jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Narendra Sutar | Shankar G. Alegaon | Gaitonde Apeksha Govind -
Source: International Journal on Science and Technology (review, 2025-05-21)
Field: Chemistry — Organic Chemistry

A detailed review of recent developments in the synthesis of pyrimidine derivatives and their anticancer potential. Pyrimidine derivatives are important in anticancer drug discovery because they mimic nucleic acids and can create hydrogen bonds and π-π interactions. This review explores various synthetic approaches, including traditional Biginelli and β-dicarbonyl condensations, as well as multicomponent, green, microwave-assisted, and catalyst-driven methods that improve efficiency and sustainability. We examine the structure-activity connections behind antimetabolite, kinase inhibitor, and DNA intercalator actions, including clinically authorized medicines such as 5-fluorouracil, capecitabine, gemcitabine, and pemetrexed. Recent breakthroughs in hybrid scaffolds, epigenetic modulators, and AI-driven design are reviewed, as well as the difficulties of resistance, toxicity, and scalability. Future directions include sustainable synthesis, machine learning optimization, and targeted degradation techniques. Pyrimidine ; Chemistry ; Combinatorial chemistry ; Computational biology ; Computer science ; Nanotechnology ; Stereochemistry ; Biology ; Materials science Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Synthesis and Reactions of Organic Compounds
Authors: Soumya Mondal (742902) | Arko Mohari (21239784) | Harshit Varma (18706254) | Ayan Datta (1268127)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Pseudo Jahn–TellerEffects Control the Trade-offbetween a Flat and Buckled Structure for Two-Dimensional Materials Two-dimensional atomically thin materials have significant applications in catalysis and energy-storage. Structural analyses for 4072 available 2D-systems show that those with heavy atoms prefer buckled structures, while the lighter ones have more planar forms. The origin is traced to the pseudo Jahn–Teller (PJT) effect, where distortions are driven by vibronic interactions between electronic states. Cases involving both Brillouin zone-center (Γ = 0) and non-zone-center phonon condensation reveal that PJT effect-driven symmetry breaking becomes favorable for heavier 2D systems. A heuristic approach involving machine-learning-assisted data analyses also indicates that boron-, carbon-, and nitrogen-containing atomic monolayers are less susceptible toward displacive type-distortions. Chemical control of the PJT effect through atomic constituents can guide the formation of perfectly planar 2D systems. Planar ; Brillouin zone ; Phonon ; Monolayer ; Condensation ; Symmetry (geometry) ; Catalysis ; Electronic structure 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Ibarra-Hoyos, Diego | Connors, Peter | Jang, Ho | Grain, Nathan | Klich, Israel | Chern, Gia-Wei | Liaw, Peter K. | Scully, John R. | Poon, Joseph
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

From Quantum Annealing to Alloy Discovery: Towards Accelerated Design of High-Entropy Alloys Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often get trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, that overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to handle large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2 % yield strength of 568 MPa, greater than 40 % compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery. Quantum annealing ; Maxima and minima ; Quantum ; Annealing (glass) ; Simulated annealing ; Alloy ; Qubit Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Geraldine Sánchez-Dueñez (22450370) | Wladimiro Diaz-Villanueva (22450373) | Jorge Escorihuela (1831741) | Laura Francés-Soriano (1782046) | Julia Pérez-Prieto (1312983)
Source: Figshare (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Predicting theOptical Properties of Gold NanoclustersUsing Machine Learning Approach The synthesis of gold nanoclusters (AuNC) is strongly influenced by various reaction conditions, and their optical properties are determined by factors such as the nature of the ligand and the measuring solvent, among others. To improve the efficiency of the synthesis of metallic gold nanoclusters with the desired functionality, the application of machine learning techniques is a smart choice. In this study, a model based on the GXBoost algorithm is proposed to predict the maximum emission wavelength of the AuNC emission from a database that includes more than 200 scientific articles. The validation of the model was carried out through the comparison of prediction versus experimental data (not included in the model) and the training and validation data. The model showed a percentage error of 1.7, 1.6, and 4.9%, respectively, indicating a reasonable return. Additionally, an independent regression was performed when the ligand was a thiolated compound different from GSH, obtaining a training and test error percentage of 0.01 and 3%, respectively. In addition, critical variables affecting the optical properties of nanoclusters were explored, and techniques such as One-Hot Encoding were used to prepare the data. Finally, this work not only underscores the relevance of AuNCs in modern science highlighted but also demonstrates the potential of machine learning as a predicting tool and design of materials in nanoscience, contributing to the optimization of their properties for future applications. Nanoclusters ; Relevance (law) ; Experimental data ; Training set ; Test data ; Gold standard (test) Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Boyang Liang (22352898) | Wenjia Huo (22352901) | Xiang Wu (1341072) | Rongrong Zheng (6985322)
Source: Figshare (article, 2025-10-01)
Field: Materials Science — Polymers and Plastics

Machine Learning-EnabledParallel Prediction and Co-Designof Polyimides for Tailored Glass Transition, Dielectric, and BandgapProperties Polyimide (PI) is widely used in aerospace, nuclear industries, microelectronics, and flexible devices due to its exceptional thermal stability, electrical insulation, mechanical strength, and low dielectric loss. However, developing high-performance PIs traditionally requires decades of experimental effort. To accelerate discovery, we integrated machine learning (ML) with molecular dynamics (MD) and quantum mechanics (QM) simulations, demonstrating the efficacy of data-driven approaches for novel PI development. We collected 1499 PI structures from relevant literature, with experimentally measured glass transition temperature (<i>T</i><sub>g</sub>), dielectric constant (DC), and bandgap width (EG). Subsequently, substructure information was extracted from their SMILES encoding as feature inputs for the model, and 90 ML models were established to describe the various properties of PI. The resulting machine learning model had good predictive performance in identifying key chemical substructures that affect PI performance. SHAP analysis identified critical substructures governing performance, guiding the design of PI with superior dielectric properties, thermal stability, or insulation capabilities compared to those of existing benchmarks. MD and QM simulations validated ML predictions showing excellent agreement, confirming the designed PI’s performance and the models’ extrapolation reliability. This work establishes an ML-driven approach integrating MD/QM validation to expedite the exploration of innovative polymers, offering a theoretical foundation for experimental synthesis. Extrapolation ; Dielectric ; Substructure ; Polyimide ; Glass transition ; Band gap ; Thermal ; Work (physics) Synthesis and properties of polymers ; Dielectric materials and actuators ; Epoxy Resin Curing Processes
Authors: Kalkat, Harvir S
Source: DigitalCommons - CalPoly (California State Polytechnic University) (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Comparing Machine Learning Paired Optimization Strategies for Single-Component Waterborne Direct-To-Metal Coatings As R&D attempts to fine tune properties before production, the redesign and testing process can become highly iterative. The repetitive nature of formulating can be difficult to escape when relying on chemical intuition alone. To provide some guidance in this process, machine learning (ML) can leverage data from previous trials1 to design suggestions for subsequent trials. Although ML capabilities have vastly expanded in the last few years, integration of ML into chemistry R&D and education has been slow. Unraveling the mystique of ML can help change what has been a slow embrace in the coatings industry. It is important to demonstrate the utility of ML in industrial coatings R&D, while also developing an intuitive curriculum to prepare the next generation of chemists. By demonstrating effectiveness and ease-of-use, this paper hopes to establish the need for ML in coatings R&D and undergraduate laboratory curricula. In this paper, different ML strategies are compared for their ability to optimize and reformulate single-component waterborne direct-to-metal (DTM) coatings. This coating system is sensitive to ingredient selection and ingredient amounts, making invalid coating formulations easy to distinguish from valid candidates in testing. Three different ML strategies are compared, built on two neural network designs. A commercial electronic lab notebook software with native ML feature integration2, is compared to two in-house models built on open-source libraries in Python. Starting with a single DTM formulation, the design space was populated with formulations by varying ingredient ratios while maintaining the ingredient list and order of addition. The ML models have a consistent problem to solve, and improvements in the model performance can be assessed over iteration of model development and material formulation. All three ML strategies struggled to predict test scores accurately, however they did manage to generate formulations that narrowed the design space towards target property scores. The neural networks struggled with making accurate predictions on a limited dataset. Combining a better model with a strong optimization strategy could still streamline the coatings R&D process at a larger scale. Ingredient ; Leverage (statistics) ; Artificial neural network ; Intuition ; Active ingredient ; Software Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Electrocatalysts for Energy Conversion
Authors: Yuxi Zhu | Zhenqian Chen
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Potential relationship of spin magnetic moment with thermal conductivity and catalytic performance in Fe–Co bimetallic catalysts: a machine-learning interatomic potential and density functional theory study The thermal transport properties of catalysts are important for the stable operation of proton exchange membrane fuel cells. In this study, density functional theory and moment tensor potentials are used to solve the Boltzmann transport equation and investigate the thermal transport properties of various designed configurations of Fe-Co bimetallic catalysts, which are the most promising non-noble metal catalysts for the oxygen reduction reaction (ORR). It is found that the velocity of the phonon group in the <i>y</i> direction is always higher than that in the <i>x</i> direction, which leads to the anisotropic thermal conductivity of these catalysts. The uniformity of the thermal conductivity exhibited by bimetallic catalysts is better than that of single-metal atom catalysts. In the designed configurations, G-FeCoN<sub>6</sub>-3 is found to show a high thermal conductivity value (55.57-376.98 W m<sup>-1</sup> K<sup>-1</sup>), which is even higher than that of G-FeN<sub>4</sub> (35.66-132.75 W m<sup>-1</sup> K<sup>-1</sup>). The analysis of phonon transport properties shows that the difference in thermal conductivity is mainly due to the difference in the phonon lifetime. The results indicate that thermal conductivity is governed by low-frequency phonons and the size effects are intensified in bimetallic catalysts. It is revealed from results on electronic structures that the low thermal conductivity may be related to the existence of band gaps in valence bands. Furthermore, these structures exhibit superior electrical conductivities with values of 0.98-2.2 × 10<sup>4</sup> Ω<sup>-1</sup> m<sup>-1</sup>. Additionally, through the results of the maximum electron thermal conductivity of these structures, it is revealed that the thermal conductivity of these catalysts is mainly dominated by the lattice thermal conductivity. Finally, a potential relationship of spin magnetic moment with the thermal conductivity and catalytic performance is revealed. Bimetallic strip ; Thermal conductivity ; Phonon ; Density functional theory ; Materials science ; Magnetic moment ; Moment (physics) ; Catalysis ; Condensed matter physics ; Spin (aerodynamics) ; Conductivity ; Thermodynamics ; Computational chemistry ; Chemistry ; Physical chemistry ; Composite material ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Thorben Prein | Elton Pan | Janik Jehkul | Steffen Weinmann | Elsa Olivetti | Jennifer L. M. Rupp
Source: ArXiv.org (preprint, 2025-06-14)
Field: Materials Science — Materials Chemistry

Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstrate that language models, without task-specific fine-tuning, can recall synthesis conditions. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash and Llama 4 Maverick, achieve a Top-1 precursor-prediction accuracy of up to 53.8 % and a Top-5 performance of 66.1 % on a held-out set of 1,000 reactions. They also predict calcination and sintering temperatures with mean absolute errors below 126 °C, matching specialized regression methods. Ensembling these language models further enhances predictive accuracy and reduces inference cost per prediction by up to 70 %. We subsequently employ language models to generate 28,548 synthetic reaction recipes, which we combine with literature-mined examples to pretrain a transformer-based model, SyntMTE. After fine-tuning on the combined dataset, SyntMTE reduces mean-absolute error in sintering temperature prediction to 73 °C and in calcination temperature to 98 °C. This strategy improves models by up to 8.7 % compared with baselines trained exclusively on experimental data. Finally, in a case study on Li7La3Zr2O12 solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable, data-efficient inorganic synthesis planning. Workflow ; Set (abstract data type) ; Calcination ; Heuristic ; Inference ; Matching (statistics) ; Sintering ; Language model Machine Learning in Materials Science
Authors: Madhav R. Muthyala | Farshud Sorourifar | Tianhong Tan | Peng You | Joel A. Paulson
Source: ArXiv.org (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multi-objective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications. Scalability ; Bayesian optimization ; Benchmark (surveying) ; Modular design ; Bayesian probability ; Multi-objective optimization ; Generative model ; Ranking (information retrieval) ; Generative grammar Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Madhav R. Muthyala | Farshud Sorourifar | Tianhong Tan | You Peng | Joel A. Paulson
Source: Industrial & Engineering Chemistry Research (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design Designing molecules that must satisfy multiple, often conflicting, objectives is a central challenge in molecular discovery. The enormous size of the chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduce both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multiobjective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multipoint Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only a simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications. Scalability ; Bayesian optimization ; Benchmark (surveying) ; Multi-objective optimization ; Modular design ; Ranking (information retrieval) ; Bayesian probability ; Generative model ; Monte Carlo method Machine Learning in Materials Science ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Madhav R. Muthyala | Farshud Sorourifar | Tianhong Tan | Peng You | Joel A. Paulson
Source: arXiv (Cornell University) (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multi-objective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications. Scalability ; Bayesian optimization ; Benchmark (surveying) ; Modular design ; Bayesian probability ; Multi-objective optimization ; Generative model ; Ranking (information retrieval) ; Generative grammar Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research