jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dale, Ashley S. | Li, Kangming | DeCost, Brian | Wan, Hao | Han, Yuchen | Fehlis, Yao | Hattrick-Simpers, Jason
Source: arXiv (Cornell University) (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active learning when using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network model architectures. We compare uncertainty estimates from ALIGNN deep ensembles to loss landscape uncertainty estimates obtained for solubility, bandgap, and formation energy prediction tasks. We then evaluate how the quality of the uncertainty estimate impacts an active learning campaign that seeks model generalization to out-of-distribution data. Uncertainty calibration methods were found to variably generalize from in-domain data to out-of-domain data. Furthermore, calibrated uncertainties were generally unsuccessful in reducing the amount of data required by a model to improve during an active learning campaign on out-of-distribution data when compared to random sampling and uncalibrated uncertainties. The impact of poor-quality uncertainty persists for random forest and eXtreme Gradient Boosting models trained on the same data for the same tasks, indicating that this is at least partially intrinsic to the data and not due to model capacity alone. Analysis of the target, in-distribution uncertainty, out-of-distribution uncertainty, and training residual distributions suggest that future work focus on understanding empirical uncertainties in the feature input space for cases where ensemble prediction variances do not accurately capture the missing information required for the model to generalize. Calibration ; Random forest ; Uncertainty quantification ; Artificial neural network ; Active learning (machine learning) ; Boosting (machine learning) ; Missing data ; Gradient boosting ; Uncertainty analysis ; Ensemble learning Machine Learning in Materials Science ; Machine Learning and Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Hiroaki Matsukawa | Yasunari Miyagi | Katsuto Otake
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Ann-Based Estimation of Pure-Component Parameters of Pc-Saft Equation of State Using Quantum Chemical Data Component (thermodynamics) ; Equation of state ; Quantum chemical ; State (computer science) ; Estimation ; Statistical physics ; Biological system ; Computer science ; Applied mathematics ; Mathematics ; Physics ; Thermodynamics ; Algorithm ; Engineering ; Quantum mechanics ; Biology ; Systems engineering Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Spectroscopy and Laser Applications
Authors: Yan Wang | Huannan Wang | Shuncun Zhang | Yidan Xi | Z Zhang
Source: Journal of Mass Spectrometry (article, 2025-12-30)
Field: Chemistry — Spectroscopy

Desorption Electrospray Ionization Mass Spectrometry Imaging: Principles, Advancements, and Multidisciplinary Applications Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI), as an ambient ionization technique, enables label-free, matrix-free, and minimal sample pretreatment molecular mapping of biological surfaces under atmospheric pressure. Since its inception in 2004, DESI-MSI has evolved through innovations in ionization sources, sprayer design, and data processing, allowing high-throughput visualization of spatial distributions for diverse molecules (e.g., lipids, metabolites, drugs) in tissues. This review systematically reviews the fundamental principles, instrumental configurations, and critical technical parameters of DESI-MSI. Recent technological advancements, specifically the development of nanoscale DESI achieving spatial resolutions under 50 μm and the implementation of additive-enhanced solvents such as metal ions and reactive anions to enhance sensitivity and specificity, are critically evaluated. Furthermore, the multidisciplinary applications of DESI-MSI are comprehensively examined: (1) in biomedicine, it facilitates cancer margin delineation (e.g., breast, prostate tumors) and neuropathological biomarker discovery; (2) in pharmaceutical research, it enables in situ drug distribution analysis and tissue metabolism profiling; (3) in plant sciences, it supports spatial mapping of phytochemicals, pesticide residues, and host-microbe interactions. Nevertheless, despite its advantages in real-time analysis and nondestructive sampling, inherent challenges persist, including limited spatial resolution (~200 μm) and complexities in interpreting complex datasets. Future work should emphasize the integration of multimodal imaging approaches, machine learning-driven data processing pipelines, and clinical translation strategies for intraoperative diagnostics. Collectively, this study positions DESI-MSI as a transformative spatial metabolomics tool with expanding potential in clinical diagnostics and high-throughput drug screening. Desorption electrospray ionization ; Mass spectrometry imaging ; Mass spectrometry ; Multidisciplinary approach ; Sample preparation ; Electrospray ionization ; Ambient ionization ; Visualization ; Drug development Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: He Zhang | Houjun Qi | Lingtao Weng | Weiguo Gao | Zhen Yang | Yanling Tian | Hengning Hu
Source: Journal of Materials Science (article, 2025-10-28)
Field: Materials Science — Surfaces, Coatings and Films

A closed-loop framework for performance-oriented design of superhydrophobic surfaces Multiphysics ; Fabrication ; Solid mechanics ; Wetting ; Surface roughness ; Surface finish ; Oscillation (cell signaling) ; Contact angle Surface Modification and Superhydrophobicity ; Advanced Materials and Mechanics ; Electrowetting and Microfluidic Technologies
Authors: Jinping Zhuang | Bin Chen | Jian Zhao | Peifu Lin | Cheng Shi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Dynamic Fracture Properties of Steel Fiber-Reinforced Potassium Magnesium Phosphate Cementitious Composites after High-Temperature Exposure Materials science ; Composite material ; Magnesium phosphate ; Magnesium ; Potassium ; Phosphate ; Fracture (geology) ; Fiber ; Cementitious ; Metallurgy ; Cement ; Chemistry Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Bone Tissue Engineering Materials
Authors: Yuriy A. Abramov
Source: Crystal Growth & Design (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

A New Hybrid Virtual Screening Model for Predicting Solvate Formation Propensity in Pharmaceutical Crystallization Efficient solvent selection is essential for controlling crystalline solvate formation in pharmaceutical crystallization. A novel COSMO-RS+ML hybrid virtual solvent screening model has been developed to predict solvate propensity by integrating COSMO-RS amorphous-phase miscibility calculations with machine learning predictions of crystallinity contributions. Validated against ten reported studies involving prolific solvate-forming molecules, the model significantly outperformed the pure COSMO-RS method in ranking solvents by their likelihood of forming solvates. When combined with targeted experimental follow-up, this computational approach provides a powerful strategy to accelerate and derisk solvent selection in pharmaceutical crystallization development. Crystallization ; Miscibility ; Crystallinity ; Solvent ; Virtual screening ; Selection (genetic algorithm) ; Ranking (information retrieval) Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Crystallography and molecular interactions
Authors: Yuriy A. Abramov (1434262)
Source: Figshare (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

A New Hybrid VirtualScreening Model for PredictingSolvate Formation Propensity in Pharmaceutical Crystallization Efficient solvent selection is essential for controlling crystalline solvate formation in pharmaceutical crystallization. A novel COSMO-RS+ML hybrid virtual solvent screening model has been developed to predict solvate propensity by integrating COSMO-RS amorphous-phase miscibility calculations with machine learning predictions of crystallinity contributions. Validated against ten reported studies involving prolific solvate-forming molecules, the model significantly outperformed the pure COSMO-RS method in ranking solvents by their likelihood of forming solvates. When combined with targeted experimental follow-up, this computational approach provides a powerful strategy to accelerate and derisk solvent selection in pharmaceutical crystallization development. Crystallization ; Miscibility ; Crystallinity ; Selection (genetic algorithm) ; Solvent ; Virtual screening Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Crystallography and molecular interactions
Authors: Dale, Ashley S. | Li, Kangming | DeCost, Brian | Wan, Hao | Han, Yuchen | Fehlis, Yao | Hattrick-Simpers, Jason
Source: ArXiv.org (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active learning when using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network model architectures. We compare uncertainty estimates from ALIGNN deep ensembles to loss landscape uncertainty estimates obtained for solubility, bandgap, and formation energy prediction tasks. We then evaluate how the quality of the uncertainty estimate impacts an active learning campaign that seeks model generalization to out-of-distribution data. Uncertainty calibration methods were found to variably generalize from in-domain data to out-of-domain data. Furthermore, calibrated uncertainties were generally unsuccessful in reducing the amount of data required by a model to improve during an active learning campaign on out-of-distribution data when compared to random sampling and uncalibrated uncertainties. The impact of poor-quality uncertainty persists for random forest and eXtreme Gradient Boosting models trained on the same data for the same tasks, indicating that this is at least partially intrinsic to the data and not due to model capacity alone. Analysis of the target, in-distribution uncertainty, out-of-distribution uncertainty, and training residual distributions suggest that future work focus on understanding empirical uncertainties in the feature input space for cases where ensemble prediction variances do not accurately capture the missing information required for the model to generalize. Calibration ; Random forest ; Uncertainty quantification ; Artificial neural network ; Active learning (machine learning) ; Boosting (machine learning) ; Missing data ; Gradient boosting ; Uncertainty analysis ; Ensemble learning Machine Learning in Materials Science ; Machine Learning and Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Daniel Pasin
Source: Annales de Toxicologie Analytique (article, 2025-03-01)
Field: Chemistry — Spectroscopy

HighResNPS: A high-resolution mass spectrometry suspect screening tool for new psychoactive substances Psychoactive substance ; Mass spectrometry ; High resolution ; Suspect ; Chemistry ; Chromatography ; Medicine ; Psychology ; Psychiatry ; Criminology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Forensic Toxicology and Drug Analysis
Authors: Yanxia Liu
Source: IET conference proceedings. (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Study on optimization and real-time monitoring of extraction process parameters of spice essential oil based on deep neural network Due to its wide application in fields such as food, medicine, and cosmetics, spice essential oils require higher efficiency and stability in extraction processes. The traditional extraction process has problems such as low efficiency and unstable parameter control, while deep neural networks (DNN), especially long short-term memory networks (LSTM), have significant advantages in processing complex process and time series data. In this study, three spices, ginger, garlic and pepper, were taken as the objects, and the key process parameters affecting the extraction of essential oil were determined through experiments, including distillation temperature, time, pressure and material-liquid ratio, and these parameters were modeled and optimized by LSTM model. The results showed that the optimized process parameters significantly improved the output of essential oil: the output of essential oil of ginger increased from 12.5 ml/kg to 15.4 ml/kg (by 23.2%), that of garlic increased from 10.2 ml/kg to 12.0 ml/kg (by 17.6%), and that of pepper increased from 8.7 ml/kg to 10.4 ml/kg (by 19.5%). At the same time, the real-time monitoring system realizes the dynamic adjustment and stable operation of the extraction process through data acquisition, preprocessing, model prediction and feedback control modules. The prediction accuracy of LSTM model on the verification data set reaches 92%, showing good generalization ability. This study shows its great application potential in the field of spice essential oil extraction, which provides theoretical basis and technical support for the development of intelligent extraction technology in the future. Spice ; Process (computing) ; Artificial neural network ; Extraction (chemistry) ; Essential oil ; Distillation ; Generalization ; Key (lock) Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications
Authors: Alexander Aghili (22786399) | Andy Bruce (22786402) | Daniel Sabo (1984513) | Sanya Murdeshwar (22786405) | Kevin Bachelor (22786408) | Ionut Mistreanu (22786411) | Ashwin Lokapally (418113) | Razvan Marinescu (11113149)
Source: Figshare (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A StandardizedBenchmark for Machine-Learned MolecularDynamics Using Weighted Ensemble Sampling The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by inconsistent evaluation metrics, insufficient sampling of rare conformational states, and the absence of reproducible benchmarks. To address these challenges, we introduce a modular benchmarking framework that systematically evaluates protein MD methods using enhanced sampling analysis. Our approach uses weighted ensemble (WE) sampling via <i>The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis</i> (WESTPA), based on progress coordinates derived from <i>Time-lagged Independent Component Analysis</i> (TICA), enabling fast and efficient exploration of protein conformational space. The framework includes a flexible, lightweight propagator interface that supports arbitrary simulation engines, allowing both classical force fields and machine learning-based models. Additionally, the framework offers a comprehensive evaluation suite capable of computing more than 19 different metrics and visualizations across a variety of domains. We further contribute a data set of nine diverse proteins, ranging from 10 to 224 residues, that span a variety of folding complexities and topologies. Each protein has been extensively simulated at 300 K for one million MD steps per starting point (4 ns). To demonstrate the utility of our framework, we perform validation tests using classic MD simulations with implicit solvent and compare protein conformational sampling using a fully trained versus under-trained CGSchNet model. By standardizing evaluation protocols and enabling direct, reproducible comparisons across MD approaches, our open-source platform lays the groundwork for consistent, rigorous benchmarking across the molecular simulation community. Benchmark (surveying) ; Benchmarking ; Molecular dynamics ; Sampling (signal processing) ; Modular design ; Set (abstract data type) ; Protein structure prediction ; Variety (cybernetics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yifan Li | Bingjia Yang | Chunyi Zhang | Axel Gomez | Pinchen Xie | Yuli Chen | Pablo M. Piaggi | Roberto Car
Source: ArXiv.org (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Assessment of First-Principles Methods in Modeling the Melting Properties of Water First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems. Liquid water ; Range (aeronautics) ; Density functional theory ; Molecular dynamics ; Melting temperature ; Path integral formulation ; Experimental data ; Water model Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Alexander Aghili | A. M. Bruce | Dubravko Sabo | Sanya Murdeshwar | Kevin Bachelor | Ionut Mistreanu | Ashwin Lokapally | Razvan Marinescu
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

A Standardized Benchmark for Machine-Learned Molecular Dynamics using Weighted Ensemble Sampling The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by inconsistent evaluation metrics, insufficient sampling of rare conformational states, and the absence of reproducible benchmarks. To address these challenges, we introduce a modular benchmarking framework that systematically evaluates protein MD methods using enhanced sampling analysis. Our approach uses weighted ensemble (WE) sampling via The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis (WESTPA), based on progress coordinates derived from Time-lagged Independent Component Analysis (TICA), enabling fast and efficient exploration of protein conformational space. The framework includes a flexible, lightweight propagator interface that supports arbitrary simulation engines, allowing both classical force fields and machine learning-based models. Additionally, the framework offers a comprehensive evaluation suite capable of computing more than 19 different metrics and visualizations across a variety of domains. We further contribute a dataset of nine diverse proteins, ranging from 10 to 224 residues, that span a variety of folding complexities and topologies. Each protein has been extensively simulated at 300K for one million MD steps per starting point (4 ns). To demonstrate the utility of our framework, we perform validation tests using classic MD simulations with implicit solvent and compare protein conformational sampling using a fully trained versus under-trained CGSchNet model. By standardizing evaluation protocols and enabling direct, reproducible comparisons across MD approaches, our open-source platform lays the groundwork for consistent, rigorous benchmarking across the molecular simulation community. Benchmark (surveying) ; Benchmarking ; Sampling (signal processing) ; Molecular dynamics ; Modular design ; Variety (cybernetics) ; Protein structure prediction ; Suite Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Denan Li | Jiyuan Yang | Xiangkai Chen | Lintao Yu | Shi Liu
Source: The Journal of Physical Chemistry C (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics? Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired by foundational large language models, recent years have seen considerable progress in developing foundational atomistic models, sometimes referred to as universal force fields, designed to cover most elements in the periodic table. This perspective adopts a practitioner’s viewpoint to ask a critical question: Are these foundational atomistic models reliable for one of their most compelling applications, in particular simulating finite-temperature dynamics? Instead of a broad benchmark, we use the canonical ferroelectric-paraelectric phase transition in PbTiO3 as a focused case study to evaluate prominent foundational atomistic models. Our findings suggest a potential disconnect between static accuracy and dynamic reliability. While 0 K properties are often well-reproduced, we observed that the models can struggle to consistently capture the correct phase transition, sometimes exhibiting simulation instabilities. We believe these challenges may stem from inherent biases in training data and a limited description of anharmonicity. These observed shortcomings, though demonstrated on a single system, appear to point to broader, systemic challenges that can be addressed with targeted fine-tuning. This perspective serves not to rank models, but to initiate a crucial discussion on the practical readiness of foundational atomistic models and to explore future directions for their improvement. Perspective (graphical) ; Point (geometry) ; Cover (algebra) ; Phase (matter) ; Field (mathematics) ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems