jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenting Yang | Shuang Li | Siqi Yu | Alex G. C. de Sá | Tanmayee Sai Sivani Ita | Tian Liang | Helen Forgham | Ruirui Qiao | Jiulong Li | Patrick S. Stayton | David B. Ascher | Huan Meng | Andrew K. Whittaker | Changkui Fu
Source: Biomacromolecules (article, 2025-10-07)
Field: Chemistry — Organic Chemistry

Combinatorial Discovery of RAFT Cationic Polymers for mRNA Delivery: Structure–Function Insights from High-Throughput Screening and Machine Learning Cationic polymers are considered promising delivery systems for mRNA, offering potential advantages over lipid nanoparticles. Here a library of tertiary amine-containing, methacrylate-based cationic polymers with diverse molecular characteristics and properties were prepared by combinatorial RAFT polymerization for mRNA delivery. The ability of the synthesized cationic polymers to complex with mRNA was thoroughly investigated. The biological responses, including cellular uptake, cytotoxicity, and mRNA transfection efficiency, of the formed mRNA-polymer polyplexes were systemically investigated. Through high-throughput screening assays, we identified several lead polymers that showed superior effectiveness in delivering mRNA, with performance significantly outperforming other synthesized cationic polymers as well as polyethylenimine (PEI) and Lipofectamine, two benchmark gene delivery materials. To unravel the complex structure-function relationships between the chemical and physical properties of cationic polymers/mRNA polyplexes and their biological responses, machine learning analyses were conducted. These in silico studies identified several key attributes that are predictive of cellular uptake, cytotoxicity, and mRNA transfection efficiency, providing valuable insights for the future design of more potent cationic polymers for mRNA delivery. Cationic polymerization ; Polymer ; Polyethylenimine ; Transfection ; Raft ; In silico ; Messenger RNA Synthesis of Tetrazole Derivatives
Authors: Ramadhan Muhammad Naufal | Nawwal Hikmah | Dessy Ariyanti
Source: Desalination and Water Treatment (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Hybrid optimization of thermally-enhanced Zn-Fe LDH catalysts for fenton-like reactions: Integrating design of experiments with machine learning models for optimisation This study presents a novel hybrid modeling framework that combines Response Surface Methodology (RSM) with machine learning (ML) algorithms– Support Vector Regression (SVR) and Gradient Boosting Regression (GBR)– to contribute to the predictive modeling and optimization of thermally-activated ZnFe-LDH based Fenton catalysis. While RSM effectively captured linear, interaction, and quadratic effects within an experimental design space, the predictive performance was restricted by the fixed polynomial form of the RSM model, especially in boundary regions. To address this, SVR and GBR were applied to uncover complex, nonlinear dependencies within the same dataset. Among the models, GBR achieved the highest predictive accuracy (R² = 0.998, MAPE = 0.9%, RMSE = 0.964), outperforming RSM by over 97% in prediction error reduction. The improvement of prediction fidelity by introducing the use of ML models into the RSM framework resulted in precise optimization across complex, multi-dimensional surfaces. This hybrid modeling framework presents a robust, data-driven platform for developing environmental catalysts, specifically in systems that may exhibit high interaction complexity, nonlinear response behavior, or curvature boundaries. Further, it provides a scalable avenue toward multi-objective and exploratory process optimizations that could extend well beyond the confines of traditional experimental design. • ZnFe-LDH catalyst achieved 87.02% decolorization under thermal Fenton-like conditions at 85 °C. • Hybrid Design of Experiment-Machine Learning Model (GBR, SVR) has been outperformed RSM in modeling nonlinear interactions within the experimental design space. • GBR model showed the best predictive fit (R² = 0.998, MAPE = 0.9%, RMSE = 0.964) within the constrained design space. • ZnFe-LDH exhibited a mesoporous structure with a BET surface area of 96.45 m²/g. • XRD, FTIR, SEM-EDS, and TGA/DSC confirmed crystallinity and thermal stability up to 600 °C. Catalysis ; Chemical engineering ; Fenton reaction ; Process engineering ; Computer science ; Materials science ; Chemistry ; Biochemical engineering ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Oliver Schilter | Carlo Baldassari | Teodoro Laino | Philippe Schwaller
Source: ChemRxiv (preprint, 2025-05-20)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning Models for Solvent Prediction in Organic Reactions: Bridging the Gap between Theory and Practical Efficacy The selection of an appropriate solvent is crucial to achieve environmentally friendly, selective, and high-yielding chemical reactions. While artificial intelligence tools have shown promise in predicting reaction conditions, including solvent selection, they often overlook sustainability criteria in their recommendations and lack experimental validation. In this work, we present data-driven machine learning models for solvent prediction for organic reactions. Building on these models' ability to capture chemical similarity in their learned representations, we developed a green solvent replacement methodology that adapts to evolving sustainability standards without requiring model retraining. Our models achieve a Top-3 accuracy of 85.1% in predicting missing solvents for patent-derived reactions, demonstrating excellent performance even for underrepresented solvent classes. Through uncertainty analysis, we found that model misclassifications correlated with reactions that could be carried out in multiple solvents. We validated our approach experimentally, achieving a 88% success rate for general solvent prediction and a 80% success rate when focusing on green solvent alternatives. Our work addresses a key challenge in organic synthesis and provides a practical methodology for predicting both conventional and sustainable solvents for organic synthesis. Bridging (networking) ; Computer science ; Organic solvent ; Solvent ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Chemical engineering Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Natalie E. Hooven | Arthur Y. Lin | Rose K. Cersonsky | Cersonsky, Rose K.
Source: ArXiv.org (preprint, 2025-09-29)
Field: Materials Science — Materials Chemistry

Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems Machine-Learning Interatomic Potentials (MLIPs) have surged in popularity due to their promise of expanding the spatiotemporal scales possible for simulating molecules with high fidelity. The accuracy of any MLIP is dependent on the data used for its training; thus, for large molecules, like polymers, where accurate training data is prohibitively difficult to obtain, it becomes necessary to pursue non-traditional methods to construct MLIPs, many of which are based on constructing MLIPs using smaller, analogous chemical systems. However, we have yet to understand the limits to which smaller molecules can be used as a proxy for extrapolating macromolecular energetics. Here, we provide a ``control study'' for such experiments, exploring the ability of MLIP approaches to extrapolate between n=1-8 n-polyalkanes at identical conditions. Through Principal Covariates Classification, we quantitatively demonstrate how convergence in chemical environments between training and testing datasets coincides with an MLIP's transferability. Additionally, we show how careful attention to the construction of an MLIP's neighbor list can promote greater transferability when considering various levels of the energetic hierarchy. Our results establish a roadmap for how one can create transferable MLIPs for macromolecular systems without the prohibitive cost of constructing system-specific training data. Extrapolation ; Transferability ; Construct (python library) ; Convergence (economics) ; Popularity ; Macromolecule ; Computation ; Interatomic potential Machine Learning in Materials Science
Authors: Debashis Swain | Sarah Maier | Surya Sekhar Manna | Krishnan Raghavachari
Source: Pure and Applied Chemistry (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Combining molecular fragmentation and machine learning for accurate prediction of adiabatic ionization potentials Abstract Accurate wavefunction methods like CCSD(T) can predict chemical and thermodynamic properties of small molecules with near-experimental precision. However, their steep computational cost limits their use for large systems or extensive datasets. In contrast, DFT is faster and more practical for large molecules but often fails to accurately capture electronic changes, especially upon geometry relaxation. To address this, we have developed a graph neural network (GNN)-based Delta-Machine Learning (ΔML) model using Connectivity-Based Hierarchy (CBH-2) molecular fragments as descriptors to predict adiabatic ionization potentials (IPs). Our approach shows systematic error cancellation, guided by electron population difference maps to identify ionization sites within the molecule to construct CBH-2 fragments. We have considered both structural and electronic features to improve our ΔML model. We have observed that upon incorporation of electronic features, the graph-based ΔML model achieved a coupled cluster level of accuracy with an MAE of 0.02 eV for IP prediction. Furthermore, we compared adiabatic and vertical IP predictions across different methods. While raw DFT errors are strongly dependent on the functional used, our ΔML model is robust and much less functional-sensitive. Adiabatic process ; Ionization ; Wave function ; Population ; Coupled cluster ; Electronic structure ; Molecule ; Ionization energy Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Amr Ismail | Santosh Biswas
Source: Journal of Nano Molecular Intelligence and Virtual Health Systems (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Hybrid Molecular Nano Frameworks for Disease Modeling Using Neuro Symbolic Machine Learning Disease modelling has advanced significantly when computational methods and molecular data are used. For prediction accuracy and interpretability, this initiative proposes to create a hybrid molecular nano framework for disease modelling employing neuro-symbolic machine learning, with Graph Attention Networks (GAT) as the central method. The GAT depicts molecules as graphs, and the technique interactively assigns focus to atom-bond interactions to extract structural and relational properties. These traits' anticipated disease-relevant relationships and biological objectives are paired with symbolic reasoning, which uses molecular similarity metrics (such as Tanimoto coefficients) and 3D structure data. One of the main discoveries is the successful use of GAT to capture necessary chemical substructures and achieve better prediction performance than conventional models. The hybrid framework showed excellent interpretability, effectively connecting biological targets like CYP2D6 and HERG to molecular patterns and offering insights into disease processes. Finally, combining GAT with symbolic reasoning reveals a viable strategy for molecular-based illness modelling by striking a balance between interpretability and prediction accuracy. Computer science ; Nano- ; Artificial intelligence ; Machine learning ; Cognitive science ; Psychology ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Khalid Ferji
Source: ChemRxiv (preprint, 2025-04-17)
Field: Materials Science — Surfaces, Coatings and Films

DetectNano: Deep Learning Detection in TEM Images for High-Throughput Polymer Nanostructure Characterization The rapid and unbiased characterization of self-assembled polymeric vesicles in transmission electron microscopy (TEM) images remains a challenge in polymer science. Here, we present a deep learning-powered detection framework based on YOLOv8, enhanced with Weighted Box Fusion, to automate the identification and size estimation of polymer nanostructures. By incorporating multiple morphologies in the training dataset, we improve model generalization and achieve robust detection across unseen TEM images. Our results demonstrate that the model provides accurate vesicle detection in under 2 seconds—an efficiency unattainable by traditional image analysis software. The proposed framework enables reproducible and scalable nano-objects characterization, paving the way for a general AI-driven automation in polymer self-assembly research. Nanostructure ; Characterization (materials science) ; Throughput ; Nanotechnology ; Materials science ; Deep learning ; Polymer ; Artificial intelligence ; Computer science ; Composite material ; Telecommunications Electron and X-Ray Spectroscopy Techniques
Authors: Qi Zeng | Yiqun Zhang | Yuting Peng | Qun Zeng | Guangxu Sun | Minshan Guo | Ting Cai
Source: Chemical Engineering Journal (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for solvent prediction and mechanistic insights in multi-component crystal screening Interpretability ; Crystallization ; Solubility ; Crystal structure prediction ; Crystal (programming language) ; Crystal engineering ; Intermolecular force ; Synthon Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Sanocki, Michal | Zavadlav, Julija
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. Although we demonstrate our methodology on metal-organic frameworks, it is broadly applicable to other materials, offering insights into the design of more robust and transferable MLIPs. Transferability ; Generalization ; Chemical space ; Space (punctuation) ; Training set ; Probably approximately correct learning Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Artem Mishchenko | Anupam Bhattacharya | Xiangwen Wang | Henry Kelbrick Pentz | Yihao Wei | Qian Yang
Source: Digital Discovery (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Deep learning methods for 2D material electronic properties This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL approaches - such as physics-aware models, generative AI, and inverse design - have significantly improved predictions of critical electronic properties, including band structures, density of states, and quantum transport phenomena. Through selected case studies, we illustrate how DL methods accelerate discoveries in emergent quantum phenomena, topology, superconductivity, and autonomous materials exploration. Finally, we outline promising future directions, stressing the need for robust data standardization and advocating for integrated frameworks that combine theoretical modeling, DL methods, and experimental validations. Deep learning ; Standardization ; Generative grammar ; Quantum ; Electronic materials ; Electronic structure ; Electronic systems Machine Learning in Materials Science ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Jie Zhu | Yajing Sun
Source: FlexTech (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Predicting Lattice Thermal Conductivity in 2D Materials: Integrating Experiments, Theory, and Machine Learning ABSTRACT Lattice thermal conductivity is a critical parameter for assessing the thermal transport properties of materials. When confined to the monolayer limit, two‐dimensional materials display unique thermal characteristics distinct from their three‐dimensional counterparts. This article first provides a concise summary of three widely applied experimental methodologies—Raman thermometry, suspended microbridge techniques, and time‐domain thermoreflectance—and their utility in validating theoretical predictions. It subsequently delves into recent advancements in theoretical modeling, encompassing both equilibrium and nonequilibrium molecular dynamics studies; first‐principles calculations grounded in the phonon Boltzmann transport equation that account for higher‐order scattering phenomena such as four‐phonon processes and phonon–electron interactions; emerging methods based on normal mode analysis for detailed phonon contribution decomposition; and novel approaches employing the Wigner transport equation to unify the description of phonon coherence and wave‐like heat transport phenomena beyond conventional theoretical frameworks. In addition, the advent of machine learning has expanded the scope of direct thermal conductivity prediction and the development of high‐precision interatomic potentials, paving the way for high‐throughput screening and extensive simulations. This review contrasts the advantages and drawbacks of these methodologies, identifies key challenges facing the field, and sketches future directions for 2D thermal transport research, emphasizing the integration of multiscale modeling, data‐driven innovation, and the synergy between experiments and theoretical insights. Thermal conductivity ; Lattice (music) ; Materials science ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Condensed matter physics ; Physics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Nawaf Alampara | Mara Schilling-Wilhelmi | Kevin Maik Jablonka
Source: ArXiv.org (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Lessons from the trenches on evaluating machine-learning systems in materials science Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning systems increasingly transform scientific fields, the question of how to effectively evaluate these systems becomes crucial for ensuring reliable progress. In this review, we examine the current state and future directions of evaluation frameworks for machine learning in science. We organize the review around a broadly applicable framework for evaluating machine learning systems through the lens of statistical measurement theory, using materials science as our primary context for examples and case studies. We identify key challenges common across machine learning evaluation such as construct validity, data quality issues, metric design limitations, and benchmark maintenance problems that can lead to phantom progress when evaluation frameworks fail to capture real-world performance needs. By examining both traditional benchmarks and emerging evaluation approaches, we demonstrate how evaluation choices fundamentally shape not only our measurements but also research priorities and scientific progress. These findings reveal the critical need for transparency in evaluation design and reporting, leading us to propose evaluation cards as a structured approach to documenting measurement choices and limitations. Our work highlights the importance of developing a more diverse toolbox of evaluation techniques for machine learning in materials science, while offering insights that can inform evaluation practices in other scientific domains where similar challenges exist. Toolbox ; Transparency (behavior) ; Context (archaeology) ; Metric (unit) ; Quality (philosophy) ; Benchmark (surveying) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Adversarial Robustness in Machine Learning
Authors: Xiaoke Zhou | Sisi He | Min Xiao | Jing He | Yuan Wang | Yuanqin Zhu | Haixiang He
Source: Molecular Diversity (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Machine learning-based activity prediction of phenoxy-imine catalysts and its structure–activity relationship study Imine ; Catalysis ; Chemistry ; Combinatorial chemistry ; Computer science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Artem Mishchenko | Anupam Bhattacharya | Xiangwen Wang | Henry Kelbrick Pentz | Yihao Wei | Qian Yang
Source: ChemRxiv (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Deep Learning Methods for 2D Material Electronic Structures This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL approaches -- such as physics-aware models, generative AI, and inverse design -- have significantly improved predictions of critical electronic properties, including band structures, density of states, and quantum transport phenomena. Through selected case studies, we illustrate how DL methods accelerate discoveries in emergent quantum phenomena, topology, superconductivity, and autonomous materials exploration. Finally, we outline promising future directions, stressing the need for robust data standardization and advocating for integrated frameworks that combine theoretical modeling, DL methods, and experimental validations. Deep learning ; Artificial intelligence ; Computer science ; Materials science Machine Learning in Materials Science ; Neural Networks and Applications ; Semiconductor materials and devices