jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivpuje Shivraj S. | Patwekar Shailesh L. | Pawde Pradip S. | Bansode Santosh S. | Pohekar Abhijeet S. | Waman Rahul L.
Source: International Journal of Drug Delivery Technology (article, 2026-04-09)
Field: Chemistry — Physical and Theoretical Chemistry

Co-Former Selection And Optimization Strategies For Solubility Enhancement Of Drugs: A Review Poor aqueous solubility continues to hinder the development of many oral drug formulations, restricting both their bioavailability and therapeutic performance. One approach that has attracted increasing attention is cocrystallization, which can alter key physicochemical properties of a compound without changing its pharmacological profile. In this review, we examine how molecular docking and related computational methods can be used to guide the rational choice of co-formers in co-crystal design. Docking studies shed light on binding strength, hydrogenbond preferences, and other intermolecular interactions, thereby helping to narrow down the pool of potential drug– co-former combinations. We highlight examples involving carboxylic acids, amino acids, and GRAS-listed coformers, while also pointing out where computational predictions fall short of experimental outcomes, particularly due to limited co-former diversity. Experimental techniques such as PXRD, DSC, and SCXRD are considered alongside current regulatory guidance from the FDA and EMA, since both scientific feasibility and regulatory acceptance are critical to successful development. We conclude by discussing the need to combine docking with molecular dynamics, crystal structure prediction, and machine learning approaches, which together may yield more reliable predictions. Unlike earlier reviews that typically emphasize either computational or experimental perspectives, our aim is to integrate both, and to place them within the broader regulatory context, in order to provide a practical framework for co-former selection. Docking (animal) ; Regulatory science ; Computational model ; Drug discovery ; Selection (genetic algorithm) Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems
Authors: Shuaishuai Yuan | Gunther G. Andersson | Gregory F. Metha | Zetian Mi | Hong Guo
Source: Open MIND (preprint, 2026-02-08)
Field: Materials Science — Materials Chemistry

Design principles for III-nitride-nanocluster photocatalysts from region-resolved electronic structure Understanding how nanocluster cocatalysts modify the electronic structure of III-nitride surfaces is central to the rational design of efficient photocatalytic interfaces. Here, we establish design principles for nanocluster cocatalysts on GaN-based semiconductors by systematically analyzing the spatially resolved electronic structure of GaN-, InGaN-, and ScGaN-based slabs decorated with six-atom elemental nanoclusters. Using a region-resolved projected local density of states (PLDOS) framework, we reveal that semiconductor-nanocluster interfaces operate as laterally heterogeneous electronic systems, in which nanocluster-covered regions govern charge injection and band bending, while uncovered nitride regions retain surface states that facilitate surface activation. We further show that cocatalyst effectiveness is controlled not only by hydrogen adsorption energy, but also by interfacial electrostatics, including band alignment, metal-induced gap-state suppression, and in-plane dipoles, with the semiconductor substrate defining the baseline electronic regime. Machine-learning regression models trained on physically motivated global and region-specific descriptors quantify the relative importance of these mechanisms and their correlation with hydrogen adsorption energetics. Together, this work provides transferable design principles for nanocluster cocatalysts on III-nitrides and a generalizable first-principles framework for studying spatially heterogeneous semiconductor-nanocluster interfaces. Electronic structure ; Rational design ; Semiconductor ; Design elements and principles ; Substrate (aquarium) ; Density functional theory ; Electronic band structure ; Adsorption Machine Learning in Materials Science ; 2D Materials and Applications ; GaN-based semiconductor devices and materials
Authors: Shumpei Chochi | Aye Chan Myint | Sota Komatsu | Tomoyuki Okuda | Hideaki Sasazawa | Yasutaka Toyoda
Source:  (article, 2026-04-08)
Field: Materials Science — Surfaces, Coatings and Films

Rapid adaptation of SEM image denoising model using conditional normalization In semiconductor manufacturing, Scanning Electron Microscope (SEM) images are utilized for quality improvement such as critical dimension (CD) measurements, overlay (OVL) measurement assessments, and defect inspections. SEM images typically suffer from low signal-to-noise ratio (SNR), resulting in poor image quality. To address this, deep learning models have been introduced for image enhancement. A major challenge, however, is that these models require retraining whenever SEM image patterns change, leading to prolonged training times that can disrupt manufacturing workflows. In this study, we propose a method that pre-trains the model on general pattern images and applies fine-tuning for new SEM image pattern types. To enable efficient adaptation to different patterns, we incorporate conditional normalization into our deep learning model. Experimental results demonstrate that our approach reduces training time from hours to minutes while preserving the precision of OVL measurements compared to existing models. Pattern recognition (psychology) ; Normalization (sociology) ; Noise reduction ; Image denoising ; Noise (video) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Image and Signal Denoising Methods
Authors: Jo S. Kurian | Ankit Mahajan | Sandeep Sharma
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Nuclear gradients from auxiliary-field quantum Monte Carlo and their application in geometry optimization and transition state search In this article, we present a method for computing accurate and scalable nuclear forces within the phaseless auxiliary-field quantum Monte Carlo (AFQMC) framework. Our approach leverages automatic differentiation of the energy functional to obtain nuclear gradients at a computational cost comparable to that of energy evaluation. The accuracy of the method is validated against finite difference calculations, showing excellent agreement. We then explore several machine learning (ML) strategies for learning noisy AFQMC data. These ML potentials are subsequently used to perform geometry optimizations and nudged elastic band (NEB) calculations, successfully identifying the transition state of the formamide-formimidic acid tautomerization. The resulting transition state geometry and barrier heights are in close agreement with coupled-cluster reference values. This work paves the way for highly accurate geometry optimization, molecular dynamics, or reaction path calculations. Quantum Monte Carlo ; Energy minimization ; Monte Carlo method ; Work (physics) ; State (computer science) ; Path (computing) ; Quantum ; Energy (signal processing) Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced NMR Techniques and Applications
Authors: Yu. D. Fomin
Source: arXiv (Cornell University) (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

The origin of complex behavior of liquid carbon: an insight from computer simulation In the present paper we perfomrm molecular dynamics simulation of liquid carbon with a machine-learning potential GAP-20. We show that within the framework of this model carbon demonstrates a relatively low critical temperature, which can affect the results of experimental measurements of melting point of graphite. Molecular dynamics ; Point (geometry) ; Carbon fibers ; Dynamics (music) ; Melting point ; Complex system Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties
Authors: Avvaru Praveen Kumar | Venkateswarlu V. | Maddike Rama Mohana Reddy
Source: Next Materials (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Emerging frontiers in machine learning-assisted design of functional nanomaterials Machine learning (ML) has facilitated the design and optimization of functional nanomaterials by accelerating the search of more complex material compositions and structures than traditional trial-and-error and first-principles methods allow. Large conscriptive bibliographies of this literature seem to ignore the vast chasm between high predictive metrics and the empirical realities of the world. To fill this gap, this review aimed to create, evaluate, and analyze the state-of-the-art on ML-based design of functional nanomaterials. This indicates a conceptual model that outlines a macro perspective of the entire life cycle starting from initiating the end-to-end operational flow through data creation, descriptor engineering, model validation, and deployment to production. Not only is this not just a report about such performance measures, but rather it critiques their validity as methodology comparisons; quantifying use criteria like energy, electronics, and biomedicine across methodologies (regression, deep learning (DL), generative models) against one another. The review identifies typical obstacles to improvement, the urge to shun information, the risk of overfitting and data information, and the disregard for the synthesizability restrictions in inverse design. The systematic review includes 75 peer-reviewed publications from 2021 to 2026 that relate to the research topic and focus on the design of functional nanomaterials assisted by ML in energy, electronics, biomedicine, and environmental research. Unlike in the previous descriptive surveys, the paper is a critical analysis of the strength of the methods and validation processes, and the shortcomings of synthesizability, and found notable gaps in the reproducibility, thermodynamic screening, and physics-informed integration. Nanomaterials ; Flexibility (engineering) ; Context (archaeology) ; Emerging technologies Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Chengchun Liu | Wendi Cai | Boxuan Zhao | Fanyang Mo
Source: Open MIND (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement Accurate molecular geometries are a prerequisite for reliable quantum-chemical predictions, yet density functional theory (DFT) optimization remains a major bottleneck for high-throughput molecular screening. Here we present GeoOpt-Net, a multi-branch SE(3)-equivariant geometry refinement network that predicts DFT-quality structures at the B3LYP/TZVP level of theory in a single forward pass starting from inexpensive initial conformers generated at a low-cost force-field level. GeoOpt-Net is trained using a two-stage strategy in which a broadly pretrained geometric representation is subsequently fine-tuned to approach B3LYP/TZVP-level accuracy, with theory- and basis-set-aware calibration enabled by a fidelity-aware feature modulation (FAFM) mechanism. Benchmarking against representative approaches spanning classical conformer generation (RDKit), semiempirical quantum methods (xTB), data-driven geometry refinement pipelines (Auto3D), and machine-learning interatomic potentials (UMA) on external drug-like molecules demonstrates that GeoOpt-Net achieves sub-milli-Å all-atom RMSD with near-zero B3LYP/TZVP single-point energy deviations, indicating DFT-ready geometries that closely reproduce both structural and energetic references. Beyond geometric metrics, GeoOpt-Net generates initial guesses intrinsically compatible with DFT convergence criteria, yielding nonzero ``All-YES'' convergence rates (65.0\% under loose and 33.4\% under default thresholds), and substantially reducing re-optimization steps and wall-clock time. GeoOpt-Net further exhibits smooth and predictable energy scaling with molecular complexity while preserving key electronic observables such as dipole moments. Collectively, these results establish GeoOpt-Net as a scalable, physically consistent geometry refinement framework that enables efficient acceleration of DFT-based quantum-chemical workflows. Energy minimization ; Topology (electrical circuits) ; Bottleneck ; Representation (politics) ; Feature (linguistics) ; Convergence (economics) ; Density functional theory ; Quantum ; Potential energy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Muchen Wang | Yue Fan
Source: Advanced Intelligent Discovery (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning to Understand and Design Disordered Solids Disordered solids lack structural order and exhibit complex atomic environments that challenge conventional theoretical and simulation frameworks. This structural complexity makes it difficult to establish quantitative structure–property relationships and to rationally design new amorphous materials. Recent advances in machine learning (ML) offer powerful tools to address these challenges by enabling high‐dimensional representations of disorder, uncovering hidden correlations, and efficiently exploring expansive compositional and configurational spaces. In this review, we survey the rapidly growing interface between ML and the study of disordered solids. We first examine structural representations that connect atomic disorder with ML models, including composition/property descriptors, atomic‐density encodings, graph representations, and topology‐based methods. We then provide an overview of traditional supervised learning, modern deep‐learning architectures, unsupervised approaches, and generative models for accelerating inverse discovery. Explainable ML techniques are highlighted for their ability to relate data‐driven predictions to underlying physical mechanisms. Building on these foundations, we review emerging applications of ML in predicting global and local structure–property relationships and in enabling inverse design of disordered solids with targeted performance. We conclude by outlining key challenges and opportunities, including the need for large multimodal datasets, interpretable and transferable models, and generative frameworks capable of producing physically meaningful atomic configurations. Collectively, these developments illustrate how ML is becoming an essential tool for understanding and designing disordered solids. Generative grammar ; Expansive ; Key (lock) ; Graph ; Interface (matter) ; Generative model Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Guanchen Wu | Chung-Yueh Yuan | Kareem Hegazy | Samuel M. Blau | Teresa Head-Gordon
Source: ArXiv.org (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohibits their routine use in TS optimization. Here, we present a machine-learning-driven TS optimizer that directly predicts the leftmost Hessian eigenvector (LMHE), the critical mode that locally approximates the reaction coordinate encompassing the TS. We demonstrate that our LMHE optimizer recovers TS solutions at the same rate as full Hessian optimizers, and more robustly from degraded initial guess geometries, thereby eliminating the excessively long wall times characteristic of full-Hessian approaches and reducing total gradient evaluations compared to standard quasi-Newton methods. We further improve accuracy and robustness using uncertainty quantification for identifying occasional LMHE prediction failures, that then falls back to a full Hessian update from the machine learned potential at that optimization step, avoiding expensive active learning. Overall our methodology and semi-automated workflow delivers second-order stability at first-order computational expense to provide a highly efficient engine for high-throughput reaction discovery. Hessian matrix ; Robustness (evolution) ; Eigenvalues and eigenvectors ; Convergence (economics) ; Stability (learning theory) ; Hessian equation ; Rate of convergence Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: Davide G. Sangiovanni | Carlos R. Salazar | Ferenc Tasnádi | Johan Klarbring | Igor Abrikosov
Source: Research Square (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Concerted mass transport governs anomalous diffusion in anharmonic bcc metals Anharmonicity ; Energy landscape ; Molecular dynamics ; Lattice (music) ; Diffusion ; Anomalous diffusion ; Crystallographic defect ; Curvature Machine Learning in Materials Science ; High-pressure geophysics and materials ; nanoparticles nucleation surface interactions
Authors: Shuaishuai Yuan | Gunther G. Andersson | Gregory F. Metha | Zetian Mi | Hong Guo
Source: ArXiv.org (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Design principles for III-nitride-nanocluster photocatalysts from region-resolved electronic structure Understanding how nanocluster cocatalysts modify the electronic structure of III-nitride surfaces is central to the rational design of efficient photocatalytic interfaces. Here, we establish design principles for nanocluster cocatalysts on GaN-based semiconductors by systematically analyzing the spatially resolved electronic structure of GaN-, InGaN-, and ScGaN-based slabs decorated with six-atom elemental nanoclusters. Using a region-resolved projected local density of states (PLDOS) framework, we reveal that semiconductor-nanocluster interfaces operate as laterally heterogeneous electronic systems, in which nanocluster-covered regions govern charge injection and band bending, while uncovered nitride regions retain surface states that facilitate surface activation. We further show that cocatalyst effectiveness is controlled not only by hydrogen adsorption energy, but also by interfacial electrostatics, including band alignment, metal-induced gap-state suppression, and in-plane dipoles, with the semiconductor substrate defining the baseline electronic regime. Machine-learning regression models trained on physically motivated global and region-specific descriptors quantify the relative importance of these mechanisms and their correlation with hydrogen adsorption energetics. Together, this work provides transferable design principles for nanocluster cocatalysts on III-nitrides and a generalizable first-principles framework for studying spatially heterogeneous semiconductor-nanocluster interfaces. Electronic structure ; Rational design ; Semiconductor ; Design elements and principles ; Substrate (aquarium) ; Density functional theory ; Electronic band structure ; Adsorption Machine Learning in Materials Science ; 2D Materials and Applications ; GaN-based semiconductor devices and materials
Authors: Tzu-Hsin Li | Hang Yu | Cheng-Hsun Cheng | Yen-Ju Cheng | Chain‐Shu Hsu | Ta-Ya Chu | Jianping Lu | Yi‐Ming Chang | Fang‐Chung Chen
Source: Expert Systems with Applications (article, 2026-04-06)
Field: Chemistry — Organic Chemistry

Expert systems for predicting the efficiencies of photomultiplication organic photodetectors Expert system ; Photodetector ; Artificial neural network ; Knowledge-based systems Chemistry and Stereochemistry Studies ; Radiation Detection and Scintillator Technologies ; Advanced Chemical Sensor Technologies
Authors: Alejandro Rodriguez | Riccardo Rurali | Changpeng Lin | Joshua Ojih | Mohammed Al-Fahdi | G. Jeffrey Snyder | Ming Hu
Source: npj Computational Materials (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Approaching lower bound of lattice thermal conductivity by simultaneously suppressing diagonal and off-diagonal phonon contributions Pushing the intrinsic lattice thermal conductivity (LTC) in crystalline materials to lower bounds is crucial for fundamental materials research towards emerging technologies including thermoelectric energy conversion and thermal management in both hypersonic aircraft and next-generation turbine systems. However, in the ultralow LTC regime ( < 1 Wm-1K-1), the competition between propagative (particle-like) and coherent phonons—arising from off-diagonal components—poses a significant challenge in further reducing LTC. We perform quantitative analysis of 4700 materials using density functional theory (DFT), spanning all crystallographic groups, to elucidate the interplay between diagonal and off-diagonal phonon contributions. We identify a critical balance between these transport mechanisms, where intermediate phonon lifetimes ( ~ 1 ps) and slow group velocities ( ~ 1 km/s) collectively suppress both contributions, enabling ultralow LTC. Results from a large dataset of 31,058 structures by machine learning models strongly resemble the DFT trends of two-channel phonon transport. Leveraging these models, we screen 25,882 additional materials and confirm their properties with DFT, identifying 12 candidates with ultralow room-temperature LTC—including a record-low value of 0.132 Wm-1K-1. Our large-scale analysis reveals fundamental insights into dual-channel phonon transport, enabling rational design of ultralow LTC materials and accelerating the discovery of advanced phononic crystals with tailored thermal transport properties. Phonon ; Thermal conductivity ; Diagonal ; Thermoelectric materials ; Lattice (music) ; Thermoelectric effect ; Density functional theory Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Shazia Muzaffer | Mohammad Al‐Emrani
Source: Metals (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

The Performance of Accelerated Testing Methods for the Evaluation of Weathering Steels Weathering steel (WS), a class of low-alloy steel, which often outperforms traditional carbon steels in bridge applications, develops a stable and adherent patina that enhances resistance to atmospheric corrosion. The patinas develop through complex electrochemical and physicochemical reactions between steel alloying elements and environmental constituents such as pollutants, oxygen, moisture, chlorides, and sulfur compounds. However, real-life field exposure tests to evaluate the performance of weathering steel in rural, urban, industrial, and marine environments are costly, time-consuming, and inconsistent, prompting the need for accelerated laboratory-based corrosion tests. This paper compiles and thoroughly examines the effectiveness of widely used accelerated corrosion testing techniques, such as ISO 16539 (Synthetic Ocean Water), Cebelcor, Prohesion (ASTM G85), Salt Spray (ISO 9227), Kesternich, and others, in simulating the weathering behavior of weathering steel. Findings show that some accelerated cyclic tests can partially replicate protective patina formation in polluted or sulfate-rich environments, whereas others, such as continuous salt spray, tend to overestimate corrosion due to the absence of key environmental factors such as wet/dry cycles, microbial activity, UV radiation, and wind-driven rain. Existing tests do not adequately replicate real-world steel–environment interactions. This review proposes a multidisciplinary approach combining localized wet/dry cycles, advanced environmental chambers, and microstructural and oxide layer analysis with AI (artificial intelligence)/ML (machine learning) for predictive models to improve test relevance and long-term performance forecasting of weathering steels. Weathering ; Weathering steel ; Corrosion ; Replicate ; Carbon steel ; Sulfur Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis