jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fan Gao | Huiji Li | Zhilong Chen | Yuan‐Qiu‐Qiang Yi | Shihao Nie | Zihao Cheng | Zhuang Liu | Yuanfang Guo | Shumin Liu | Qizhen Qin | Zheng Jian Li | Lisong Zhang | Han Hu | C. C. Li | Liang Yang | Yunhong Wang | Guangxu Chen
Source: Nature Communications (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles Traditional nanomaterial development faces inefficiency and unstable results due to labor-intensive trial-and-error methods. To overcome these challenges, we developed a data-driven automated platform integrating artificial intelligence (AI) decision modules with automated experiments. Specifically, the platform employs a Generative Pre-trained Transformer (GPT) model to retrieve methods/parameters and implements an A* algorithm centered closed-loop optimization process. It achieves optimized diverse nanomaterials (Au, Ag, Cu<sub>2</sub>O, PdCu) with controlled types, morphologies, and sizes, demonstrating efficiency and repeatability. Using the A* algorithm, we comprehensively optimized synthesis parameters for multi-target Au nanorods (Au NRs) with longitudinal surface plasmon resonance (LSPR) peak under 600-900 nm across 735 experiments, and for Au nanospheres (Au NSs)/Ag nanocubes (Ag NCs) in 50 experiments. Reproducibility tests showed deviations in characteristic LSPR peak and full width at half maxima (FWHM) of Au NRs under identical parameters were ≤1.1 nm and ≤ 2.9 nm, respectively. Researchers only need initial script editing and parameter input, significantly reducing human resource requirements. Comparative analysis confirms the A* algorithm outperforms Optuna and Olympus in search efficiency, requiring significantly fewer iterations. Nanorod ; Computer science ; Nanomaterials ; Materials science ; Surface plasmon resonance ; Nanoparticle ; Nanotechnology ; Full width at half maximum ; Repeatability ; Optoelectronics ; Mathematics Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Copper-based nanomaterials and applications
Authors: Félix Therrien | Edward H. Sargent | Oleksandr Voznyy
Source: Nature Communications (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Using GNN property predictors as molecule generators Graph neural networks (GNNs) have emerged as powerful tools to accurately predict materials and molecular properties in computational and automated discovery pipelines. In this article, we exploit the invertible nature of these neural networks to directly generate molecular structures with desired electronic properties. Starting from a random graph or an existing molecule, we perform a gradient ascent while holding the GNN weights fixed in order to optimize its input, the molecular graph, towards the target property. Valence rules are enforced strictly through a judicious graph construction. The method relies entirely on the property predictor; no additional training is required on molecular structures. We demonstrate the application of this method by generating molecules with specific energy gaps verified with density functional theory (DFT) and with specific octanol-water partition coefficients (logP). Our approach hits target properties with rates comparable to or better than state-of-the-art generative models while consistently generating more diverse molecules. Moreover, while validating our framework we created a dataset of 1617 new molecules and their corresponding DFT-calculated properties that could serve as an out-of-distribution test set for QM9-trained models. Property (philosophy) ; Molecule ; Computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dipayan Das | Birson Ingti | P. K. Paul | Jaya Pati Jamatia | Toi Ete | Tahira Khan | J. Kalita
Source: Discover Soil. (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Soil microbial dynamics in response to the impact of nanoparticles on agricultural implications Nanotechnology is revolutionising agriculture by enhancing crop productivity, nutrient efficiency, and pest management while promoting sustainability. Nano-fertilisers minimise nutrient loss, nano-pesticides precisely target pests, and nanosensors improve precision farming by detecting contaminants. These advancements contribute to higher yields, resource conservation, and food security. Nonetheless, the impact of nanotechnology on soil microbial communities remains a critical concern. Soil microbes, including bacteria and fungi, play essential roles in nutrient cycling, soil structure, and plant health. Sustainable farming relies on maintaining microbial diversity, which supports nutrient uptake, disease suppression, and symbiotic plant interactions. The introduction of nanoparticles into agricultural soils alters soil structure, chemistry, and biological processes. Metal-based nanoparticles can disrupt nutrient cycles, microbial activity, and even induce genetic mutations affecting plant growth. Nanoparticles generate reactive oxygen species, leading to oxidative stress in soil organisms, which can alter microbial physiology and ecosystem balance. The toxicity of nanoparticles varies based on their chemical composition, solubility, and surface properties. Nanoparticles enter microbial cells through passive diffusion, endocytosis, or membrane disruption, potentially causing cytotoxicity and immune responses. Microbes counteract nanotoxicity through antioxidant production, membrane modifications, efflux pumps, and genetic adaptations such as horizontal gene transfer and biofilm formation. Understanding microbial responses to nanotoxicity is essential for developing sustainable agricultural strategies. Regulating nanoparticle use, optimising nanoparticle design, and implementing soil management practices will be crucial for mitigating risks while leveraging nanotechnology’s benefits. By analysing these interactions, the review seeks to provide insights into the sustainable application of nanotechnology in agriculture while mitigating potential risks to soil health and ecosystem balance. Agriculture ; Dynamics (music) ; Environmental science ; Soil science ; Agronomy ; Ecology ; Biology ; Psychology Nanoparticles: synthesis and applications ; Heavy metals in environment
Authors: Songjiang Lu | Xu Zhang | Yanan Hu | Jielei Chu | Qianhua Kan | Guozheng Kang
Source: Mechanics of Materials (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Machine learning-based constitutive parameter identification for crystal plasticity models Crystal plasticity ; Materials science ; Constitutive equation ; Identification (biology) ; Plasticity ; Composite material ; Mechanical engineering ; Biological system ; Structural engineering ; Mechanics ; Artificial intelligence ; Finite element method ; Computer science ; Engineering ; Physics Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Igor Poltavsky | Mirela Puleva | Anton Charkin-Gorbulin | Grégory Fonseca | Ilyes Batatia | Nicholas J. Browning | Stefan Chmiela | Mengnan Cui | J. Thorben Frank | Stefan Heinen | Bing Huang | Silvan Käser | Adil Kabylda | Danish Khan | Carolin Müller | Alastair J. A. Price | Kai Riedmiller | Kai Töpfer | Tsz Wai Ko | Markus Meuwly | Matthias Rupp | Gábor Cśanyi | O. Anatole von Lilienfeld | Johannes T. Margraf | Klaus-Robert Müller | Alexandre Tkatchenko
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023 We present the second part of the rigorous evaluation of modern machine learning force fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of the performance of MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19* in modeling molecules, molecule-surface interfaces, and periodic materials. We compare observables obtained from molecular dynamics (MD) simulations using different MLFFs under identical conditions. Where applicable, density-functional theory (DFT) or experiment serves as a reference to reliably assess the performance of the ML models. In the absence of DFT benchmarks, we conduct a comparative analysis based on results from various MLFF architectures. Our findings indicate that, at the current stage of MLFF development, the choice of ML model is in the hands of the practitioner. When a problem falls within the scope of a given MLFF architecture, the resulting simulations exhibit weak dependency on the specific architecture used. Instead, emphasis should be placed on developing complete, reliable, and representative training datasets. Nonetheless, long-range noncovalent interactions remain challenging for all MLFF models, necessitating special caution in simulations of physical systems where such interactions are prominent, such as molecule-surface interfaces. The findings presented here reflect the state of MLFF models as of October 2023. Molecular dynamics ; Molecule ; Nanotechnology ; Crash ; Force field (fiction) ; Computer science ; Materials science ; Chemical physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electronic and Structural Properties of Oxides
Authors: Sheila E. Whitman | Marat I. Latypov
Source: Acta Materialia (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Machine learning of microstructure–property relationships in materials leveraging microstructure representation from foundational vision transformers Microstructure ; Materials science ; Transformer ; Representation (politics) ; Artificial intelligence ; Metallurgy ; Computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Xianfeng Lin | Lixin Kang | Jiaqi Feng | Nuo Duan | Zhouping Wang | Shijia Wu
Source: Analytical Chemistry (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Deep Learning-Assisted Fluorescence Single-Particle Detection of Fumonisin B<sub>1</sub> Powered by Entropy-Driven Catalysis and Argonaute Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein, a deep learning-assisted and entropy-driven catalysis (EDC)-Argonaute powered fluorescence single-particle aptasensing platform was developed for ultrasensitive detection of fumonisin B<sub>1</sub> (FB<sub>1</sub>) using single-stranded DNA modified with biotin and red fluorescence-encoded microspheres as a signal probe and streptavidin-conjugated magnetic beads as separation carriers. The binding of aptamer with FB<sub>1</sub> releases the trigger sequence to mediate EDC cycle to produce numerous 5'-phosphorylated output sequences, which can be used as the guide DNA to activate downstream <i>Thermus thermophilus</i> Argonaute (<i>Tt</i>Ago) for cleaving the signal probe, resulting in increased number of fluorescence microspheres remaining in the final reaction supernatant after magnetic separation. Subsequently, through fast and accurate counting of red bright particles in the captured confocal fluorescence images from the supernatant via a YOLOv9 deep learning model, the sensitive and specific detection of FB<sub>1</sub> could be realized. This approach has a limit of detection (LOD) of 0.89 pg/mL with a linear range from 1 pg/mL to 100 ng/mL, and satisfactory recovery (87.2-113.5%) in real food samples indicates its practicality. The integration of the aptamer and EDC with <i>Tt</i>Ago broadens the target range of Argonaute and enhances sensitivity. Furthermore, incorporating deep learning significantly improves the analytical efficiency of single-particle detection. This work provides a promising analytical strategy in biosensing and promotes the application of fluorescence single-particle detection in food safety monitoring. Chemistry ; Fluorescence ; Catalysis ; Argonaute ; Nanotechnology ; Combinatorial chemistry ; Biochemistry ; RNA ; Gene ; Quantum mechanics ; RNA interference Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Ritesh Kumar | Minh Canh Vu | Peiyuan Ma | Chibueze V. Amanchukwu
Source: Chemistry of Materials (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Electrolytomics: A Unified Big Data Approach for Electrolyte Design and Discovery Electrolyte discovery is the bottleneck for developing next-generation batteries. For example, lithium metal batteries (LMBs) promise to double the energy density of current Li-ion batteries (LIBs), while next-generation LIBs are desired for operations at extreme temperature conditions and with high voltage cathodes. However, there are no suitable electrolytes to support these battery chemistries. Electrolyte requirements are complex (conductivity, stability, safety), and the chemical design space (salts, solvents, additives, concentration) is practically infinite; hence, discovery is primarily guided through trial and error, which slows the deployment of such next-generation battery chemistries. Inspired by artificial intelligence (AI)-enabled drug discovery, we adapt these machine learning (ML) approaches to electrolyte discovery. We assemble the largest small molecule experimental liquid electrolyte ionic conductivity data set and build highly accurate ML and deep learning models to predict ionic conductivity across a wide range of electrolyte classes. The developed models yield results similar to those of molecular dynamics (MD) simulations and are interpretable without explicit encoding of ionic solvation. While most ML-based approaches target a single property, we build additional models of oxidative stability and Coulombic efficiency and develop a metric called the electrolyte score (eScore) to unify the predicted disparate electrolyte properties. Deploying these models on large unlabeled data sets, we discover distinct electrolyte solvents, experimentally validate that the electrolyte is conductive (>1 mS cm–1), stable up to 6 V, supports efficient anode-free LMB, and even LIB cycling at extreme temperatures. Our work marks a significant step toward efficient electrolyte design, accelerating the development and deployment of next-generation battery technologies. Drug discovery ; Electrolyte ; Computer science ; Big data ; Data science ; Nanotechnology ; Materials science ; Chemistry ; Data mining ; Electrode Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; NMR spectroscopy and applications
Authors: Ying Wang | Zhijie Jiang | Weigang Lu | Dan Li
Source: Angewandte Chemie International Edition (article, 2025-03-11)
Field: Chemistry — Inorganic Chemistry

Machine Learning‐Assisted Exploration of Chemical Space of MOF‐5 Analogs for Enhanced C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> Separation Adsorptive separation using C<sub>2</sub>H<sub>6</sub>-selective adsorbents can produce high-purity C<sub>2</sub>H<sub>4</sub> directly, making it an energy-efficient separation method with the potential to replace cryogenic distillation. Although many C<sub>2</sub>H<sub>6</sub>-selective MOFs have been reported, developing MOFs with both large C<sub>2</sub>H<sub>6</sub> adsorption capacity and high C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> selectivity remains challenging. Herein, we present a machine learning-assisted molecular simulation strategy to explore the C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> separation capability of pcu-MOFs isoreticular to MOF-5. The eXtreme gradient boosting (XGBoost) algorithm showed high accuracy in predicting the C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> selectivity and C<sub>2</sub>H<sub>6</sub> uptake, where Henry coefficient ratio (S<sup>0</sup>) and Henry coefficient of C<sub>2</sub>H<sub>6</sub> (K(C<sub>2</sub>H<sub>6</sub>)) were identified as key factors. We further synthesized the top-performing MOF termed A-66 and experimentally verified its large C<sub>2</sub>H<sub>6</sub> adsorption capacity and excellent C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> separation performance. This work provides a valuable strategy for exploring the chemical space of MOF-5 analogs and identifying promising candidates for the efficient purification of C<sub>2</sub>H<sub>4</sub> from C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> mixtures. Adsorption ; Selectivity ; Distillation ; Materials science ; Metal-organic framework ; Process engineering ; Chemical engineering ; Chemistry ; Chromatography ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Boron and Carbon Nanomaterials Research
Authors: Lane E. Schultz | Yiqi Wang | Ryan Jacobs | Dane Morgan
Source: npj Computational Materials (article, 2025-04-05)
Field: Materials Science — Materials Chemistry

A general approach for determining applicability domain of machine learning models Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets. Our approach assesses the distance between data in feature space using kernel density estimation, where this distance provides an effective tool for domain determination. We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure. We also show that high measures of dissimilarity are associated with poor model performance (i.e., high residual magnitudes) and poor estimates of model uncertainty (i.e., unreliable uncertainty estimation). Automated tools are provided to enable researchers to establish acceptable dissimilarity thresholds to identify whether new predictions of their own machine learning models are in-domain versus out-of-domain. Computer science ; Domain (mathematical analysis) ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Machine Learning and Data Classification
Authors: Hongbin He | Yuqi Ren | Shengnan Lan | Hongyang Zhang | Yuan‐Hao Zhu | Ruoxuan Peng | Jiancheng Zhou | Maochang Liu | Yitao Si | Dengwei Jing | Naixu Li
Source: Applied Catalysis B: Environmental (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Cross-scale construction of photothermal synergistic catalytic systems: Mechanistic insights from single atoms, clusters to nanoparticles and energy conversion applications Photothermal therapy ; Nanoparticle ; Nanotechnology ; Catalysis ; Materials science ; Scale (ratio) ; Chemical physics ; Chemistry ; Physics ; Organic chemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Ruoyu Wang | Shaoheng Fang | Qixing Huang | Yuanyue Liu
Source: Journal of Chemical Theory and Computation (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Constant-Potential Machine Learning Force Field for the Electrochemical Interface Better understanding and prediction of the electrochemical interface require large-scale atomistic simulations. Machine learning force fields (MLFFs) have proven to be an effective approach. However, current MLFFs typically do not account for the effect of electrode potential, which requires treating interface electrons with a grand canonical ensemble. Here, we develop a constant potential MLFF (CP-MLFF) based on an equivariant graph neural network and implement it into MACE. Specifically, we design an architecture that can take the number of electrons as the input and accurately predict the Fermi level. The CP-MLFF allows us to examine the convergency of the electrochemical barrier with respect to sampling, which we demonstrate through the example of CO<sub>2</sub> reduction on the Ni-N-C catalyst. Our work provides a useful method and tool enabling accurate and efficient large-scale simulation of the electrochemical interface. Computer science ; Interface (matter) ; Electron ; Force field (fiction) ; Constant (computer programming) ; Electrochemical potential ; Artificial neural network ; Electrochemistry ; Electrode ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Xue Jia | Tianyi Wang | Di Zhang | Xuan Wang | Heng Liu | Liang Zhang | Hao Li
Source: Journal of Catalysis (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives • Data science accelerates electrocatalyst discovery for sustainable energy applications. • DFT-derived parameters enable volcano plots and predictive models for reactions. • Transition from DFT-based descriptors to high-dimensional data analysis. • Machine learning reveals complex patterns beyond traditional descriptor approaches. The integration of data science into electrocatalysis has revolutionized the discovery of high-performance catalysts for sustainable energy applications. To emphasize the role of data science and guide future research in electrocatalyst design, this mini-review traces the evolution from low-dimensional data science—rooted in density functional theory (DFT) descriptors such as d -band center and binding/adsorption energies—to high-dimensional analytics powered by large-scale computational datasets and machine learning (ML). First, DFT-derived parameters establish predictive volcano models for various electrochemical reactions, linking atomic-scale descriptors to macroscopic performance within the framework of low-dimensional data science. Meanwhile, with the development of large-scale datasets, ML deciphers complex structure–property relationships, accelerating the design of promising electrocatalysts. Additionally, machine learning potentials (MLPs) bridge quantum precision and scalability, not only accelerating thermodynamic adsorption energy calculations but also enabling simulations of dynamic catalytic mechanisms more efficiently. Finally, we discuss emerging opportunities to deepen data science’s impact. This mini-review highlights the transformative role of data science in bridging theoretical insights, computational efficiency, and experimental validation, ultimately accelerating the design of next-generation electrocatalysts for a sustainable energy future. Chemistry ; Electrocatalyst ; State (computer science) ; Lens (geology) ; Through-the-lens metering ; Nanotechnology ; Electrochemistry ; Physical chemistry ; Optics ; Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials