance OER catalysis Electrocatalyst ; Overpotential ; Tafel equation ; Nanoparticle ; Oxygen evolution ; Catalysis Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Burns, Jackson W. | Zalte, Akshat Shirish | Abreu, Charlles R. A. | Sieg, Jochen | Feldmann, Christian | Mathea, Miriam | Green, William H.
Source: arXiv (Cornell University) (preprint, 2025-06-18)
Field: Materials Science — Materials Chemistry

Deep Learning Foundation Models from Classical Molecular Descriptors Fast and accurate data-driven prediction of molecular properties is pivotal to scientific advancements across myriad chemical domains. Deep learning methods have recently garnered much attention, despite their inability to outperform classical machine learning methods when tested on practical, real-world benchmarks with limited training data. This study seeks to bridge this gap with CheMeleon, a O(10M) parameter foundation model that enables directed message-passing neural networks to finally exceed the performance of classical methods. Evaluated on 58 benchmark datasets from Polaris and MoleculeACE, CheMeleon achieves a win rate of 75% on Polaris tasks, outperforming baselines like Random Forest (68%), fastprop (36%), and Chemprop (32%), and a 97% win rate on MoleculeACE assays, surpassing Random Forest (50%) and other foundation models. Unlike conventional pre-training approaches that rely on noisy experimental data or biased quantum mechanical simulations, CheMeleon utilizes low-noise molecular descriptors to learn rich and highly transferable molecular representations, suggesting a new avenue for foundation model pre-training. Deep learning ; Foundation (evidence) ; Benchmark (surveying) ; Random forest ; Bridge (graph theory) ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks