jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan G. Rittig | Manuel Dahmen | Martin Grohe | Philippe Schwaller | Alexander Mitsos
Source: JuSER (Forschungszentrum Jülich) (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

Molecular Machine Learning in Chemical Process Design We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing highly accurate predictions for properties of pure components and their mixtures, and (ii) exploring the chemical space for new molecular structures. We review current state-of-the-art molecular ML models and discuss research directions that promise further advancements. This includes ML methods, such as graph neural networks and transformers, which can be further advanced through the incorporation of physicochemical knowledge in a hybrid or physics-informed fashion. Then, we consider leveraging molecular ML at the chemical process scale, which is highly desirable yet rather unexplored. We discuss how molecular ML can be integrated into process design and optimization formulations, promising to accelerate the identification of novel molecules and processes. To this end, it will be essential to create molecule and process design benchmarks and practically validate proposed candidates, possibly in collaboration with the chemical industry. Chemical space ; Process (computing) ; Identification (biology) ; Process design ; Chemical process ; Artificial neural network ; Molecular binding ; Molecular graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Tsz Wai Ko | Bowen Deng | Marcel Nassar | Luis Barroso-Luque | Runze Liu | Ji Qi | Eric Hsien Lung Liu | Gerbrand Ceder | Santiago Miret | Shyue Ping Ong
Source: ArXiv.org (preprint, 2025-03-05)
Field: Materials Science — Materials Chemistry

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, our intention is for MatGL to be an extensible ``batteries-included'' library for the development of advanced graph deep learning models for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also includes a variety of pre-trained universal interatomic potentials (aka ``foundational materials models (FMM)'') and property prediction models are also included for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL includes support for Pytorch Lightning for rapid training of models. Deep learning ; Graph ; Benchmarking ; Python (programming language) ; Graph database ; Epigraph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Kun Tang | Yujing Zhao | Lei Zhang | Jian Du | Qingwei Meng | Qilei Liu
Source: ChemRxiv (preprint, 2025-03-28)
Field: Materials Science — Materials Chemistry

Accelerating transition state search and ligand screening for organometallic catalysis with reactive machine learning potential Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations. In this work, a reactive machine learning potential (RMLP) model is developed to expedite transition state optimizations and ligand screenings for organometallic catalysis based on an automatic transition state database construction method and a higher-order equivariant message passing neural network. In a case study involving the ethylene hydrogenation reaction catalyzed by organometallic catalysts, RMLP rapidly predicts potential energy surfaces along intrinsic reaction coordinate paths, achieving speeds nearly three orders of magnitude faster than those of rigorous quantum chemistry calculations. Meanwhile, it maintains comparable accuracy with a root mean square deviation of 0.221 Å for transition state geometries and a mean absolute error of 0.135 kcal/mol for reaction barriers on the external test set, significantly outperforming semi-empirical quantum chemistry methods. Our RMLP model offers an effective alternative to both rigorous and semi-empirical quantum chemistry approaches for rapid and precise transition state optimizations, facilitating high-throughput and large-scale screenings of advanced organometallic catalyst ligands. Catalysis ; Ligand (biochemistry) ; Reactive intermediate ; State (computer science) ; Transition (genetics) ; Computer science ; Organometallic chemistry ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jin Xiao | Yingfeng Zhang | Bowen Li | Shuwen Zhang | Ya Gao | Han Wang | John Z. H. Zhang | Tong Zhu
Source: Research Square (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy Computer science ; Deep learning ; Artificial intelligence ; Density functional theory ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science
Authors: Hui Rong | Yili Chen | Shubo Zhang | Yue Chen | Lin Shen | Wei‐Hai Fang
Source: Chemical Research in Chinese Universities (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Darwin4Matter: A Platform Integrating Machine Learning and Quantum Chemistry for New Materials Design Field (mathematics) ; Quantum dot ; Quantum ; Quantum chemistry ; Layer (electronics) ; Diode Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Priya Vij | Malay K. Nandy | Mamta Pandey
Source: International Journal of Engineering Science and Information Technology (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Machine Learning-guided Synthesis of Quantum Entangled Materials The synthesis of materials into quantum entangled materials is a complicated challenge to an accurate and computational prediction of those materials. In this proposed work, it develops an AI-guided framework based on the combination between machine learning (ML) and reinforcement learning (RL), and quantum simulations to push the designing and validating of quantum materials at a much faster pace. In the first, graph neural networks (GNNs) are used to extract the atomic level quantum features, and in the second, generative models (VAE/GAN) are utilized to discover some novel entangled structures. In addition, fabrication with the synthesis parameters as parameters in the reinforcement learning results in an improvement of the experiment synthesis and a decrease of experiment failures as well as significant improvement of reproducibility. It demonstrates that the proposed hybrid ML-quantum simulation is validated on entanglement fidelity in real-world quantum computing platforms using IBM Qiskit and Google Cirq. As the proposed method is way beyond traditional ones, it has higher quantum coherence time, synthesis efficiency as well as higher prediction accuracy. In addition to enabling scaling-up of cryptography, quantum computing, and next generation nanomaterials, it is a cost and scalable framework for creating next generation quantum technologies applications as it is. And the model is further researched for the generalization in regards to real-time experimental feedback and for the expansion of the framework to a more general quantum materials program. The results show that AI approaches can truly accelerate the quantum material innovation even when syntheses are not at all possible. Quantum entanglement ; Quantum ; Quantum computer ; Coherence (philosophical gambling strategy) ; Reinforcement learning ; Quantum technology ; Scalability ; Quantum sensor ; Quantum network Machine Learning in Materials Science
Authors: Michael Kilgour | Mark E. Tuckerman | Jutta Rogal
Source: ArXiv.org (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

MXtalTools: A Toolkit for Machine Learning on Molecular Crystals We present MXtalTools, a flexible Python package for the data-driven modelling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes of utilities: (1) synthesis, collation, and curation of molecule and crystal datasets, (2) integrated workflows for model training and inference, (3) crystal parameterization and representation, (4) crystal structure sampling and optimization, (5) end-to-end differentiable crystal sampling, construction and analysis. Our modular functions can be integrated into existing workflows or combined and used to build novel modelling pipelines. MXtalTools leverages CUDA acceleration to enable high-throughput crystal modelling. The Python code is available open-source on our GitHub page, with detailed documentation on ReadTheDocs. Python (programming language) ; Workflow ; Documentation ; Modular design ; Crystal (programming language) ; Crystal structure prediction Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jin Xiao (111246) | Yingfeng Zhang (6695606) | Bowen Li (200859) | Shuwen Zhang (3383528) | Ya Gao (526722) | Wei Chen (23863) | Han Wang (254423) | John Z. H. Zhang (7136237) | Tong Zhu (8079)
Source: Figshare (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

A Deep Learning-AugmentedDensity Functional Frameworkfor Reaction Modeling with Chemical Accuracy Accurate prediction of reaction energetics remains a fundamental challenge in computational chemistry, as conventional density functional theory (DFT) often fails to reconcile high accuracy with computational efficiency. Here, we introduce Deep post-Hartree–Fock (DeePHF), a machine learning framework that integrates neural networks with quantum mechanical descriptors to achieve CCSD­(T)-level precision while retaining the efficiency of DFT to solve the reaction problems. By establishing a direct mapping between the eigenvalues of local density matrices and high-level correlation energies, DeePHF circumvents the traditional accuracy-scalability tradeoff. Trained on a limited data set of small-molecule reactions, our model demonstrates superior performance across multiple benchmark data sets, exhibiting exceptional transferability. In fact, its accuracy even surpasses that of advanced double-hybrid functionals, all while maintaining O­(N<sup>3</sup>) scaling. DeePHF offers a promising pathway to bridge the gap between high-level quantum chemistry methods and the practical demands for scalable, accurate models in computational chemistry, and with further refinement, it is poised to make significant contributions to the advancement of chemical reaction modeling. Density functional theory ; Benchmark (surveying) ; Set (abstract data type) ; Eigenvalues and eigenvectors ; Quantum chemical ; Bridge (graph theory) ; Artificial neural network ; Computational model ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Belay Sitotaw Goshu
Source: International Journal of Advanced Nano Computing and Analytics (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Machine Learning with Quantum-Chemical Descriptors and Hybrid Modeling The integration of quantum chemistry with Machine Learning (ML) offers transformative potential for molecular property prediction, critical for drug design and catalyst development. However, challenges like high computational costs and unreliable predictions in safety-critical applications necessitate robust frameworks. This study aims to develop and evaluate a quantum-enhanced ML framework using a Graph Neural Network (GNN) to predict properties like HOMO-LUMO gaps, binding affinities and reaction energies, focusing on Uncertainty Quantification (UQ), active learning and transfer learning to optimize efficiency and reliability. A GNN model, enhanced with quantum descriptors (MO energy, total electron density) from PySCF, was trained on simulated QM9 subsets and real-world proxies. Techniques included monte carlo dropout and ensemble methods for UQ, active learning to reduce quantum calculations and transfer learning for improved convergence. Performance was evaluated using Mean Absolute Error (MAE) across datasets, with visualization of loss, uncertainty and computational cost. The framework achieved a QM9 MAE of 3.406, outperforming baselines (MAE ~3.5-4.0), but showed higher errors (4.2-4.3) for drug and catalyst proxies due to limited quantum data. UQ provided reliable confidence intervals (e.g., [4.2, 5.4]) and active learning reduced costs by 50%. The framework demonstrates efficacy for QM9-like tasks but requires optimization for real-world applications. Future work should scale to larger datasets, integrate quantum computing and validate with experimental data. Artificial neural network ; Monte Carlo method ; Active learning (machine learning) ; Quantum ; Transfer of learning ; Graph ; Property (philosophy) ; Quantum machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum Computing Algorithms and Architecture
Authors: Shiqi Wang | Maja Kubik | Pedro H. C. Camargo
Source: Materials and Interfaces (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Chemically Interpretable Machine Learning for Predicting HER Activity in Au-Based Alloys Hydrogen evolution reaction (HER) catalysts are essential for renewable hydrogen production, but conventional screening through density functional theory (DFT) remains computationally demanding. Here, we present a proof-of-concept machine learning (ML) framework that predicts hydrogen adsorption energies on Au-based alloys using a curated dataset and nine chemically interpretable descriptors. Tree-based ensemble models achieve quantitative accuracy (mean absolute error ≈ 0.13 eV), sufficient to resolve weak, optimal, and strong adsorption regimes. Feature attribution highlights generalized coordination number and site-specific electronic descriptors as dominant factors, consistent with established chemisorption principles. Guided by these insights, the trained model was applied to a library of Au–M alloys, with targeted DFT validation identifying Au–Mo, Au–W, Au–Ta, Au–Hf, and Au–Tc as promising candidates that couple favorable adsorption with alloy stability. Beyond specific predictions, this work introduces an accessible, reproducible workflow that connects ML predictions to catalytic intuition. The framework serves as a reference for experimental chemists, lowering the barrier for integrating data-driven approaches into catalyst discovery. Workflow ; Density functional theory ; Mean absolute error ; Chemisorption ; Artificial neural network ; Adsorption ; Hydrogen Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis for Biomass Conversion
Authors: Jiahui Chen | Yuxin Qiu | Wenyao Chen | Hongye Cheng | Xuezhi Duan | Zhiwen Qi | Zhen Song
Source: Frontiers of Chemical Science and Engineering (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

FusNet: unlocking molecular fusion properties through machine learning Fusion ; Computer science ; Artificial intelligence ; Machine learning ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Khoukha Khoussa | Patrick Lévêque | Larbi Boubchir
Source: Advanced Theory and Simulations (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs ABSTRACT Organic Photovoltaic (OPV) Devices Have Emerged as a Promising Alternative to Conventional Solar Cells due to Their Flexibility, Lightweight Nature, and Potential for Low‐cost Production. However, Optimizing OPV Performance Remains a Complex Challenge, Traditionally Requiring Extensive Experimental Trials or Computational Chemistry Approaches Based on Molecular Descriptors. To Accelerate the Development of High‐efficiency OPVs, Artificial Intelligence (AI) Has Been Increasingly Utilized, Particularly Machine Learning Models That Rely on Chemical Descriptors. While these Methods Have Shown Success, They Are Often Limited by the Quality and Completeness of the Selected Descriptors, Potentially Overlooking Key Structural and Morphological Information. In this Work, We Propose a Novel Deep Learning Framework Leveraging Convolutional Neural Networks (CNNs) to Predict OPV Performance Directly from 2D Images of Donor and Acceptor Materials. By Employing a Customized Representation of Molecular Structures, Our Approach Captures Spatial and Hierarchical Patterns That Traditional Descriptors Based ML Models May Miss. We Compare Our Model's Predictive Capability to Conventional Machine Learning Techniques and Demonstrate Its Potential for Improving Prediction Accuracy and Generalization without Need to Add the Frontier Molecular Orbitals (FMOs) to Enhance Predictions. Our Findings Highlight the Power of Deep Learning in Accelerating the Discovery of Efficient Organic Photovoltaic Materials, Paving the Way for a Data‐driven Approach to Materials Science and Device Optimization. Deep learning ; Convolutional neural network ; Organic solar cell ; Photovoltaic system ; Artificial neural network ; Generalization ; Representation (politics) ; Key (lock) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications
Authors: Jiwon Sun (14461249) | JunHo Song (19774908) | Juo Kim (16698915) | Jinyoung Jeong (2279911) | Eunsong Kim (15925987) | Joonchul Kim (14461255) | Taehyun Park (3632845) | Kyoungmin Min (1473985)
Source: Figshare (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Cation-DisorderedHigh-Entropy Garnet Structures asSolid-State Electrolytes for All-Solid-State Batteries: Machine Learning-DrivenDiscovery Exploring the vast chemical space of high-entropy (HE) solid-state electrolytes (SSEs) has become a highly active area of battery studies owing to the exceptional performance of all-solid-state batteries (ASSBs) with higher energy density and improved safety. The compositional complexity and extensive chemical space inherent to HE SSEs pose significant challenges for their investigation via conventional methodologies such as experimental approaches and density functional theory (DFT) calculations. In this study, we propose a novel material screening methodology aimed at accelerating the exploration of promising HE SSEs while maintaining reasonable computational costs and efficiency. Specifically, we introduce a machine learning (ML)-based screening framework to evaluate 4348 cation-disordered high-entropy (CDHE) garnet-type SSE candidates derived from the well-known Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> structure. First, the ML-based surrogate model was incorporated to screen electron-conducting (bandgap (<i>E</i><sub>g</sub>) < 1 eV) and thermodynamically unfavorable (energy above the convex hull (<i>E</i><sub>hull</sub>) > 0.040 eV/atom). Second, the crystal Hamiltonian graph neural network (CHGNet), a recently developed machine learning interatomic potential, was adapted to find the most stable atomic configuration of each composition with the following further DFT relaxations. Third, the CHGNet potential was fine-tuned on the CDHE garnet-type materials. Then, the elastic properties were calculated with retrained CHGNet to confirm whether the candidate materials could suppress dendrite formation and exhibit interfacial stability. Finally, molecular dynamics (MD) simulations equipped with fine-tuned CHGNet potential were conducted to investigate the lithium diffusion characteristics of CDHE garnet-type materials, resulting in the confirmation of three promising CDHE garnet-type candidates with ionic conductivities exceeding 10<sup>–4</sup> S/cm at room temperature. Density functional theory ; Convex hull ; Electrolyte ; Molecular dynamics ; Chemical space ; Ionic bonding ; Hamiltonian (control theory) ; Artificial neural network ; Solvation Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; High Entropy Alloys Studies
Authors: Liang Li | Manyu Zhu | Ming Li
Source: Tehnicki vjesnik - Technical Gazette (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Research on the Prediction of Nano-Organic Synthesis Reaction Pathways Based on Graph Neural Networks At present, due to the potential biological toxicity risks and economic considerations in the preparation of nanomaterials, their green synthesis and application in environmental governance have attracted much attention.However, this field still faces core challenges: the molecular mechanism of the green synthesis pathway is not yet clear, and the pollutant removal efficiency still has a significant gap compared with traditional methods.In this study, the synthesis conditions of nanometers were optimized through the graph neural network model, and an improved graph neural network algorithm based on molecular segmentation was proposed.Based on the composition mechanism of material molecules and the division of functional groups, an unsupervised learning method is constructed to segment the graph data structure composed of molecules.Combined with the structure after molecular segmentation, a new graph neural network is designed to pay more attention to the local effects of functional groups.Through experiments in databases such as solubility, the improved graph neural network has better prediction performance.Meanwhile, the combination of molecular segmentation and graph interpretation algorithms guides the graph interpretation algorithms to search for substructures containing complete functional groups.For the interpretation of structure-performance, it is more in line with the mechanism of molecular composition and has more practical significance for performance analysis and the design of new materials. Artificial neural network ; Graph ; Segmentation ; Graph theory ; Molecular graph ; Power graph analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks