jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Farhan Ali | Yi‐Sheng Wang
Source: Mass Spectrometry (review, 2025-06-11)
Field: Chemistry — Spectroscopy

Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review Artificial intelligence (AI) has provided viable methods for retrieving, organizing, and analyzing mass spectrometry (MS) data in various applications. However, several challenges remain as this technique is still in its early, preliminary stages. Critical limitations include the need for more effective methods for identification, quantification, and interpretation to ensure rapid and accurate results. Recently, high-throughput MS data have been leveraged to advance machine learning (ML) techniques, particularly in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS and MS imaging (MSI). The accuracy of AI models is intricately linked to the sampling techniques used in MALDI and MALDI imaging measurements. With the help of artificial neural networks, traditional barriers are being overcome, accelerating data acquisition for different applications. AI-driven analysis of chemical specificity and spatial mapping in two-dimensional datasets has gained significant attention, highlighting its potential impact. This review focuses on recent AI applications, particularly supervised ML in MALDI-TOF MS and MALDI-MSI data analysis. Additionally, this review provides an overview of sample preparation methods and sampling techniques essential for ensuring high-quality data in deep learning-based models. Computational biology ; Data science ; Biology ; Computer science Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yahui Cao | Tao Zhang | Xin Zhao | Haotong Li
Source: Journal of Chemical Information and Modeling (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models and chemistry prediction tasks and enable the translation of complex chemical processes into a format that NLP models can understand and learn from. However, previous representation methods fail to adequately consider the hierarchical and structural information inherent in chemical reactions. Here, we propose a tool named HiRXN to learn the comprehensive representation of chemical reactions based on their hierarchical structure. In order to significantly enhance feature engineering for machine learning (ML) models, HiRXN develops an effective tokenization method called RXNTokenizer to capture atomic microenvironment features with multiradius. Then, the hierarchical attention network is used to integrate information from atomic microenvironment-level and molecule-level to accurately understand chemical reactions. The experimental results show that HiRXN is capable of representing chemical reactions and achieves remarkable performance in terms of reaction regression and classification prediction tasks. A web server has been developed to provide a specialized service that accepts Reaction SMILES as input and provides predicted results. The Web site is accessible at http://bdatju.com. Representation (politics) ; Computer science ; Cognitive science ; Artificial intelligence ; Cognitive psychology ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Dominic Robe | Adrian Menzel | Andrew W. Phillips | Elnaz Hajizadeh
Source: Computational Materials Science (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

From SMILES to scattering: Automated high-throughput atomistic polyurethane simulations compared with WAXS data A critical bottleneck in high throughput molecular modeling is the manual declaration of force field parameters. An expert operator must consider the particular environment of each atom to specify its interactions. We address this challenge by developing an end-to-end fully automated workflow, which integrates and extends several software tools (LAMMPS, RDKit, RadonPy, Signac, Psi4, and Freud) to construct, execute, and analyze molecular dynamics simulations of polymers en masse without any operator. We study polyurethanes as a class of materials with a non-trivial multi-block structure and a wide range of achievable properties. Our workflow receives SMILES strings representing hard, soft, and chain extender monomers, and procedurally constructs fully specified models with varied chemistry, molecular weight, and hard component volume fraction. This automatic modeling of polyurethanes required novel implementation of explicit representations of full chemical structures, as well as neighborhood-dependent atomic charges. With these considerations, automatically constructed models reproduced the experimental structure data from WAXS experiments, in spite of model assumptions and computational limitations. Simulations with varying hard segment content indicate that the structure factor interpolates linearly between the extremes of nearly pure hard or soft systems. The effects of temperature, block length, and block connectivity are also investigated systematically. This capability enables fully autonomous high-throughput expansion of computational data sets necessary for machine learning, material screening, and inverse design. • Atomistic models of polyurethanes with arbitrary chemistry and architecture. • Comparison of simulation structure factor calculations with experimental WAXS data. • Systematic survey of effects of various parameters on partial structure factors. Throughput ; Materials science ; Scattering ; Polyurethane ; Molecular dynamics ; Chemical physics ; Chemical engineering ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Optics ; Physics ; Computational chemistry ; Telecommunications ; Engineering Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; CO2 Sequestration and Geologic Interactions
Authors: Jun Yin | Wentao Li | Honghao Chen | Ju Qiu | Huasheng Feng | Xiangya Xu | Qiuyan Jin | Xiaonan Wang
Source: ACS Catalysis (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

CaTS: Toward Scalable and Efficient Transition State Screening for Catalyst Discovery Large-scale screening of materials via machine learning is emerging as an effective strategy for accelerating scientific discovery and industrial applications. Machine learning methods for transition state (TS)-based screening for catalysts remain underexplored due to the scarcity of TS data sets and the inherent challenges of TS searching tasks. Here, we present a framework for large-scale transition states screening for catalysts (CaTS), which uniquely bridges microscopic reaction kinetics and macroscopic computational efficiency by leveraging TS energy, a mechanistically rigorous yet computationally prohibitive descriptor. CaTS integrates automated structure generation with a machine learning force field-based nudged elastic band (NEB) method, enabling high-throughput TS exploration at 104 the speed of density functional theory (DFT). Initially optimized and validated on a small-molecule TS database comprising 10,000 reactions (achieving sub-0.2 eV errors in TS energy prediction) and further applied to a metal–organic complex catalyst (0.16 eV MAE with only 327 training samples), CaTS achieves DFT-level accuracy at 0.01% computational cost. Scaling to over 1000 unseen metal–organic complex structures, it identifies top candidates validated by rigorous DFT. AI-assisted analysis using ChatGPT o3 and SHAP confirms that the predictions are consistent with mechanistic heuristics, providing theoretical validation for large-scale prediction. This paradigm shift from static descriptors to kinetic-resolution screening enables industrial-scale catalyst discovery with atomistic precision. Catalysis ; CATS ; Drug discovery ; Scalability ; Chemistry ; Transition state analog ; Nanotechnology ; Computer science ; Combinatorial chemistry ; Materials science ; Active site ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Dennis Delali Kwesi Wayo | Leonardo Goliatt | D.D. Ganji
Source: Reviews in Chemical Engineering (preprint, 2025-09-27)
Field: Materials Science — Materials Chemistry

DFT and hybrid classical–quantum machine learning integration for photocatalyst discovery and hydrogen production Abstract Photocatalytic hydrogen production is a key pathway toward sustainable energy, driven by semiconductors that utilize sunlight for water splitting. This review highlights recent advances in material design, theoretical modeling, and data-driven discovery. Focus is given to visible-light-active semiconductors with optimal band gaps (1.8–2.4 eV), such as BiVO 4 , g-C 3 N 4 , and CdS, which enable efficient redox reactions. Hybrid architectures, including Pt-loaded TiO 2 and CdS/ZnS core–shell systems, demonstrate hydrogen evolution rates exceeding 10 5 mol m −2 s −1 . Upconversion nanomaterials based on rare-earth-doped fluorides extend light harvesting into the NIR, enhancing quantum yields when combined with quantum dots. Engineered heterojunctions and carbon-based 2D interfaces improve charge separation and suppress recombination. Thermodynamic parameters such as low overpotentials (&lt;0.3 V) and high absorption coefficients (&gt;10 5 cm −1 ) correlate with high catalytic efficiency. Time-dependent simulations and density functional theory (DFT) offer insights into structure–property relationships. Additionally, machine learning models expedite discovery by navigating complex compositional and structural spaces. While integrating theoretical, experimental, and AI-driven approaches, this review presents a framework for the rational design of scalable photocatalysts that meet future energy demands. Photocatalysis ; Hydrogen production ; Binary number ; Production (economics) ; Quantum ; Hydrogen ; Environmental science ; Materials science ; Chemistry ; Physics ; Economics ; Catalysis ; Mathematics ; Quantum mechanics ; Organic chemistry ; Arithmetic Machine Learning in Materials Science
Authors: Rahul Kumar Sharma | Harpriya Minhas | Biswarup Pathak
Source: ACS Applied Materials & Interfaces (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of Alloy Nanoclusters: Steering Morphology-Based Activity and Selectivity Relationships in Bifunctional Electrocatalysts Nanoclusters designed with atomic precision are poised to transform next-generation electrode materials for energy devices due to their exceptional performance. However, traditional computational studies often focus solely on individual nanoclusters, neglecting the impact of structurally diverse, low-energy isomers that coexist in a sample. Herein, we present a data-driven approach to screen late-transition metal-based core-shell nanoclusters for bifunctional electrocatalysis. Utilizing geometric and electronic properties, we establish morphology-based relationships for activity and selectivity, emphasizing the critical role of structural diversity in fuel cell applications. We identify the unique single-cluster catalyst identity of M<sub>55</sub> nanoclusters, where intermediate adsorption is primarily governed by the constituent metals' electronic and elemental characteristics. Our findings identified the Au<sub>48</sub>W<sub>7</sub> nanocluster as the most efficient electrocatalyst, exhibiting the lowest bifunctional overpotential of 0.76 V, with η<sub>OER</sub> = 0.33 V and η<sub>ORR</sub> = 0.43 V, highlighting its outstanding catalytic performance at the nano regime. Guided by the Sabatier principle, we highlight the limitations of conventional numerical methods and reshape the activity volcano, transitioning from RuO<sub>2</sub> and Pt to Au/Ag-based nanoclusters. Furthermore, the trained ML model enables the screening of electrocatalysts for two- and four-electron pathways, steering selectivity between H<sub>2</sub>O<sub>2</sub> and H<sub>2</sub>O formation. This study provides intuitive guidelines for designing efficient bifunctional electrocatalysts, redefining activity volcanoes, and modulates selectivity in nanocluster alloys. Nanoclusters ; Bifunctional ; Electrocatalyst ; Overpotential ; Materials science ; Selectivity ; Nanotechnology ; Cluster (spacecraft) ; Catalysis ; Chemical physics ; Electrode ; Computer science ; Chemistry ; Physical chemistry ; Electrochemistry ; Organic chemistry Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Li-Hui Mou | Gui-Duo Jiang | Chao Wang | Xin Cheng | Jia Yu | Ziyu Li | Jun Jiang
Source: ACS Catalysis (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Unveiling Universal Reactivity Descriptors of Metal Clusters toward Dinitrogen Activation: A Machine Learning Protocol with Three-Level Feature Extraction The activation of dinitrogen (N2) by metal clusters is a fundamental challenge in chemistry and has been extensively studied. However, previous studies have primarily focused on case-by-case interpretations of experimental reactivity, resulting in a lack of universal reactivity descriptors and predictive models that can quantitatively estimate reaction rates and elucidate structure–activity relationships. In this study, we develop predictive, interpretable, and transferable machine learning models for metal cluster reactivity toward N2, employing a systematic and hierarchical feature extraction strategy that generates electronic and intrinsic features from three structural levels. Crucial features influencing cluster reactivity were identified, and the mechanisms by which these features govern reactivity were elucidated. To enhance model robustness and transferability, pairwise learning (data augmentation) and feature interaction (feature augmentation) strategies were employed, leading to a deep neural network model that correlates feature differences with reaction rate differences. Trained on 159 homonuclear metal clusters, the model demonstrates satisfactory transferability to 57 heteronuclear metal clusters, enabling reaction rate predictions based on their feature differences. This work presents an expert-guided machine learning protocol for developing generalizable models to predict and understand metal cluster reactivity. Reactivity (psychology) ; Protocol (science) ; Catalysis ; Feature (linguistics) ; Chemistry ; Metal ; Computer science ; Combinatorial chemistry ; Organic chemistry ; Medicine Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; History and advancements in chemistry
Authors: Raouf Hassan | Mohammad Reza Kazemi
Source: Scientific Reports (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Data-driven frameworks to robustly predict solubility parameter of diverse polymers This study intends to effectively forecast solubility parameter of diverse polymers by creating machine learning models that can grasp the complex relationships between essential input factors like molecular weight, melting point, boiling point, liquid molar volume, radius of gyration, dielectric constant, dipole moment, refractive index, van der Waals area and reduced volume, and parachor, alongside the target variable, which is solubility coefficient of polymers. The goal is to create strong models that accurately capture these intricate relationships to facilitate accurate forecasts of the solubility parameter for polymers. Multiple machine learning algorithms, ranging from basic methods like Linear Regression to advanced techniques such as Artificial Neural Networks (ANNs), Ridge Regression, Lasso Regression, Support Vector Machines (SVMs), Linear Regression, Random Forests (RFs), Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Elastic Net, Decision Trees, Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Networks (CNNs), and Extreme Gradient Boosting (XGBoost) were utilized. These methods were utilized to create data-driven models that adeptly seize the intricate connections between input characteristics and output variable, facilitating precise predictions of the solubility parameter for polymers. The efficacy of the developed models was rigorously evaluated using statistical metrics such as R², RMSE, and MRD%, along with visual tools including cross-plots, deviation plots, and SHAP analysis to enhance interpretability and predictive reliability. To guarantee the dataset's reliability, consisting of 1,799 datapoints on the solubility parameter of polymers, the Monte Carlo outlier detection algorithm was utilized. This stage verified the dataset's accuracy and appropriateness for model training and evaluation. Results indicated that the models CatBoost, ANN, and CNN surpassed other techniques, attaining superior accuracy shown by the highest R-squared values and the lowest error rates. Sensitivity analysis showed that every input feature impacted the target variable, while SHAP analysis determined that dielectric constant was the most significant factor influencing the solubility parameter of polymers. These results highlight the efficiency of the utilized machine learning methods and emphasize the vital importance of these input parameters in establishing the solubility parameter of polymers. This method not only verifies that the models can make accurate predictions but also provides valuable insights into the impact of input features on solubility parameters of polymers, enhancing algorithm interpretability and scientific understanding. Solubility ; Polymer ; Computer science ; Data science ; Data mining ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Yang Xiao | Yuqi Liu | Zihan Tan | Bohan Zhang | Ke Xu | Zheyong Fan | Shunda Chen | Shiyun Xiong | Haikuan Dong
Source: Physical Review Materials (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing thermoelectric performance remains a significant challenge due to the complex interplay between electronic and thermal transport properties. In this work, we systematically optimize $ZT$ in graphene antidot lattices (GALs), nanostructured graphene sheets with periodic nanopores characterized by two geometric parameters: the hexagonal unit cell side length $L$ and the antidot radius $R$. The lattice thermal conductivity is determined through machine-learned potential-driven molecular dynamics (MD) simulations, while electronic transport properties are computed using linear-scaling quantum transport in combination with MD trajectories based on a bond-length-dependent tight-binding model. This method is able to account for electron-phonon scattering, allowing access to diffusive transport in large-scale systems, overcoming limitations of previous methods based on a nonequilibrium Green's function formalism. Our results show that the introduction of the antidots effectively decouples lattice and electronic transport and leads to a favorable and significant violation of the Wiedemann-Franz law. We find that optimal $ZT$ values occur in GALs with intermediate $L$ and $R$, closely correlated with peak power factor values. Notably, thermoelectric performance peaks near room temperature, with maximal $ZT$ values approaching 2, highlighting GALs as promising candidates for high-performance thermoelectric energy conversion. Materials science ; Graphene ; Thermoelectric effect ; Molecular dynamics ; Quantum ; Thermoelectric materials ; Nanotechnology ; Dynamics (music) ; Condensed matter physics ; Thermal conductivity ; Quantum mechanics ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Mingliang Wu | Jinxin Sun | Yu Cui | Linfeng Fan | Kunquan Hong | Wei Liu | Qiang Li | Zhiyang Lyu | Jinlan Wang
Source: Advanced Functional Materials (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Active Learning‐Driven Discovery of Donor‐Acceptor Covalent Triazine Frameworks for High‐Performance Photocatalysts Abstract Donor‐acceptor (D‐A) structure enables precise tuning of the electronic and optical properties of materials, enabling widely applicable in organic semiconductors and photocatalysts. However, the vast diversity of donor and acceptor units and their combinations pose considerable challenges to experimental development. Here, this study presents a screening strategy that integrates an active learning (AL)‐based multi‐model framework with experimental synthesis validation to discover high‐performance D‐A covalent triazine frameworks (CTFs) photocatalysts. This framework combines an AL model, trained on experimental data of reported D‐A‐CTFs, with a graph neural networks model that establishes the relationship between molecular structure and electronic properties. Meanwhile, expert chemical knowledge is incorporated into this multi‐model framework to improve the synthesizability and stability, resulting in 113 identified candidates from a database of 21807 structures. Experimental validation confirms that 9 out of 10 newly synthesized D‐A‐CTFs exhibit the predicted photocatalytic performances. Notably, CTF‐[1,1′‐Biphenyl]‐4,4′‐dicarbaldehyde achieved a record hydrogen evolution rate of 33.29 mmol g −1 h −1 for CTF‐based bulk photocatalysts. Further feature engineering analysis reveals that carbon and nitrogen charges critically determine the photocatalytic performance, offering an optimization strategy for D‐A‐CTFs design. This study paves a promising way to accelerate the discovery of effective D‐A structured materials. Materials science ; Triazine ; Covalent bond ; Acceptor ; Nanotechnology ; Photochemistry ; Polymer chemistry ; Organic chemistry ; Chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Domain Adaptation and Few-Shot Learning
Authors: Tian Zheng | Chong Wang | Bai–Xin Dong | Hong‐Yu Yang | Le Zhan | Dan Luo | Feng Qiu | Qi–Chuan Jiang
Source: Materials (review, 2025-09-01)
Field: Materials Science — Biomaterials

Rare Earth Elements in Heat-Resistant Magnesium Alloys: Mechanisms, Performance, and Design Strategies This study investigates the influence of RE elements on the room- and high-temperature properties of magnesium alloys. The effects of RE type, addition level, and multi-element alloying strategies were systematically analyzed to clarify the underlying strengthening mechanisms and processing pathways for optimizing Mg-RE alloys. RE elements enhance the mechanical and thermal properties of Mg alloys through crystal structure modification, formation of thermally stable dispersed phases, precipitation strengthening, and solid-solution strengthening. Compared with conventional alloying elements, RE additions offer distinct advantages in strengthening efficiency and overall performance. To fully exploit these benefits, new research paradigms that integrate machine learning and other advanced techniques are required, enabling the intelligent design of multicomponent alloy systems tailored to specific application requirements. Magnesium ; Rare earth ; Materials science ; Metallurgy ; Earth (classical element) ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials