jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meiru Liu | Yinru Jiang | Penghui Yuan | Shuang Li | Baoxi Zhang | Xia Zhou | Bin Su | Yifei Xie | Dezhi Yang | Linglei Kong | Li Zhang | Yang Lv | Guanhua Du
Source: International Journal of Pharmaceutics X (article, 2025-11-06)
Field: Chemistry — Physical and Theoretical Chemistry

Crystal engineering optimizes emodin-tetramethylpyrazine combination: From cocrystal design to in vivo anti-colitis efficacy assessment Emodin (EMO) shows therapeutic promise for ulcerative colitis (UC), yet its clinical utility is hampered by low bioavailability. To rationally overcome this limitation, this study employed cocrystal engineering, strategically selecting tetramethylpyrazine (TMP)-a natural compound from traditional Chinese medicine-as the cocrystal coformer (CCF). The selection of TMP was guided by a systematic CCF screening strategy, incorporating extensive literature analysis of natural compound CCF candidates, computational chemistry methods to predict favorable hydrogen-bonding interactions and interaction sites with EMO, and machine learning assessment of cocrystallization propensity. Utilizing this rational design approach, we successfully synthesized and characterized a novel EMO-TMP cocrystal through comprehensive solid-state characterization techniques. The resulting cocrystal significantly enhanced the aqueous solubility of EMO while preserving its intrinsic bioactivity. Pharmacokinetic studies confirmed that the cocrystal formulation markedly improved the oral bioavailability of EMO. In a dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) model, the EMO-TMP cocrystal demonstrated superior efficacy compared to EMO alone, effectively alleviating colitis symptoms and associated pathological markers. This enhanced <i>in vivo</i> efficacy is attributed to the significantly improved systemic exposure achieved through the rationally designed cocrystal. Our findings establish the EMO-TMP cocrystal as a highly promising strategy to surmount the physicochemical barriers of EMO, unlocking its full clinical potential for UC treatment. Critically, this work not only validates TMP as an efficient and safe CCF specifically suited for active pharmaceutical ingredients (APIs) rich in hydrogen-bond donors, but also exemplifies the value of leveraging formulation principles and compatible components inherent in traditional Chinese medicine through advanced crystal engineering approaches. Cocrystal ; Rational design ; Ulcerative colitis ; Pharmacokinetics ; Bioavailability ; Solubility ; Crystal engineering Crystallography and molecular interactions ; Crystal structures of chemical compounds ; Crystallization and Solubility Studies
Authors: Lorena Alzate-Vargas | Kashi N. Subedi | Roxanne Tutchton | M. Cooper | Tammie Gibson | Richard A. Messerly
Source: arXiv (Cornell University) (preprint, 2025-07-23)
Field: Materials Science — Materials Chemistry

Atomistic modeling of uranium monocarbide with a machine learning interatomic potential Uranium monocarbide (UC) is an advanced ceramic fuel candidate due to its superior uranium density and thermal conductivity compared to traditional fuels. To accurately model UC at reactor operating conditions, we developed a machine learning interatomic potential (MLIP) using an active learning procedure to generate a comprehensive training dataset capturing diverse atomic configurations. The resulting MLIP predicts structural, elastic, thermophysical properties, defect formation energies, and diffusion behaviors, aligning well with experimental and theoretical benchmarks. This work significantly advances computational methods to explore UC, enabling efficient large-scale and long-time molecular dynamics simulations essential for reactor fuel qualification. Uranium ; Thermal conductivity ; Work (physics) ; Interatomic potential ; Molecular dynamics ; Ceramic ; Experimental data ; Thermal Nuclear Materials and Properties ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Amit Kumar (81180) | Arslan Akbar (22276740) | Hannah Lesmes (22276743) | Seán R. Kavanagh (12004154) | David O. Scanlon (1234266) | Jakoah Brgoch (1485937)
Source: Figshare (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Machine-Learning-AssistedDiscovery of Cr<sup>3+</sup>-Based Near-Infrared Phosphors Cr<sup>3+</sup>-substituted inorganic phosphors exhibit three distinct near-infrared (NIR) photoluminescence emission peak shapes that typically fall between 650 and 950 nm. The exact position and shape are governed by the (weak, intermediate, or strong) crystal field splitting environment of the octahedrally coordinated Cr<sup>3+</sup> ions. These emission characteristics are commonly quantified by the Dq/<i>B</i> ratio, where Dq represents the crystal field splitting parameter and <i>B</i> is the Racah parameter. Precise knowledge of this ratio is therefore critical for designing Cr<sup>3+</sup>-based NIR phosphors for applications like biomedical imaging, night vision, food quality analysis, and luminescence thermometry. Unfortunately, targeting specific Dq/<i>B</i> values in the solid state remains nontrivial due to the complex interplay between the composition, structure, and local coordination environment. To address this challenge, we developed a machine-learned regression model capable of predicting Dq/<i>B</i> trained on 193 experimentally determined Dq/<i>B</i> values and their associated compositional and structural features. We then applied it to estimate the Dq/<i>B</i> values of over 6060 known inorganic structures with potential octahedral Cr<sup>3+</sup> substitution sites. Eight phosphor hosts, Y<sub>2</sub>Mg<sub>3</sub>Ge<sub>3</sub>O<sub>12</sub>, YInGe<sub>2</sub>O<sub>7</sub>, LiInW<sub>2</sub>O<sub>6</sub>, Gd<sub>3</sub>SbO<sub>7</sub>, Ba<sub>2</sub>ScTaO<sub>6</sub>, Ba<sub>2</sub>MgWO<sub>6</sub>, LiLaMgWO<sub>6</sub>, and Ca<sub>3</sub>MgSi<sub>2</sub>O<sub>8</sub>, representing a range of crystal field environments were selected from this list for synthesis and characterization. Their measured Dq/<i>B</i> values closely match model predictions, demonstrating the utility of this machine-learning framework for accelerating the discovery of application-specific Cr<sup>3+</sup>-substituted NIR phosphors. Phosphor ; Photoluminescence ; Luminescence ; Crystal (programming language) ; Octahedron ; Crystal structure ; Solid-state lighting ; Field (mathematics) Luminescence Properties of Advanced Materials ; Pigment Synthesis and Properties ; Advanced Photocatalysis Techniques
Authors: Dhamala, Anish | Bhattarai, Sarthak | Gurung, Basanta | Hyolmo, Chhuldim | Neupane, Mahesh Kumar | Lamichhane, Rupak Raj | Chaulagain, Shivaji | Magar, Bijay Sijapati
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

Comparative Study of Soft and Hard Boundary Constraints in Physics-Informed Neural Networks for Quantum Mechanical Eigenvalue Problems Developing methods to accurately and efficiently solve the Schrödinger equation is important for advancing computational studies in quantum physics and chemistry. In this work, we embed physical and mathematical constraints into unsupervised neural network training to solve the Schrödinger equation for different potentials and focus on comparing the performance of models trained with soft and hard boundary constraints. We benchmark the solutions against analytical results when available, or numerical solvers otherwise, to assess accuracy. For the harmonic oscillator, the soft-constrained model accurately predicts multiple excited states but requires more training epochs and fails for the Coulomb and the screened Coulomb potentials when the angular momentum is not zero. Hard-constrained models can converge more efficiently, though their success depends strongly on the chosen formulation. In particular, ansätze that reflect the asymptotic behavior of solutions and include a learnable parameter achieve accurate results with fewer epochs. Additional gains in both accuracy and efficiency are obtained by employing adaptive learning rate scheduling based on the differential equation residual. Artificial neural network ; Eigenvalues and eigenvectors ; Benchmark (surveying) ; Boundary value problem ; Coulomb ; Quantum ; Differential equation ; Angular momentum Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Claudio Rapisarda | Matthew McGilvray | Luca di Mare
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physics-Informed Deep Neural Operators with Autoencoders for Accelerated Thermochemical Nonequilibrium Kinetics Non-equilibrium thermodynamics ; Kinetics ; Physics ; Statistical physics ; Artificial neural network ; Deep neural networks ; Artificial intelligence ; Computer science ; Thermodynamics ; Classical mechanics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: L. F. Trugilho | Stefan Auer | L. G. Rizzi | Sergei V. Krivov
Source: Journal of Chemical Theory and Computation (article, 2025-10-19)
Field: Chemistry — Spectroscopy

Nonparametric Determination of the Committor in Multimolecular Systems A fundamental problem in analyzing large longitudinal data sets modeling dynamics in multimolecular systems is determining the underlying free-energy landscapes as a function of the committor, the optimal reaction coordinate. Here, we demonstrate that by combining a nonparametric approach with a systematic method for generating permutationally invariant collective variables, the committor can be effectively determined to describe multimolecular aggregation in a system with anisotropic interactions. The optimality of the committor is verified by a stringent validation test, and it is shown that the diffusive model along the committor yields kinetic properties identical to those derived from the original dynamics. Our method is general and relevant to the large machine learning community developing methods to determine the committor from longitudinal data sets. Nonparametric statistics ; Invariant (physics) ; Function (biology) ; Anisotropy ; Parametric statistics ; Complex system ; Kinetic energy ; Experimental data Analytical Chemistry and Chromatography ; thermodynamics and calorimetric analyses ; Chemical Thermodynamics and Molecular Structure
Authors: Yi Fang | Chun Wang | Sihai Zhang
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2025-06-03)
Field: Materials Science — Surfaces, Coatings and Films

Physics-Informed Machine Learning-Based Edge Detection for SEM Images The Scanning Electron Microscope (SEM) images of Random Access Memory (RAM) chips contain valuable processrelated information, particularly at the edges, which can provide critical insights for hotspot detection and Line Edge Roughness (LER) measurement. However, significant noise and low grayscale variation in SEM images often lead to edge omissions and misdetections. In this paper, we introduce the concept of the SEM Interlayer Effect (SIE), based on empirical observations and theoretical analysis, to address these challenges. Leveraging insights from SIE, we propose a novel Physics-Informed Edge Detection (PIED) method, which enhances the underlying neural network architecture and incorporates a hierarchical weighted loss function. Based on the real-world SEM image dataset from RAM production, PIED achieves a superior Optimal Dataset Scale (ODS) F-measure compared to the Canny edge detector, improving from 0.9001 to 0.9701—a 7.8% increase. This demonstrates that even in the absence of ground truth, PIED significantly enhances edge detection performance, which is crucial for improving process control in semiconductor manufacturing. Edge detection ; Enhanced Data Rates for GSM Evolution ; Artificial intelligence ; Computer science ; Engineering drawing ; Image processing ; Computer vision ; Machine learning ; Engineering ; Materials science ; Physics ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Machine Learning in Materials Science
Authors: Ivan Saha | Lukáš Grajciar | Daniel Willimetz
Source: ChemRxiv (article, 2025-09-19)
Field: Materials Science — Surfaces, Coatings and Films

CRISP: Enhancing ASE Workflows with Advanced Molecular Simulation Post-Processing Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customisation, interactivity, and efficiency with large datasets. To address this, we developed CRISP (Comprehensive Repository for Insightful Simulation Post-Processing), an open-source Python toolkit designed to enhance workflows within the Atomic Simulation Environment (ASE). CRISP provides a versatile platform for detailed analysis and visualisation, featuring a customizable and modular design, various static analysis methods, interactive 3D visualisations, and parallel processing capabilities optimised for high-performance computing. We demonstrate its effectiveness through case studies, including the analysis of statistical convergence in zeolite simulations, subsampling large datasets for machine learning, and analysing the dynamic stability of atomic clusters. CRISP effectively bridges the gap between raw simulation data and actionable insights, offering an efficient solution for researchers and saving significant time in code development. Workflow ; Python (programming language) ; Modular design ; Toolbox ; Bridging (networking) ; Raw data ; Scalability ; Stability (learning theory) ; Extensibility Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Clara S. Bender | Anna C. Cardall | F. Rodríguez | J. Kerwin | Ricardo Mejia | Adam J. Wachtor
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Variable Atwood Number Rayleigh-Taylor InstabilitiesInducedby Volumetric Energy Deposition Detection and identification of nonlinearity is a task of high importance for structural dynamics.On the one hand, identifying nonlinearity in a structure would allow one to build more accurate models of the structure.On the other hand, detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage within the structure.Common damage cases which cause nonlinear behaviour are breathing cracks and points where some material may have reached its plastic region.Therefore, it is important, even for safety reasons, to detect when a structure exhibits nonlinear behaviour.In the current work, a method to detect nonlinearity is proposed, based on the distribution of the gradients of a data-driven model, which is fitted on data acquired from the structure of interest.The data-driven model selected for the current application is a neural network.The selection of such a type of model was done in order to not allow the user to decide how linear or nonlinear the model shall be, but to let the training algorithm of the neural network shape the level of nonlinearity according to the training data.The neural network is trained to predict the accelerations of the structure for a time-instant using as input accelerations of previous time-instants, i.e. one-step-ahead predictions.Afterwards, the gradients of the output of the neural network with respect to its inputs are calculated.Given that the structure is linear, the distribution of the aforementioned gradients should be unimodal and quite peaked, while in the case of a structure with nonlinearities, the distribution of the gradients shall be more spread and, potentially, multimodal.To test the above assumption, data from an experimental structure are considered.The structure is tested under different scenarios, some of which are linear and some of which are nonlinear.More specifically, the nonlinearity is introduced as a column-bumper nonlinearity, aimed at simulating the effects of a breathing crack and at different levels, i.e. different values of the initial gap between the bumper and the column.Following the proposed method, the statistics of the distributions of the gradients for the different scenarios can indeed be used to identify cases where nonlinearity is present.Moreover, via the proposed method one is able to quantify the nonlinearity by observing higher values of standard deviation of the distribution of the gradients for lower values of the initial column-bumper gap, i.e. for "more nonlinear" scenarios. Deposition (geology) ; Variable (mathematics) ; Energy (signal processing) ; Intensity (physics) ; Relation (database) Diamond and Carbon-based Materials Research ; Laser-induced spectroscopy and plasma
Authors: Bhaskar Rana | Nicolas Viot | Jessica A. Martinez B. | Xuecheng Shao | Pablo Ramos | Michele Pavanello
Source: Journal of Chemical Theory and Computation (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds Machine learning of the one-electron reduced density matrix (1-RDM) provides a computationally efficient surrogate to conventional electronic structure methods. In this work, we train models that map the electron-nuclear interaction potential to the 1-RDM with such an accuracy that predicted 1-RDMs deviate from fully converged ones by no more than a standard self-consistent field (SCF) threshold. Through targeted model optimization strategies, we demonstrate that training set sizes substantially smaller than those required in our previous work [Shao, X. <i>Nat. Commun.</i> 14, 6281 (2023)] are sufficient to reach this accuracy. Furthermore, we introduce a force-correction algorithm that enables stable ab initio molecular dynamics powered by the machine learned 1-RDMs, extending the applicability of the surrogate electronic structure methods to molecules as large as biphenyl. Convergence (economics) ; Set (abstract data type) ; Field (mathematics) ; Matrix (chemical analysis) ; Ab initio ; Electronic structure ; Work (physics) ; Training set Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Priya Kumar | Prashik B. Dudhe | Shrikant Nilewar
Source: Journal of Pharmaceutical Research and Integrated Medical Sciences (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

Analytical Method Validation in Pharmaceutical Quality Control: Current Guidelines and Challenges Analytical method validation (AMV) in the veterinary field forms an important aspect of quality control in pharmaceutical companies in which drugs are used to treat various species of animals to guarantees safety, efficacy and regulatory actions are adhered to. This review points to the significance of the species-based approaches in pharmacokinetics of drug absorption, distribution, metabolism, and residue detection with a focus on the issues of physiological diversity, complex biological samples and ethical requirement. Modern methods such as HPLC, UPLC, GC, LC-MS/MS, UV-Vis, and FTIR are characterized by sensitivity, specificity, and reproducibility and newer techniques such as capillary electrophoresis and microfluidic-based technology, high-resolution mass spectrometry and bioinformatics-directed validation incorporates enhanced sensitivity, specificity, reproducibility, reduced sample volumes, and increased throughput. Future methodological advances in multi-dimensional platforms, green analytical chemistry, chemometrics, and machine learning offer new ways of addressing matrix effects and resource limitations and regulatory limitations. Taken together, these strategies can contribute to effective veterinary drug monitoring, food safety, and sustainable and species-specific AMV practices and direct future research at achieving harmonized and technologically more advanced validation processes. Quality (philosophy) ; Current (fluid) ; Computer science ; Control (management) ; Biochemical engineering ; Risk analysis (engineering) ; Reliability engineering ; Engineering ; Medicine ; Physics ; Artificial intelligence ; Electrical engineering Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Bhaskar Rana (6894608) | Nicolas Viot (22836737) | Jessica A. Martinez B (22438631) | Xuecheng Shao (7108694) | Pablo Ramos (1581832) | Michele Pavanello (1302207)
Source: Figshare (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Learning the One-Electron Reduced Density Matrix atSCF Convergence Thresholds Machine learning of the one-electron reduced density matrix (1-RDM) provides a computationally efficient surrogate to conventional electronic structure methods. In this work, we train models that map the electron−nuclear interaction potential to the 1-RDM with such an accuracy that predicted 1-RDMs deviate from fully converged ones by no more than a standard self-consistent field (SCF) threshold. Through targeted model optimization strategies, we demonstrate that training set sizes substantially smaller than those required in our previous work [Shao, X. Nat. Commun. 14, 6281 (2023)] are sufficient to reach this accuracy. Furthermore, we introduce a force-correction algorithm that enables stable ab initio molecular dynamics powered by the machine learned 1-RDMs, extending the applicability of the surrogate electronic structure methods to molecules as large as biphenyl. Convergence (economics) ; Set (abstract data type) ; Field (mathematics) ; Matrix (chemical analysis) ; Ab initio ; Work (physics) ; Training set ; Electronic structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Ao Wang (1688005) | Wenkai Liu (6649370) | Xiaohan Jin (11146542) | Hecheng Wu (13181181) | Dongfei Zhang (18106394) | Xianglong Han (1450315) | Yang Liu (4829) | Zhen Li (49109) | Mingming Ding (1674565) | Jiehua Li (472477) | Hong Tan (472478)
Source: Figshare (article, 2025-04-28)
Field: Materials Science — Biomaterials

Dynamics andMachine Learning Reveal the Link betweenTripeptide Sequences and Evaporation-Driven Material Properties Previous research showed that a peptide composed of three tyrosines (YYY) can turn into organic glass and cause strong adhesion between substrates via evaporation. However, the mechanisms of these processes remain unclear, and the exploration of applications of other peptide sequences is necessary. In this study, an optimized evaporation method was employed in molecular dynamics. It was found that YYY evaporation products possess abundant internal hydrogen bonds, which may facilitate the amorphous glass state formation. Moderate hydrophilicity of a peptide enhances molecular mobility and self-healing ability, while excessive hydrophilicity causes a water plasticizing effect. Stronger hydrophilicity also brings a larger curvature of evaporation products on polydimethylsiloxane (PDMS) substrate. A machine learning model was developed to predict the evaporation contact angle of peptide evaporation products and agrees well with the experiment. This research aims to improve understanding of peptide structure–function relationships and aid in designing custom organic optical devices based on peptide sequences. Polydimethylsiloxane ; Evaporation ; Peptide ; Tripeptide ; Amorphous solid ; Molecular dynamics ; Curvature Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis