jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jyoti Singh | R. K. Thareja | Pragati Malik
Source: ChemPhysChem (review, 2025-02-18)
Field: Materials Science — Materials Chemistry

Exploring the Potential of Quantum Dot‐Sensitized Solar Cells: Innovation and Insights Photovoltaic technologies have garnered significant attention towards generating renewable and clean energy from solar power. Quantum-dot-sensitized solar cells represent a promising third-generation photovoltaic technology that offers alternatives to conventional silicon-based solar cells due to their unique properties, their favourable optoelectronic properties for photovoltaic applications including simplified manufacturing, lower processing temperatures, enhanced flexibility, semi-transparent design, and a theoretical efficiency up to 44 %. The unique characteristic of tailoring the size and composition of quantum dots makes them valuable absorber materials capable of efficiently harnessing a broader range of the solar spectrum. The potential of quantum dot-sensitized solar cells to revolutionize the field of photovoltaic technology is a cause for optimism. However, the major limitation of the overall power conversion efficiency lies in their inability to absorb ultraviolet and near-infrared. Therefore, a photovoltaic technology that can effectively harness the entire solar spectrum becomes imperative. This review discusses the synthesis and light conversion mechanisms of these solar cells. Additionally, it offers an overview of the various advancements made in quantum dot-sensitized solar cells for enhancement in the efficiency of energy conversion. It focuses on the light-absorbing materials used, their efficiency, and the advantages and drawbacks of quantum dot solar cell technology. Photovoltaic system ; Quantum dot ; Solar cell ; Energy conversion efficiency ; Optoelectronics ; Materials science ; Photovoltaics ; Nanotechnology ; Solar energy ; Engineering physics ; Quantum dot solar cell ; Polymer solar cell ; Physics ; Electrical engineering ; Engineering Quantum Dots Synthesis And Properties
Authors: Sumner B. Harris | Rama K. Vasudevan | Yongtao Liu
Source: npj Computational Materials (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Active oversight and quality control in standard Bayesian optimization for autonomous experiments Abstract The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization. Bayesian probability ; Quality (philosophy) ; Control (management) ; Bayesian optimization ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Hanxun Jin | Boyu Zhang | Qianying Cao | Enrui Zhang | Aniruddha Bora | Sridhar Krishnaswamy | George Em Karniadakis | Horacio D. Espinosa
Source: Advanced Materials (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators Machine learning (ML) is emerging as a transformative tool for the design of mechanical metamaterials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro-scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, a comprehensive end-to-end scientific ML framework, leveraging deep neural operators (including DeepONet and its variants) is introduced, to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high-quality in situ experimental data. Various neural operators and standard neural networks are systematically compared to identify the model that offers better interpretability and accuracy. The approach facilitates the efficient inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from stochastic spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%. This work underscores that by employing neural operators with advanced nano- and micro-mechanical experiments, the design of complex micro-architected materials with desired properties becomes feasible, even in scenarios constrained by data scarcity. This work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights. Metamaterial ; Interpretability ; Artificial neural network ; Inverse ; Materials science ; Computer science ; Nonlinear system ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Physics ; Engineering ; Mathematics Thermal properties of materials ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Quintina L. Campbell | Sam Cox | Jorge-Luis Pérez-Medina | Brittany Watterson | Andrew Dickson White
Source: ArXiv.org (preprint, 2025-02-13)
Field: Materials Science — Materials Chemistry

MDCrow: Automating Molecular Dynamics Workflows with Large Language Models Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 tasks of varying required subtasks and difficulty, and we evaluate the agent's robustness to both difficulty and prompt style. \texttt{gpt-4o} is able to complete complex tasks with low variance, followed closely by \texttt{llama3-405b}, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models. Workflow ; Computer science ; Dynamics (music) ; Programming language ; Software engineering ; Natural language processing ; Database ; Sociology Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Tsz Wai Ko | Bowen Deng | Marcel Nassar | Luis Barroso-Luque | Runze Liu | Ji Qi | Atul C. Thakur | Amiya Kanta Mishra | Eric Hsien Lung Liu | Gerbrand Ceder | Santiago Miret | Shyue Ping Ong
Source: npj Computational Materials (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry Abstract Graph deep learning models, which incorporate a natural inductive bias for atomic structures, 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, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures 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 provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training. Graph ; Computer science ; Open source ; Chemistry ; Information retrieval ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiao-Qin Cao | Yongqing Zhang | Zhenghua Sun | Hongyao Yin | Yujun Feng
Source: Progress in Materials Science (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine learning in polymer science: A new lens for physical and chemical exploration Materials science ; Polymer ; Lens (geology) ; Nanotechnology ; Composite material ; Engineering ; Petroleum engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Microfluidic and Capillary Electrophoresis Applications
Authors: Wail Al Zoubi | Yujun Sheng | Iftikhar Hussain | Heo Seongjun | Nokeun Park
Source: Coordination Chemistry Reviews (article, 2025-04-03)
Field: Materials Science — Materials Chemistry

Multi-principal element nanoparticles: Synthesis strategies and machine learning prediction Chemistry ; Nanoparticle ; Principal (computer security) ; Element (criminal law) ; Nanotechnology ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Laser-Ablation Synthesis of Nanoparticles
Authors: Qiuhong Yuan | Xiaobei Wang | Dongdong Xu | Hongyan Liu | Hanwen Zhang | Yu Qian | Yanliang Bi | Lixin Li
Source: Catalysts (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Catalysts for Advanced Oxidation Processes: Progress, Challenges, and Prospects Advanced oxidation processes (AOPs) are recognized as one of the most effective methods in the field of wastewater treatment, and the selection of catalysts in the oxidation process is very important. In the face of the traditional test trial-and-error method, the method of screening advanced oxidation catalysts is time-consuming and inefficient. This paper examines approximately two decades’ worth of literature pertaining to the development of catalysts facilitated by machine learning. A synopsis of the various advanced oxidation processes and reactive oxygen species (ROS) is provided. Subsequently, it is posited that the swift advancement of machine learning (ML) and its algorithmic classification has significantly propelled the progress in ML-assisted catalyst screening, active site prediction, the discovery of acceleration mechanisms, and catalyst structural research, which are subsequently elucidated. Despite ML’s proven efficacy as a tool within the domain of AOPs’ catalysis, the article concludes by presenting challenges and outlining future development strategies, particularly in light of issues pertaining to data quality and quantity, as well as inherent model limitations. Computer science Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Daeun Chloe Shin | Hakan Karasu | Kyojin Jang | Chang Soo Kim | Kyeongsu Kim | Dongjin Kim | Young Jin | Ki Bong Lee | Keun Hwa Chae | Il Moon | Da Hye Won | Jonggeol Na | Ung Lee
Source: ACS Catalysis (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Discovering the Origin of Catalyst Performance and Degradation of Electrochemical CO<sub>2</sub> Reduction through Interpretable Machine Learning Catalyst degradation is a significant challenge for the commercialization of the electrochemical reduction of CO2, as it decreases activity and selectivity. However, the high experimental cost of catalyst characterization hinders the generation of sufficient and valuable information regarding catalyst degradation. Recently, machine learning (ML) models have exhibited high potential to replace costly processes, but their low interpretability makes their application challenging. Herein, we introduce an interpretable ML framework that accurately projects the catalyst status using simple linear sweep voltammetry (LSV) within subseconds while providing insights into the origin of catalyst degradation. A convolutional neural network trained on experimentally collected 5196 LSV results achieved superior performance in total current and Faradaic efficiency predictions. The ML framework demonstrates an impressive accuracy of mean absolute error below 0.5% in predicting the Faradaic efficiency of various products, irrespective of the operating conditions and catalyst types. The prediction mechanism learnt by the model was interpreted via explainable artificial intelligence (XAI), and critical degradation factors were identified. We performed catalyst surface analyses at milestone points to verify the XAI interpretation and demonstrate the reliability of the proposed framework. This approach can potentially be applied to a wide range of electrochemistry involving catalytic process, battery degradation, and chemical process monitoring, suggesting that it offers a viable means of rapidly and reliably monitoring performance. Catalysis ; Degradation (telecommunications) ; Reduction (mathematics) ; Electrochemistry ; Computer science ; Chemistry ; Materials science ; Electrode ; Mathematics ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Fuel Cells and Related Materials
Authors: Chenggong Jiang | Bill Yan | Bryan R. Goldsmith | Suljo Linic
Source: Nature Catalysis (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Predictive model for the discovery of sinter-resistant supports for metallic nanoparticle catalysts by interpretable machine learning Artificial neural network ; Nanomaterial-based catalyst ; Nanoparticle ; Molecular dynamics ; Stability (learning theory) ; Key (lock) ; Feature (linguistics) Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ge-Hao Liang | Heng-Su Liu | Ximing Zhang | Jianfeng Li | Shisheng Zheng
Source: Journal of Energy Chemistry (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Topology-based machine learning for predicting curvature effects in metal-nitrogen-carbon single-atom catalysts Curvature ; Catalysis ; Nitrogen atom ; Atom (system on chip) ; Carbon fibers ; Metal ; Topology (electrical circuits) ; Nitrogen ; Carbon atom ; Materials science ; Chemistry ; Computer science ; Mathematics ; Metallurgy ; Combinatorics ; Geometry ; Parallel computing ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Surface Chemistry and Catalysis
Authors: Anas A. Rahman | Bo Wang | Jiahao Yu | Yunfei Gao | Yuanxin He | Tao Jin | Zhihua Gan
Source: Applied Thermal Engineering (article, 2025-01-11)
Field: Chemistry — Spectroscopy

Machine learning approaches for the prediction of thermal performance of multilayer insulation materials at low temperatures Materials science ; Thermal insulation ; Thermal ; Mechanical engineering ; Composite material ; Process engineering ; Engineering ; Thermodynamics ; Physics Aerogels and thermal insulation ; High voltage insulation and dielectric phenomena ; Building Energy and Comfort Optimization
Authors: Hao Wang | Yue Zhu | Jinliang Li | Xinjuan Liu | Yongchao Ma | Ye‐Feng Yao | Jie Zhang | Likun Pan
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated exploration on element doping-triggering material performance improvement for energy conversion and storage applications The prediction performances of machine learning in the field of element-doped materials for energy conversion and storage applications are summarized. Doping ; Element (criminal law) ; Materials science ; Energy storage ; Computer science ; Process engineering ; Engineering ; Optoelectronics ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Debasis Maji | Atish Ghosh | Debaditya Barman | Pranab Sarkar
Source: The Journal of Physical Chemistry Letters (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Accelerating Molecular Dynamics with a Graph Neural Network: A Scalable Approach through E(q)C-GNN Ab initio molecular dynamics simulations are an integral part of any electronic structure calculation to access thermal stability and perform non-adiabatic dynamics but are computationally very demanding. To enhance the computational efficiency of crucial ab initio molecular dynamics simulations, in this work, we implemented the graph neural network (GNN)-accelerated predictions for the molecular dynamics simulation of two-dimensional systems with varying atom connectivity. In this work, we developed an equivariant GNN model that employs only the time-evolved AIMD-simulated atomic coordinates for training and successfully predicts the key parameters of stable two-dimensional g-CN, WTe<sub>2</sub>, and g-CN/WTe<sub>2</sub>, like potential energy and kinetic energy, while also delving into the structural and thermodynamical parameters like entropy and interatomic force variation, resulting in a fluctuation level of ±3%, and the computational speed has improved by several orders of magnitude. Hence, incorporating an equivariant GNN model will serve as a viable substitute for predictions in extensive AIMD simulations of homogeneous or symmetrically periodic low-dimensional materials. Molecular dynamics ; Adiabatic process ; Statistical physics ; Computer science ; Kinetic energy ; Scalability ; Work (physics) ; Ab initio ; Artificial neural network ; Entropy (arrow of time) ; Potential energy ; Physics ; Classical mechanics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; 2D Materials and Applications
Authors: Felix Pultar | Moritz Thürlemann | Igor Gordiy | Eva Doloszeski | Sereina Riniker
Source: Journal of the American Chemical Society (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution We present the design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM) simulations. Substitution of a computationally expensive QM Hamiltonian by an NNP with the same accuracy largely reduces the computational cost and enables efficient sampling in prospective MD simulations, the main limitation faced by traditional QM/MM setups. The model relies on the recently introduced anisotropic message passing (AMP) formalism to compute atomic interactions and encode symmetries found in QM systems. AMP is shown to be highly efficient in terms of both data and computational costs and can be readily scaled to sample systems involving more than 350 solute and 40,000 solvent atoms for hundreds of nanoseconds using umbrella sampling. Most deviations of AMP predictions from the underlying DFT ground truth lie within chemical accuracy (4.184 kJ mol<sup>-1</sup>). The performance and broad applicability of our approach are showcased by calculating the free-energy surface of alanine dipeptide, the preferred ligation states of nickel phosphine complexes, and dissociation free energies of charged pyridine and quinoline dimers. Results with this ML/MM approach show excellent agreement with experimental data and reach chemical accuracy in most cases. In contrast, free energies calculated with static DFT calculations paired with implicit solvent models or QM/MM MD simulations using cheaper semiempirical methods show up to ten times higher deviation from the experimental ground truth and sometimes even fail to reproduce qualitative trends. Chemistry ; Artificial neural network ; Biological system ; Computational chemistry ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Haneen Alzamer | Russlan Jaafreh | Jung-Gu Kim | Kotiba Hamad
Source: Crystals (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery Recent advancements in artificial intelligence (AI), particularly in algorithms and computing power, have led to the widespread adoption of AI techniques in various scientific and engineering disciplines. Among these, materials science has seen a significant transformation due to the availability of vast datasets, through which AI techniques, such as machine learning (ML) and deep learning (DL), can solve complex problems. One area where AI is proving to be highly impactful is in the design of high-performance Li-ion batteries (LIBs). The ability to accelerate the discovery of new materials with optimized structures using AI can potentially revolutionize the development of LIBs, which are important for energy storage and electric vehicle technologies. However, while there is growing interest in using AI to design LIBs, the application of AI to discover new electrolytic systems for LIBs needs more investigation. The gap in existing research lies in the lack of a comprehensive framework that integrates AI-driven techniques with the specific requirements for electrolyte development in LIBs. This research aims to fill this gap by reviewing the application of AI for discovering and designing new electrolytic systems for LIBs. In this study, we outlined the fundamental processes involved in applying AI to this domain, including data processing, feature engineering, model training, testing, and validation. We also discussed the quantitative evaluation of structure–property relationships in electrolytic systems, which is guided by AI methods. This work presents a novel approach to use AI for the accelerated discovery of LIB electrolytes, which has the potential to significantly enhance the performance and efficiency of next-generation battery technologies. Electrolyte ; Nanotechnology ; Materials science ; Computer science ; Data science ; Engineering ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Gayatri Rajput | Vijayalakshmi Gosu | Verraboina Subbaramaiah
Source: Journal of environmental chemical engineering (article, 2025-01-31)
Field: Chemistry — Organic Chemistry

Synthesis of copper-impregnated MCM-41 from synthetic and rice husk-derived silica for efficient adsorption of levofloxacin: A machine learning approach Husk ; Copper ; Adsorption ; Chemical engineering ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions ; Zeolite Catalysis and Synthesis ; Adsorption and biosorption for pollutant removal