jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zai-Hui Wu | Ran Gu
Source: ChemRxiv (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Vacancy-Aware Graph Neural Networks for Predicting Oxygen Evolution Reaction Activity in High-Entropy Layered Double Hydroxides Vacancy defect ; Artificial neural network ; Oxygen evolution ; Cooperativity ; Graph ; Node (physics) ; Enhanced Data Rates for GSM Evolution ; Layered double hydroxides Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Govindarajan Padmanaban | Princy Suganthi Bai S
Source:  (article, 2025-03-04)
Field: Chemistry — Analytical Chemistry

Integration of Chemometric Analysis and Machine Learning for Predictive Classification of Raisin Quality The integration of chemometric analysis and machine learning techniques to enhance the predictive classification of raisin quality, a vital task for agricultural and food processing industries. The study employs chemometrics to analyze physical and chemical characteristics, selecting critical features such as color, texture, and chemical composition to reduce data redundancy and optimize model accuracy. Various machine learning algorithms, including Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are applied and compared to predict raisin quality. By merging chemometric approaches with advanced machine learning methods, the proposed framework demonstrates improved classification accuracy over traditional techniques, showcasing its potential for real-world applications in quality control and automated sorting systems. Future efforts will aim to expand the dataset, refine model performance, and explore new feature selection techniques to further boost classification accuracy. Computer science ; Artificial intelligence ; Machine learning ; Quality (philosophy) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses
Authors: Dongdong Wang | Yüting Xiong | Qianying Sheng | Yizhong Huang | Guangyan Qing
Source: Chemistry - An Asian Journal (review, 2025-04-09)
Field: Chemistry — Spectroscopy

Theoretical Calculations in Separation Science for Analytical Chemistry: Applications and Insights Separation and enrichment are critical steps in analytical detection, necessitating advanced materials with high selectivity and adsorption capacity for target compounds. In order to improve separation efficiency and selectivity, computational simulation could elucidate interaction mechanisms and analyze potential adsorption/desorption processes, providing a theoretical foundation for the optimization and design of separation materials. Recently, computational simulation has become an indispensable and crucial mean in separation science for analytical chemistry. Using various simulation software, researchers could investigate the structures, properties, and performance of separation materials at multiple levels and scales. In this review, we summarize the applications of computational simulations in the field of separation science, focusing on the separation of polar molecules, geometric isomers, enantiomer compounds, and post-translationally modified peptides. These calculation methods include quantum chemistry, molecular docking, molecular dynamics simulations, high-throughput screening, and machine learning. Finally, we discuss the current challenges and potential breakthroughs in computational simulation, aiming to offer valuable insights for researchers dedicated to computational simulation, material development, and separation applications. Separation (statistics) ; Molecular dynamics ; Computer science ; Separation of concerns ; Separation method ; Chemistry ; Adsorption ; Computational chemistry ; Biochemical engineering ; Nanotechnology ; Materials science ; Software ; Machine learning ; Physical chemistry ; Chromatography ; Engineering Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Zhiguo Hao | Zhipeng Li | Xing Li | Lijun Li | Ridong He | Yanfang Gao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-physics Coupling Model for Molten Hydroxide Direct Carbon Fuel Cells: Performance Prediction and Validation under Dual Gradient Distribution of Concentration and Temperature Isothermal process ; Anode ; Electrolyte ; Power density ; Multiphysics ; Cathode ; Coupling (piping) ; Voltage ; Electrochemistry Advancements in Solid Oxide Fuel Cells ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Nikhil
K. Barua (18965429) | Holger Kleinke (1680766)
Source: Figshare (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Machine LearningPredictions of Thermopower for ThermoelectricMaterial Screening Machine or deep learning studies to predict the electronic properties, particularly the Seebeck coefficient, of thermoelectric (TE) materials remain relatively few. Considering the benefits and limitations of these studies, we developed a data-driven, structure-independent machine learning script. The script can produce Seebeck coefficient predictions by using a terminal-executed batch file. Our model within the script was trained with the latest version of the Starry data set, with experimentally derived thermoelectric data. The model was then tested across multiple sets of test sets. Postprediction, we use SHapley Additive exPlanations to analyze the interpretability of our model with chemical insights into features influencing our models’ decisions. Interpretability ; Thermoelectric effect ; Seebeck coefficient ; Thermoelectric materials ; Power (physics) ; Test data ; Experimental data Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; CO2 Reduction Techniques and Catalysts
Authors: Bijan Paul (22084331) | Sahand Serajian (19928783) | Hongzhi Guo (123522) | Mona Bavarian (6083588)
Source: Figshare (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

LiCondAI: A MachineLearning Model for PredictingLithium-Ion Conductivity in Solid Electrolytes Significant momentum is driving advancements in solid-state battery (SSB) technology, where the choice of electrolyte plays a key role in determining cell performance. In this study, an advanced machine learning ensemble model, called LiCondAI (lithium-ion conductivity using Artificial Intelligence), was developed to predict the ionic conductivity (IC) of solid electrolytes (SEs) in solid-state batteries (SSBs). LiCondAI combines the predictive capabilities of three modelsXGBoost, Gradient Boosting, and Random Forestusing a stacking regressor mechanism, achieving 97% accuracy in predicting IC. The proposed model, LiCondAI, accurately predicts IC in SEs for lithium-ion and lithium metal SSBs. LiCondAI is a useful platform for examining the behavior of SEs in SSBs and serves as a tool for guiding the design of new electrolytes for SSBs. Conductivity ; Electrolyte ; Ionic conductivity ; Stacking ; Key (lock) ; Momentum (technical analysis) ; Ensemble learning ; Artificial neural network ; Electrical conductor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Daniel S. King | Daniel Grzenda | Ruijie Zhu | Nathaniel Hudson | Ian Foster | Bingqing Cheng | Laura Gagliardi
Source: ChemRxiv (preprint, 2025-07-15)
Field: Chemistry — Physical and Theoretical Chemistry

Cartesian Equivariant Representations for Learning and Understanding Molecular Orbitals Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree-Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONet), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine learned potentials. We find that CEONet performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONet representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONet to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONet to infer multireference character via the orbital entropy paves the way towards the machine-learned selection of active spaces. Equivariant map ; Atomic orbital ; Physics ; Molecular orbital ; Computer science ; Psychology ; Mathematics ; Pure mathematics ; Quantum mechanics ; Molecule ; Electron History and advancements in chemistry ; Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Xinyu Mao | Qi Xie
Source: Applied Optics (article, 2025-07-28)
Field: Chemistry — Spectroscopy

Neural-network-based multi-spectral thermometry and emissivity reconstruction in cavity high-temperature environments This study presents a neural-network-assisted framework for accurate radiation thermometry in high-temperature cavities, overcoming challenges from unknown emissivity and multi-reflection effects. The method combines Monte Carlo ray-tracing with deep learning through: (1) physics-informed training data including diffuse/specular reflections, (2) alternating neural networks for decoupled temperature/emissivity prediction, and (3) full multi-reflection modeling. Validated with zirconia in a graphite cavity within the 1273-1673 K temperature range and 2-16 µm spectral range, it achieves 0.7% temperature error (9 K, compared to real temperature) and 0.05-0.1 emissivity error in 2-16 µm, outperforming first-order methods (neglecting multiple reflections) by 5%-27% (peak at 2 µm) in emissivity reconstruction. The framework maintains <1<i>%</i> error, with only 10 spectral channels and tolerates 1% intensity noise (<1.8<i>%</i> variation), enabling reliable thermometry in low-emissivity materials like alloys and ceramics where conventional methods may fail. Emissivity ; Optics ; Materials science ; Artificial neural network ; Remote sensing ; Computer science ; Physics ; Artificial intelligence ; Geology Spectroscopy and Laser Applications ; Heat Transfer and Boiling Studies ; Calibration and Measurement Techniques
Authors: Mahmoud Shaaban | Aravind Krishnamoorthy | S. El-Borgi
Source: Research Square (preprint, 2025-02-17)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning for Experimental Design of Ultrafast Electron Diffraction Ultrafast electron diffraction ; Ultrashort pulse ; Diffraction ; Electron diffraction ; Electron ; Computer science ; Materials science ; Physics ; Optics ; Nuclear physics ; Laser Electron and X-Ray Spectroscopy Techniques ; Photonic Crystals and Applications ; Crystallography and Radiation Phenomena
Authors: T. SEO | Donghun Kim | Shinwon Ham | You Kyoung Chung | Inho Jeong | Joonsuk Huh | Hyunwoo Kim | Do Hyun Ryu
Source: ChemRxiv (preprint, 2025-04-23)
Field: Materials Science — Materials Chemistry

Generality-driven optimization of enantio- and regioselectivecatalysis by high-throughput experimentation and machinelearning The longstanding quest for achieving substrate generality stems from the unpredictability of single-model optimization. Leveraging the potential of high-throughput experimentation (HTE), we present a practical multi-substrate screening strategy that enables the general asymmetric mono-reduction of unsymmetrical 1,2-dicarbonyl compounds. This study combines quantitative 1H NMR with simultaneous chiral analysis facilitated by 19F NMR for pooled samples without any purification steps. Remarkably, this approach accelerated the workflow by eight times compared to the traditional method, enabling robust screening of 31 chiral oxazaborolidinium ion (COBI) variants across eight 1,2-dicarbonyl compounds. Extensive substrate screening successfully addressed even the most challenging substrates with subtle steric differentiation, such as Et/Me. Moreover, the HTE data were utilized in a machine learning (ML) model with descriptors based on the CGR (Condensed Graphs of Reaction), which identified catalysts with enhanced reactivity for previously unexplored substrates, independent of quantum chemical calculations. In this approach, the ARMS (Automated Reaction Mapping for various Substituents) system was introduced to streamline SMILES (Simplified Molecular Input Line Entry System) preprocessing for multi-substrate reactions, improving data efficiency and enabling CGR generation. The resulting chiral α-silyloxy ketones, obtained in excellent yields (up to >99%) with excellent selectivities (up to >99% ee, up to >20:1 r.r.), were then easily transformed to form various natural products and pharmaceuticals, such as (S)-bupropion—originally sold as a racemic mixture but now available in its chiral form. Generality ; Regioselectivity ; Throughput ; Computer science ; Artificial intelligence ; Catalysis ; Chemistry ; Psychology ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Yifan Sun | Zhi Li | Tetsuya Imamura | Yuji Ohishi | Chris Wolverton | Ken Kurosaki
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Lattice-to-total thermal conductivity ratio: a phonon-glass electron-crystal descriptor for data-driven thermoelectric design Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds with low thermal conductivity $κ$. Using a curated dataset of 71,913 entries, we show that high-$ZT$ materials reside not only in the low-$κ$ regime but also cluster near a lattice-to-total thermal conductivity ratio ($κ_\mathrm{L}/κ$) of approximately 0.5, consistent with the phonon-glass electron-crystal design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both $κ$ and $κ_\mathrm{L}/κ$ for screening and guiding the optimization of TE materials. Among 104,567 compounds screened, our models identify 2,522 ultralow-$κ$ candidates. Follow-up case studies demonstrate that this framework can reliably provide optimization strategies by suggesting new dopants and alloys that shift pristine materials toward the $κ_\mathrm{L}/κ$ approaching 0.5 regime. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework effectively bridges the critical gap between materials discovery and performance enhancement. Thermal conductivity ; Thermoelectric materials ; Dopant ; Thermoelectric effect ; Thermal ; Lattice (music) ; Cluster (spacecraft) ; Construct (python library) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yingkai Liu
Source: ChemRxiv (preprint, 2025-10-17)
Field: Materials Science — Materials Chemistry

Causal Feature Selection for Materials Optimization: Identifying Causal Process Parameters via Machine Learning and High-Dimensional Hypothesis Testing Causation ; Process (computing) ; False discovery rate ; Feature (linguistics) ; Causal model ; Feature selection ; Statistical hypothesis testing ; Control (management) ; Path (computing) Machine Learning in Materials Science
Authors: Luqi Dong | Shuxiang Yang | Su-Huai Wei | Yunhao Lu
Source: Physical Review Letters (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Variational Machine Learning Model for Electronic Structure Optimization via the Density Matrix We present a novel approach that combines machine learning with direct variational energy optimization via the density matrix to solve the Kohn-Sham equation in density functional theory. Instead of relying on the conventional self-consistent field method, our approach directly optimizes the ground state by predicting the density matrix using a neural network, thereby bypassing Hamiltonian matrix diagonalization. Our model employs equivariant neural networks to generate a physically constrained density matrix, enabling stable and efficient energy minimization. This method integrates the construction of training sets directly into the model training process and achieves high accuracy in predicting ground-state properties across various molecular and extended systems, thereby establishing a powerful machine learning paradigm for electronic structure optimization and paving the way for large-scale quantum simulations. Artificial neural network ; Hamiltonian (control theory) ; Electronic structure ; Density matrix ; Ground state ; Matrix (chemical analysis) ; Field (mathematics) ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Moucun Yuan | Zihan Zou | Wei Hu
Source: ArXiv.org (preprint, 2025-01-31)
Field: Materials Science — Materials Chemistry

QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules Developing machine learning protocols for molecular simulations requires comprehensive and efficient datasets. Here we introduce the QMe14S dataset, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, Br) and 47 functional groups. Using density functional theory at the B3LYP/TZVP level, we optimized the geometries and calculated properties including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian. At the same level, we obtained the harmonic IR, Raman and NMR spectra. Furthermore, we conducted ab initio molecular dynamics simulations to generate dynamic configurations and extract nonequilibrium properties, including energy, forces, and Hessians. By leveraging our E(3)-equivariant message-passing neural network (DetaNet), we demonstrated that models trained on QMe14S outperform those trained on the previously developed QM9S dataset in simulating molecular spectra. The QMe14S dataset thus serves as a comprehensive benchmark for molecular simulations, offering valuable insights into structure-property relationships. Organic molecules ; Molecule ; Computer science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; History and advancements in chemistry
Authors: Itsuki Yoshikawa (15186374) | Omid Aghababaei Tafreshi (17182032) | Yu-Chen Sun (721273) | Naomi Matsuura (1729336) | Ken-Ichiro Sotowa (3192819) | Hani E. Naguib (4820577)
Source: Figshare (article, 2025-05-23)
Field: Chemistry — Spectroscopy

Data-Driven Discovery of Multifunctional Organic PolymerAerogels by Machine Learning-Assisted Large-Scale Property Screening Polyimide (PI) aerogels are next-generation thermal insulation materials. Due to their intrinsic nanoporous structures, they are ultralow density and have superior thermal stability, making them important materials in fields such as aeronautics, thermal management, energy storage, and life sciences. The material properties and performance of PI aerogels are strongly correlated to the types of monomers and cross-linking agents. However, the time-consuming and costly synthesis process poses challenges in exploring new formulations of multifunctional PI aerogels with optimized properties. To discover new aerogel formulations, this research utilizes different machine learning methods to model the relationship between pristine monomers, processing conditions, and the properties of cross-linked aerogels. In addition, we have utilized machine learning to discover and predict new chemical structures via large-scale property screening. By introducing the Morgan fingerprint method, the chemical structure, including the hybrid monomer backbone, was effectively used as input for machine learning. Random forest and Gaussian process regression models were trained to estimate the density, compression modulus, and decomposition temperature. Over 1,600,000 novel PI aerogel structures were generated and screened using the trained machine learning models. Consequently, several structural candidates were identified as multifunctional PI aerogels with optimized properties and will have superior performance to those of previously reported aerogels. Aerogel ; Nanoporous ; Polyimide ; Monomer ; Process (computing) ; Polymer ; Thermal ; Property (philosophy) Aerogels and thermal insulation ; Polymer composites and self-healing ; Hydrogels: synthesis, properties, applications
Authors: Joris Meurs (5173031) | Aishah Nasir (7364402) | Grazziela P. Figueredo (9611043) | Laurence Burroughs (2230228) | Salah A. Abdelrazig (21516185) | Chris Denning (3817858) | David A. Winkler (220462) | David A. Barrett (50012) | Dong-Hyun Kim (117030) | Morgan R. Alexander (1340226)
Source: Figshare (article, 2025-06-10)
Field: Materials Science — Surfaces, Coatings and Films

High-ThroughputAnalysis of Protein Adsorption toa Large Library of Polymers Using Liquid Extraction Surface Analysis–TandemMass Spectrometry (LESA-MS/MS) Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachment, control immune cell attachment and phenotype, or direct stem cell fate. In-vitro preadsorption of proteins from culture medium plays a pivotal role in controlling cell response. However, there is a paucity of studies on the screening of protein adsorption into material libraries. Here, we show how quantitative analysis of protein adsorption on a 208-member polymer microarray can be achieved using liquid extraction surface analysis, combined with an adaptation of the droplet microarray (DMA) approach and tandem mass spectrometry (LESA-MS/MS) for protein identification. This study uses a fully defined cell culture medium containing only four proteins (Essential 8) to demonstrate the feasibility of the analysis approach. Our findings show that we can generate quantitative and predictive machine learning models of protein adsorption that elucidate key polymer features that describe the relationship between surface chemistry and protein adsorption. This information is of use for the rational design of new materials with bespoke protein attachment properties for biomaterials, medical devices, or in vitro compound screening. Polymer ; Protein adsorption ; Adsorption ; Bespoke ; Mass spectrometry ; Extraction (chemistry) ; Tandem mass spectrometry ; Protein microarray ; Protein purification Polymer Surface Interaction Studies ; Advanced Biosensing Techniques and Applications ; Collagen: Extraction and Characterization