jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin Airas | Bin Zhang
Source: PubMed (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model. Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools. Protein folding ; Folding (DSP implementation) ; Solvent models ; Process (computing) ; Artificial neural network ; Protein structure prediction ; Molecular dynamics ; Computational model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Qing Wu | Liyan Jiang | Si Huang | Aokui Sun | Yong Xia
Source: Computational and Theoretical Chemistry (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Interpretable prediction model for organic solar cell performance: A principal component analysis-based investigation of feature importance Principal component analysis ; Feature (linguistics) ; Component (thermodynamics) ; Pattern recognition (psychology) ; Organic solar cell Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Chemical and Physical Properties of Materials
Authors: Zamar Daka | Yuqi Zhang | Jin Li | Liying Yao | Chenxi Luo | Meng Tian | Xi Xia | Guowen Qin | Rong Jiang | Min Song | Shunli Ji
Source: Chemical Engineering Journal (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Design of green multifunctional mesoporous nanosheets for trace biomarker detection, AI-enhanced pollutant discrimination, and synergistic catalytic degradation Catalysis ; Adsorption ; Phloroglucinol ; Mesoporous material ; Degradation (telecommunications) ; Ligand (biochemistry) ; Pollutant ; Electron transfer Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yunlong Zhu | Junzhi Cui | Jingli Ren
Source: iScience (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

AI-enabled prediction and transformation analysis of phases in multi-component nitride thin films and coatings This study investigates the relationship between composition, descriptors, and phase structure in nitride thin films and coatings using machine learning. A dedicated dataset of 79 systems, comprising 627 individual records, is analyzed using features derived from elemental properties and thermodynamic parameters. The Gradient Boosting model performs excellently on the 8 key features, achieving 0.94 mean accuracy in phase classification. Critical descriptors such as <i>D</i> <sub>CR</sub> and <i>C</i> <sub>N</sub> are identified as dominant predictors through feature importance analysis. Interpretable models reveal a pronounced preference for FCC phase formation at higher values of <i>D</i> <sub>CR</sub> (>0.33) and <i>C</i> <sub>N</sub> (>0.35). The study further proposes data inclusion principles to address the limitations of existing models under limited data conditions. By reformulating the classification task as a regression problem via a sigmoid function, an explicit predictive expression based on the key descriptors is derived. These findings highlight the value of diverse compositional databases and interpretable machine learning for understanding complex material systems and guiding the design of materials with stable structures. Sigmoid function ; Thin film ; Gradient boosting ; Boosting (machine learning) ; Nitride ; Feature (linguistics) ; Transformation (genetics) ; Support vector machine ; Key (lock) Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; X-ray Diffraction in Crystallography
Authors: Pablo M. Granitto | Maria Mazzucotelli | Michele Pedrotti | Iuliia Khomenko | Franco Biasioli
Source: Chemometrics and Intelligent Laboratory Systems (article, 2026-03-07)
Field: Chemistry — Spectroscopy

Gradient boosting applied to PTR-ToF-MS analysis of agrifood samples Rapid and non-invasive analysis of food products is essential in the agrifood sector for ensuring quality, safety and authenticity. In this context, Volatile Organic Compound (VOC) analysis plays a key role, and direct injection mass spectrometry, Proton Transfer Reaction Mass Spectrometry (PTR-ToF-MS) in particular, offers an optimal tool due to its speed and high sensitivity. The resulting datasets from these analyses are typically modeled using classification, regression, and peak selection methods. In these tasks, gradient boosting methods, and XGBoost in particular, have demonstrated outstanding performance, often surpassing classical machine learning techniques and deep learning approaches. In this work, we investigate the applicability of XGBoost to PTR-ToF-MS datasets of food VOCs in detail. We show that XGBoost requires careful (and time-consuming) optimization to achieve competitive results in this specific domain. Our results indicate that the performance of XGBoost on food products is better in classification than in other analysis tasks, and is comparable on regression and peak selection to that of other state-of-the-art methods, when all methods are appropriately tuned. Given the inherent difficulty of modeling small and noisy real world datasets, our work highlights the importance of carefully evaluating methods within each specific domain, rather than extrapolating their performance as a given. • We evaluate the use of gradient boosting, in particular XGBoost, in PTR-Tof-MS datasets from the agrifood domain. • We include classification, regression and peak selection problems in the evaluation. • We analyze the tuning procedure for XGBoost. • We compare the performance with methods often used for our data. • XGBoost shows good performance for classification problems • For regression and peak selection the results are similar to other methods. Boosting (machine learning) ; Gradient boosting ; Pattern recognition (psychology) ; Gradient method Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical chemistry methods development
Authors: Kai-Yin Wu | Ziyang Feng | Zhi Li | Hazar Guesmi | L. Piccolo | Li-Xin Chen | Chao Su | Beibei Xiao
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

DFT insights into the stability of single-metal-atoms on Mo-based <i>o</i> -MXenes driven by the ligand effect MXenes, a novel class of two-dimensional transition metal carbides, nitrides or carbonitrides, have emerged as promising supports for single-atom catalysts owing to their tunable structural and electronic properties. Using density functional theory calculations, we investigate ordered Mo-based bimetallic MXenes without surface functional groups ( and <i>o</i>-Mo<sub>2</sub>M″C<sub>2</sub>, where M″ = Ti, Zr, Hf, V, Nb and Ta) as materials to stabilize platinum-group-metal (PGM) single atoms at Mo vacancies. Compared with the corresponding Mo-only MXenes, introducing a second metal component, M″, strengthens PGM anchoring and increases sintering resistance, which we attributed to a ligand effect that enhances PGM-C covalency and reorganizes the PGM <i>d</i> states. This stabilization is further captured by a combined electronic descriptor <i>δ</i> = <i>ε</i><sub>p</sub> - <i>ε</i><sub>d</sub>. Here, <i>δ</i> is defined as the difference between the p band center (<i>ε</i><sub>p</sub>) of the C atoms adjacent to the PGM and the d band center (<i>ε</i><sub>d</sub>) of the PGM atom. This descriptor <i>δ</i> shows a strong correlation with the binding strength between PGM and MXenes. To assess the changes of PGM reactivity raised by different MXenes, we use CO, CH<sub>3</sub> and NH<sub>3</sub> adsorption strength, highlighting a stability-reactivity trend. An XGBoost-based machine learning analysis further revealed that adsorption energies are governed by multiple electronic descriptors (beyond <i>ε</i><sub>d</sub>), with the average electronegativity (<i>χ</i><sub>avg</sub>) of the PGM-MXene system contributing to the observed trends in adsorption energies. This study provides a new atomic-level understanding of the rational design principle for stabilizing atomically dispersed SACs on MXenes and provides guidelines for the experimental synthesis. MXenes ; Bimetallic strip ; Electronegativity ; Density functional theory ; Adsorption ; Electronic structure ; Ligand (biochemistry) ; Stability (learning theory) ; Reactivity (psychology) MXene and MAX Phase Materials ; 2D Materials and Applications ; Catalysis and Hydrodesulfurization Studies
Authors: S. Chakraborty | Andrew T. Krasley | Colby Smith | Abraham G. Beyene | Lela Vuković
Source: Nano Letters (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Analyte-Induced Fluorescence Perturbations in DNA-Functionalized Carbon Nanotubes Single-walled carbon nanotubes (SWCNTs) functionalized with single-stranded DNAs can function as near-infrared nanosensors for molecular analytes. However, predicting which analytes elicit strong optical responses for specific nanosensors remains challenging. We developed machine learning (ML) models to predict analyte-induced fluorescence changes in a DNA-SWCNT dopamine nanosensor. Using a data set of 63 small molecules sampling chemical space around dopamine, we encoded analytes with RDKit fingerprints, with or without HOMO and LUMO energies, and applied principal component analysis to identify structural motifs associated with optical response strength. We trained support vector regression and classification models using two strategies: ensembles of 200 models and cross-validation. Regression models achieved mean <i>R</i><sup>2</sup> values of 0.2-0.4, with cross-validation outperforming ensembles, while classifiers reached mean F1 scores of ∼0.8. Cross-validation performed best for predictions on a blind set of 21 molecules. These findings show that ML can capture structure-response patterns in modest data sets and guide in silico DNA-SWCNT nanosensor design. Nanosensor ; Analyte ; Support vector machine ; Principal component analysis ; Carbon nanotube ; Pattern recognition (psychology) ; Data set ; In silico ; Chemical space Carbon Nanotubes in Composites ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Manuel Ballester | Santiago López-Tapia | Seth Gossage | Patrick Koller | Philipp M. Srivastava | Ugur Demir | Yongseok Jo | E. Márquez | Christoph Wuersch | Souvik Chakraborty | Vicky Kalogera | Aggelos K. Katsaggelos
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Materials Science — Materials Chemistry

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as \texttt{MESA} (Modules for Experiments in Stellar Astrophysics), which employ adaptive finite-difference methods, can become computationally expensive and challenging to scale for large stellar population synthesis ($&gt;10^9$ stars). In this work, we present an self-supervised physics-informed neural network (PINN) framework that provides a mesh-free and fully differentiable approach to solving the stellar structure equations under hydrostatic and thermal equilibrium. The model takes as input the stellar boundary conditions (at the center and surface) together with the chemical composition, and learns continuous radial profiles for mass $M_r(r)$, pressure $P(r)$, density $ρ(r)$, temperature $T(r)$, and luminosity $L_r(r)$ by enforcing the governing structure equations through physics-based loss terms. To incorporate realistic microphysics, we introduce auxiliary neural networks that approximate the equation of state and opacity tables as smooth, differentiable functions of the local thermodynamic state. These surrogates replace traditional tabulated inputs and enable end-to-end training. Once trained for a given star, the model produces continuous solutions across the entire radial domain without requiring discretization or interpolation. Validation against benchmark \texttt{MESA} models across a range of stellar masses yields a Mean Relative Absolute Error of $3.06\%$ and an average $R^2$ score of $99.98\%$. To our knowledge, this is the first demonstration that the stellar structure equations can be solved in a fully self-supervised and data-free fashion employing PINNs. This work establishes a foundation for scalable, physics-informed emulation of stellar interiors and opens the door to future extensions toward time-dependent stellar evolution. Stellar structure ; Artificial neural network ; Hydrostatic equilibrium ; Discretization ; Differentiable function ; Benchmark (surveying) ; Boundary value problem ; Equation of state ; Boundary (topology) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Manuel Ballester | Santiago López-Tapia | Seth Gossage | Patrick Koller | Philipp M. Srivastava | Ugur Demir | Yongseok Jo | E. Márquez | Christoph Wuersch | Souvik Chakraborty | Vicky Kalogera | Aggelos K. Katsaggelos
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as \texttt{MESA} (Modules for Experiments in Stellar Astrophysics), which employ adaptive finite-difference methods, can become computationally expensive and challenging to scale for large stellar population synthesis ($>10^9$ stars). In this work, we present an self-supervised physics-informed neural network (PINN) framework that provides a mesh-free and fully differentiable approach to solving the stellar structure equations under hydrostatic and thermal equilibrium. The model takes as input the stellar boundary conditions (at the center and surface) together with the chemical composition, and learns continuous radial profiles for mass $M_r(r)$, pressure $P(r)$, density $ρ(r)$, temperature $T(r)$, and luminosity $L_r(r)$ by enforcing the governing structure equations through physics-based loss terms. To incorporate realistic microphysics, we introduce auxiliary neural networks that approximate the equation of state and opacity tables as smooth, differentiable functions of the local thermodynamic state. These surrogates replace traditional tabulated inputs and enable end-to-end training. Once trained for a given star, the model produces continuous solutions across the entire radial domain without requiring discretization or interpolation. Validation against benchmark \texttt{MESA} models across a range of stellar masses yields a Mean Relative Absolute Error of $3.06\%$ and an average $R^2$ score of $99.98\%$. To our knowledge, this is the first demonstration that the stellar structure equations can be solved in a fully self-supervised and data-free fashion employing PINNs. This work establishes a foundation for scalable, physics-informed emulation of stellar interiors and opens the door to future extensions toward time-dependent stellar evolution. Stellar structure ; Artificial neural network ; Hydrostatic equilibrium ; Discretization ; Differentiable function ; Benchmark (surveying) ; Boundary value problem ; Equation of state ; Boundary (topology) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Haili Jia | Yiming Chen | Gi‐Hyeok Lee | Jacob Smith | Miaofang Chi | Wanli Yang | Maria K. Y. Chan
Source: ACS Nano (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. In this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures. Characterization (materials science) ; Lithium (medication) ; Ab initio ; Spectroscopy ; Local structure ; X-ray absorption spectroscopy ; Ab initio quantum chemistry methods ; Electronic structure ; Extended X-ray absorption fine structure Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Amir Kordijazi
Source:  (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Deconstructing Triboinformatics: A Conceptual Framework for the Intersection of Machine Learning and Tribology Tribology has traditionally functioned as an empirical discipline, heavily reliant on physical experimentation rather than unified first principles. To address the resulting epistemic bottlenecks, the paradigm of "Triboinformatics" has emerged, leveraging machine learning (ML) to extract structured knowledge from high-entropy, multiscale datasets. Moving beyond conventional systematic reviews that primarily catalog bibliographic trends, this paper presents a conceptual framework that deconstructs triboinformatics into its fundamental building blocks. We identify three major pillars: the Machine Learning Component (the data-driven computational engine encompassing data sources, algorithms, and task modes), the Physics Component (the domain context defined by system scale and physical laws), and the dynamic Interface between them. This paper critically analyzes this bidirectional Interface, conceptualizing two primary pathways of innovation. Direction A explores how ML algorithms drive epistemic gain through surrogate modeling, inverse materials design, and explainable AI. Conversely, Direction B examines how the strict laws of tribology compel methodological innovation, resulting in Physics-Informed Machine Learning (PIML) architectures that constrain algorithms within thermodynamically and mechanically plausible boundaries. Ultimately, this framework demonstrates that the future of triboinformatics lies not in replacing physical experiments with black-box algorithms, but in their symbiotic integration, highlighting the transition from statistical correlation to causal discovery as the next critical frontier. Intersection (aeronautics) ; Context (archaeology) ; Component (thermodynamics) ; Domain (mathematical analysis) ; Interface (matter) ; Conceptual framework ; Relation (database) ; Task (project management) Machine Learning in Materials Science ; Lubricants and Their Additives ; Force Microscopy Techniques and Applications
Authors: Chenglin Wu | Jiaoli Li | Matthew G. Boebinger | Kun Luo | Yanxiao Li | Wenpei Gao | Qi An
Source: Research Square (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

Autonomous Atomic-Scale Self-healing in Two-Dimensional MXenes via Diffusion-Driven Lattice Reconstruction MXenes ; Nanopore ; Titanium carbide ; MAX phases ; Transmission electron microscopy ; Lattice (music) ; Kinetics ; Carbide MXene and MAX Phase Materials ; Micro and Nano Robotics ; 2D Materials and Applications
Authors: Songshan Dai | Wenhao Wang | Jun Zhu
Source: Organometallics (article, 2026-01-20)
Field: Chemistry — Organic Chemistry

Charge-Promoted Adaptive Aromaticity in Metallabenzenes: A Combined DFT and Machine Learning Study In accordance with the constraints by Hückel’s and Baird’s rules, species generally exhibit aromaticity in one state (the lowest singlet state S0 or the lowest triplet state T1). Consequently, species with adaptive aromaticity (being aromatic in both the S0 and T1 states) are particularly rare. In this study, density functional theory (DFT) was employed to investigate adaptive aromaticity in 14e–, 16e– and 18e– metallabenzenes. Based on bond length, fuzzy bond order (FBO), multicenter index (MCI), delocalized bond electron density (EDDB) analyses, magnetically induced current-density (MICD) analysis, and isomerization stabilization energy (ISE), 14e– ruthenabenzenes are found to possess adaptive aromaticity. A reduction in the delocalization of the spins in the six-membered ring of metallabenzenes will facilitate the maintenance of their T1 aromaticity. The ligand’s effect on adaptive aromaticity in 14e– ruthenabenzenes was conducted by principal component analysis (PCA), one of the most commonly used unsupervised machine learning algorithms, which suggests that electron-deficient ligands are detrimental to adaptive aromaticity. A supervised machine learning method, multiple linear regression (MLR), was employed to develop a quantitative equation for predicting aromaticity in the T1 state. This study demonstrates the first example of a metallabenzene with adaptive aromaticity, which is a valuable contribution to the field of aromatic chemistry. Aromaticity ; Delocalized electron ; Density functional theory ; Singlet state ; Factorization ; Ring (chemistry) ; Isomerization ; Aromatic amino acids Synthesis and Properties of Aromatic Compounds ; Graph theory and applications ; Magnetism in coordination complexes