jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Satish Prajapati | Saibal Ray | Prasanta Kumar
Source: ChemRxiv (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

A Thermodynamically Consistent Multiscale Framework for Additive Transport in Pharmaceutical Polymers: The Pharm-PATIK Theory The incorporation of functional additives into pharmaceutical polymers underpins modern drug delivery systems, yet the predictive modeling of additive mass transfer during processing remains a fundamental challenge. Existing frameworks rely on decoupled Fickian diffusion models that fail to capture the complex interplay between polymer rheology, crystallization dynamics, and mechano-chemical degradation-phenomena critically important for pharmaceutical applications where additive uniformity directly impacts drug product safety and efficacy. Here, we present the Pharm-PATIK (Pharmaceutical Polymer Additive Transport and Interaction Kinetics) framework: a thermodynamically consistent, multiscale computational theory for additive mass transfer in polymer matrices. The framework is built upon the GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) formalism and integrates: (i) a multicomponent Maxwell-Stefan diffusion model with shear-dependent Flory-Huggins interaction parameters; (ii) a phase-field description of polymer crystallization with additive exclusion dynamics; (iii) a finite-strain viscoelastic model capturing stress-induced additive migration via configurational temperature theory; and (iv) mechano-chemical degradation kinetics for predictive assessment of leachables formation. The governing equations are formulated as a system of coupled nonlinear partial differential equations with degenerate mobility, for which we establish mathematical well-posedness under physically relevant conditions. Homogenization theory bridges molecular-scale free-volume effects to equipmentscale transport phenomena. Validation against experimental benchmarks including forced Rayleigh scattering diffusivity measurements, X-ray fluorescence mapping, and twin-screw extrusion trials demonstrates predictive accuracy with mean absolute percentage error below 7%. The framework provides a rigorous foundation for digital design of pharmaceutical polymer processing operations, enabling predictive control of additive fate from extrusion through shelflife storage under quality-by-design (QbD) paradigms. Thermal diffusivity ; Homogenization (climate) ; Polymer ; Nonlinear system ; Viscoelasticity ; Extrusion ; Mass transfer ; Additive model Polymer crystallization and properties ; Membrane Separation and Gas Transport ; Rheology and Fluid Dynamics Studies
Authors: Alexander Arjun Lobo | Urvi Awasthi | Leonid Zhukov
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Property-Guided Molecular Generation and Optimization via Latent Flows Molecular discovery is increasingly framed as an inverse design problem: identifying molecular structures that satisfy desired property profiles under feasibility constraints. While recent generative models provide continuous latent representations of chemical space, targeted optimization within these representations often leads to degraded validity, loss of structural fidelity, or unstable behavior. We introduce MoltenFlow, a modular framework that combines property-organized latent representations with flow-matching generative priors and gradient-based guidance. This formulation supports both conditioned generation and local optimization within a single latent-space framework. We show that guided latent flows enable efficient multi-objective molecular optimization under fixed oracle budgets with controllable trade-offs, while a learned flow prior improves unconditional generation quality. Modular design ; Prior probability ; Generative grammar ; Property (philosophy) ; Generative model ; Optimization problem ; Flow (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jakes Udabe
Source: Artificial Intelligence Chemistry (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

A scientist's guide to AI-driven molecular discovery Artificial intelligence (AI) is increasingly steering the discovery of functional molecules and materials, but its progress with generative modeling is held back by the messy, mixed-up nature of the experimental data and a scarcity of high-quality ground truth. This review synthesizes recent advances in data curation, representation, and generative modeling for molecular and materials discovery, and proposes a practical four-stage workflow that integrates structured data capture, intelligent featurization, generative design, and closed-loop experimental validation. Core algorithmic families (supervised, semi-supervised, unsupervised, reinforcement learning) and specialized generative architectures (VAEs, GANs, diffusion models, graph-based models) are surveyed, and discuss how each maps to real-world discovery tasks. The enabling infrastructure (e.g.as electronic lab notebooks (ELNs), knowledge graphs, autonomous laboratories) is likewise analyzed and highlight best practices for reproducibility, uncertainty quantification, and ethical safeguards. Finally, a prioritized checklist was provided for researchers and laboratories to adopt AI-compatible infrastructure and describe open challenges (data standards, causal inference, accessibility) to guide future work. Generative grammar ; Workflow ; Core (optical fiber) ; Generative model ; Automation ; Knowledge extraction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Massimiliano Zamengo | Stephen Wu | Ryo Yoshida | Junko Morikawa
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physics-informed active learning for the design of thermally conductive composite microstructures with controlled anisotropy Thermal conductivity ; Solver ; Scalability ; Anisotropy ; Electrical conductor ; Active learning (machine learning) ; Autoencoder ; Thermal Thermal properties of materials ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Yuhao Liu | Yufan Zheng | Ying Lin | Yunxiao Zhang | Hao Hu | Bin Cao | Liming Wang
Source: Polymer Degradation and Stability (article, 2026-04-06)
Field: Materials Science — Polymers and Plastics

A unified perspective on diverse water uptake behavior in polymer composites: a water diffusion model accounting for interfacial and aggregation effects Polymer ; Diffusion ; Perspective (graphical) ; Agrégation ; Properties of water ; Absorption of water Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Membrane Separation and Gas Transport
Authors: Akta Singh | Nayana Mukherjee | Jagannath Mondal | Pushpita Ghosh
Source: arXiv (Cornell University) (preprint, 2026-01-22)
Field: Materials Science — Biomaterials

Emergence of spatiotemporal patterns in a fuel-driven coupled cooperative supramolecular system Chemically fueled supramolecular systems can exhibit complex, time-dependent behaviors reminiscent of living matter when maintained far from equilibrium by continuous energy or fuel consumption. Here, we introduce a minimal reaction-diffusion model that captures the essential dynamics of a cooperative supramolecular polymerization network driven by monomer activation and deactivation. We show that a balance between autocatalytic growth and inhibitory decay sustains a nonequilibrium steady state in the model that undergoes a Hopf bifurcation, giving rise to autonomous oscillations. When spatial transport is introduced through diffusion, the system displays rich spatiotemporal phenomena, such as traveling wavefronts and transient polygonal patterns. Our results demonstrate that the interplay between reaction kinetics and diffusion can spontaneously generate self-organized, life-like dynamics in synthetic supramolecular polymer systems. This theoretical framework not only bridges molecular self-assembly and active matter dynamics but also provides design principles for creating adaptive, oscillatory, and self-patterning materials powered by chemical fuels. Supramolecular chemistry ; Non-equilibrium thermodynamics ; Autocatalysis ; Supramolecular polymers ; Autocatalytic reaction ; Monomer ; Diffusion ; Bistability ; Energy landscape ; Transient (computer programming) Supramolecular Self-Assembly in Materials ; Micro and Nano Robotics ; Supramolecular Chemistry and Complexes
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

C2NP: A Benchmark for Learning Scale-Dependent Geometric Invariances in 3D Materials Generation Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remains largely untested. We introduce Crystal-to-Nanoparticle (C2NP), a systematic benchmark for evaluating generative models when moving between infinite crystalline unit cells and finite nanoparticles, where surface effects and size-dependent distortions dominate. C2NP defines two complementary tasks: (i) generating nanoparticles of specified radii from periodic unit cells, testing whether models capture surface truncation and geometric constraints; and (ii) recovering bulk lattice parameters and space-group symmetry from finite particle configurations, assessing whether models can infer underlying crystallographic order despite surface perturbations. Using diverse materials as a structurally consistent testbed, we construct over 170,000 nanoparticle configurations by carving particles from supercells derived from DFT-relaxed crystal unit cells, and introduce size-based splits that separate interpolation from extrapolation regimes. Experiments with state-of-the-art approaches, including diffusion, flow-matching, and variational models, show that even when losses are low, models often fail geometrically under distribution shift, yielding large lattice-recovery errors and near-zero joint accuracy on structure and symmetry. Overall, our results suggest that current methods rely on template memorization rather than scalable physical generalization. C2NP offers a controlled, reproducible framework for diagnosing these failures, with immediate applications to nanoparticle catalyst design, nanostructured hydrides for hydrogen storage, and materials discovery. Dataset and code are available at https://github.com/KurbanIntelligenceLab/C2NP. Extrapolation ; Benchmark (surveying) ; Interpolation (computer graphics) ; Generative model ; Surface (topology) ; Superposition principle ; Scalability ; Unit sphere Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quasicrystal Structures and Properties
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: Open MIND (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

C2NP: A Benchmark for Learning Scale-Dependent Geometric Invariances in 3D Materials Generation Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remains largely untested. We introduce Crystal-to-Nanoparticle (C2NP), a systematic benchmark for evaluating generative models when moving between infinite crystalline unit cells and finite nanoparticles, where surface effects and size-dependent distortions dominate. C2NP defines two complementary tasks: (i) generating nanoparticles of specified radii from periodic unit cells, testing whether models capture surface truncation and geometric constraints; and (ii) recovering bulk lattice parameters and space-group symmetry from finite particle configurations, assessing whether models can infer underlying crystallographic order despite surface perturbations. Using diverse materials as a structurally consistent testbed, we construct over 170,000 nanoparticle configurations by carving particles from supercells derived from DFT-relaxed crystal unit cells, and introduce size-based splits that separate interpolation from extrapolation regimes. Experiments with state-of-the-art approaches, including diffusion, flow-matching, and variational models, show that even when losses are low, models often fail geometrically under distribution shift, yielding large lattice-recovery errors and near-zero joint accuracy on structure and symmetry. Overall, our results suggest that current methods rely on template memorization rather than scalable physical generalization. C2NP offers a controlled, reproducible framework for diagnosing these failures, with immediate applications to nanoparticle catalyst design, nanostructured hydrides for hydrogen storage, and materials discovery. Dataset and code are available at https://github.com/KurbanIntelligenceLab/C2NP. Extrapolation ; Benchmark (surveying) ; Interpolation (computer graphics) ; Generative model ; Surface (topology) ; Superposition principle ; Scalability ; Unit sphere Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quasicrystal Structures and Properties
Authors: Yuchen Zhu | Di Zhao | Yangyang Zhang | Yitong Li | Xiaorui Wang | Shuwang Li | Yue Kong | Beichen Zhang | Ricki Chen | Chang Liu (35901) | Xingcai Zhang | Tingjun Hou | Chang-Yu Hsieh
Source: arXiv (Cornell University) (preprint, 2026-02-28)
Field: Materials Science — Materials Chemistry

Designing the Haystack: Programmable Chemical Space for Generative Molecular Discovery Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space. Chemical space ; Space (punctuation) ; Executable ; Graph ; Synthetic biology ; Drug discovery ; Tree traversal ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yuchen Zhu | Di Zhao | Yangyang Zhang | Yitong Li | Xiaorui Wang | Shuwang Li | Yue Kong | Beichen Zhang | Ricki Chen | Chang Liu (35901) | Xingcai Zhang | Tingjun Hou | Chang-Yu Hsieh
Source: arXiv (Cornell University) (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Designing the Haystack: Programmable Chemical Space for Generative Molecular Discovery Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space. Chemical space ; Space (punctuation) ; Executable ; Graph ; Synthetic biology ; Drug discovery ; Tree traversal ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Dominika Rapacz-Kinas | Katarzyna. Smolińska-Kempisty | Agnieszka Urbanowska | Joanna Wolska
Source: Molecules (article, 2026-03-11)
Field: Chemistry — Analytical Chemistry

Detection of S-Metolachlor in Surface Water near Cornfields Using pH-Sensitive Green Molecularly Imprinted Polymers In this study, <i>core-shell</i> molecularly imprinted polymers (CS-MIP) were utilized for the detection of the herbicide S-metolachlor in surface water samples, collected from a river and pond that are in the proximity of cornfields. The study revealed that no traces of herbicide were detected in the samples that were analyzed. The collected water samples were treated with membrane filtration-microfiltration and ultrafiltration. The adsorption isotherms were fitted using the Langmuir, Freundlich, Dubinin-Radushkevich, and Scatchard models. This indicated that the Scatchard model is the most appropriate for CS-MIP. The data obtained from the kinetic study were analyzed using the pseudo-first-order and pseudo-second-order models, as well as Fick's second law. For CS-MIP, the most suitable model was determined to be the particle diffusion model, while for <i>core-shell</i> non-imprinted polymers (CS-NIP), the film diffusion model was identified as the limiting step. A method for the desorption of S-metolachlor from the pH-sensitive sorbent bed has been developed, thereby enabling the material to be reused. The optimum eluent from the multicomponent solution was determined to be a 30% aqueous ethanol solution with a pH of approximately 9. This solution effectively removed the majority of contaminants, with the exception of S-metolachlor, which was retained within polymer pores. Molecularly imprinted polymer ; Sorbent ; Adsorption ; Polymer ; Desorption ; Aqueous solution ; Diffusion Analytical chemistry methods development ; Pesticide and Herbicide Environmental Studies ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Samuel Cognolato | Davide Rigoni | Marco Ballarini | Luciano Serafini | Stefano Moro | Alessandro Sperduti
Source: Neurocomputing (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

D4: Distance diffusion for a truly equivariant molecular design In recent years, there has been a growing interest in using generative models for de novo drug design. State-of-the-Art methods typically focus on either 2D structures or 3D structures, also known as conformers. Designing 3D structures is more challenging because it involves predicting spatial coordinates, necessitating the use of SE(3) equivariant architectures to ensure consistency under coordinate transformations like rotations and translations. This study presents D4, a novel Distance and Discrete Denoising Diffusion model that utilizes the distance matrix of molecular atoms to predict a molecule’s 3D coordinates, which are naturally unaffected by such transformations. This method effectively sidesteps the difficulties encountered with traditional coordinate-based training done by State-of-the-Art methods and allows explicit conditioning of bond types on distances. The experiments performed on three well-established datasets — QM9, GDB13, and ZINC250K — of varying challenges show that this approach significantly surpasses the performance of MiDi, a State-of-the-Art approach for generating 3D molecular structures. Additionally, an ablation study confirms the significance of adopting a novel regularization loss, which addresses errors in distance predictions and bounds the triangle inequality, validating the use of distance matrices in molecular generative models. • Generation of 3D molecules through distance and discrete denoising diffusion • SE(3) equivariance is built into the model by the use of distances • A loss on eigenvalues leads to better generation of Euclidean Distance Matrices in 3D • D4 surpasses State-of-the-Art model in the generation of realistic dis- tances in QM9, ZINC, and GDB13 • KDEs offer additional qualitative insights, showing improved distribu- tion learning Equivariant map ; Euclidean distance ; Regularization (linguistics) ; Focus (optics) ; Consistency (knowledge bases) ; Distance measures ; Eigenvalues and eigenvectors ; Euclidean geometry ; Scalability ; Noise reduction Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions ; Chemical Synthesis and Analysis