jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Charlie Adams (22692200) | Joshua T. Horton (10686953) | Lily Wang (83905) | Simon Boothroyd (11468131) | David L. Mobley (1323615) | David W. Wright (361828) | Daniel J. Cole (213778)
Source: Figshare (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

A Graph NeuralNetwork Charge Model Targeting AccurateElectrostatic Properties of Organic Molecules Common methods for assigning atom-centered partial charges in computational chemistry, such as RESP and AM1-BCC, rely on quantum mechanical or semiempirical calculations of the molecule of interest, which are expensive to compute and dependent on the choice of input molecular conformer(s). Graph neural network (GNN) based continuous atom embeddings have been shown to be a fast and flexible solution for partial charge assignment, but those developed so far for condensed phase modeling have usually been trained to reproduce AM1-BCC charges, which themselves seek to reproduce the HF/6-31G(d) molecular electrostatic potential. Here, we investigate the suitability of various common charge assignment schemes, including ESP and atoms-in-molecule (AIM) based approaches, as training targets for new GNN-based charge models. We show that the strengths of both approaches can be combined by cotraining GNN models to AIM charges and molecular dipoles and electrostatic potentials. We collect a data set of quantum mechanical AIM properties computed at a high level of theory (ωB97X-D/def2-tzvpp), in both vacuum and implicit solvent, and train new GNN charge models to each. Charges can be scaled between the vacuum and solvated charge sets, and combined with Lennard-Jones parameters optimized using the Open Force Field infrastructure, to yield force fields that are suitably polarized for condensed phase modeling. We further demonstrate that the charge models may be applied to explore electrostatics-driven structure–activity relationships in medicinal chemistry. The charge models are freely available at: https://github.com/cole-group/nagl-mbis/. Partial charge ; Charge (physics) ; Electrostatics ; Dipole ; Artificial neural network ; Quantum ; Molecule ; Force field (fiction) ; Atom (system on chip) ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Negar Nayerain Jazi | Sajad Alavimanesh | Asma Vafadar | Maedeh Choubani | Shayan Khalili Alashti | Farhad Moradi | Amir Savardashtaki
Source: Cancer Cell International (review, 2025-12-11)
Field: Materials Science — Materials Chemistry

Theranostic potential of MXene-based platforms for dual MiRNA targeting in metastatic bladder cancer Metastatic bladder cancer (mBC) continues to resist current treatment approaches, largely because aberrant microRNAs (miRNAs) simultaneously fuel epithelial-mesenchymal transition, enable immune system evasion, and promote resistance to therapies. Mounting evidence pinpoints miR-21 as a key oncomiR that amplifies PI3K/AKT signaling, while miR-200c plays a counterbalancing role by maintaining epithelial traits and promoting apoptosis. This makes the combined strategy of suppressing miR-21 while restoring miR-200c an especially compelling, though technically intricate, therapeutic target. Recently, two-dimensional transition-metal carbides and nitrides-known as MXenes-have emerged as promising tools, thanks to their exceptionally high surface area, excellent conductivity, and customizable surface chemistry, all of which make them ideal for shielding miRNAs, delivering them selectively to tumors, and enabling real-time photothermal monitoring. In this review, we weave together insights from epidemiology, molecular oncology, and nanotechnology to chart a translational pathway for applying MXene-based theranostic systems in mBC. We compile mechanistic data regarding miR-21 and miR-200c, detail the MXene physicochemical traits crucial for RNA loading and biosensing, and critically assess polymer-, ligand-, and ion-intercalation methods that improve biocompatibility without compromising electrical performance. Notably, preclinical studies show that MXene scaffolds can co-deliver anti-miR-21 and miR-200c mimics, destroy tumors via near-infrared photothermal therapy, and allow for electrochemical tracking of miRNA activity inside tumors-together achieving a potent, combined blockade of metastatic processes. Persistent challenges, including oxidative degradation, dose-dependent toxicity, and large-scale manufacturing, are addressed alongside promising innovations such as biodegradable surface coatings and machine-learning-assisted material design. Altogether, dual-miRNA theranostics using MXenes hold immense promise as transformative tools for precision treatment of metastatic bladder cancer. Photothermal therapy ; microRNA ; Bladder cancer ; Oncomir ; Nanomedicine ; Cancer ; Biocompatibility MXene and MAX Phase Materials ; Nanoplatforms for cancer theranostics ; MicroRNA in disease regulation
Authors: Fan Zhang | Li Fu | Weiwei Gao | Peihong Zhang | Jijun Zhao
Source: ArXiv.org (preprint, 2025-01-24)
Field: Materials Science — Materials Chemistry

Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information Vacancy-ordered double perovskites (VODPs) are promising alternatives to three-dimensional lead halide perovskites for optoelectronic and photovoltaic applications. Mixing these materials creates a vast compositional space, allowing for highly tunable electronic and optical properties. However, the extensive chemical landscape poses significant challenges in efficiently screening candidates with target properties. In this study, we illustrate the diversity of electronic and optical characteristics as well as the nonlinear mixing effects on electronic structures within mixed VODPs. For mixed systems with limited local environment options, the information regarding atomic-site occupation in-principle determines both structural configurations and all essential properties. Building upon this concept, we have developed a model that integrates a data-augmentation scheme with a transformer-inspired graph neural network (GNN), which encodes atomic-site information from mixed systems. This approach enables us to accurately predict band gaps and formation energies for test samples, achieving Root Mean Square Errors (RMSE) of 21 meV and 3.9 meV/atom, respectively. Trained with datasets that include (up to) ternary mixed systems and supercells with less than 72 atoms, our model can be generalized to medium- and high-entropy mixed VODPs (with 4 to 6 principal mixing elements) and large supercells containing more than 200 atoms. Furthermore, our model successfully reproduces experimentally observed bandgap bowing in Sn-based mixed VODPs and reveals an unconventional mixing effect that can result in smaller band gaps compared to those found in pristine systems. Vacancy defect ; Space (punctuation) ; Computer science ; Materials science ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Optical Imaging and Spectroscopy Techniques
Authors: Cao, Yang | Zhao Song | Jiahao Zhang | Jiale Zhao
Source: ArXiv.org (preprint, 2025-10-07)
Field: Materials Science — Materials Chemistry

Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective Graph neural networks (GNNs) have become a core paradigm for learning on relational data. In materials science, equivariant GNNs (EGNNs) have emerged as a compelling backbone for crystalline-structure prediction, owing to their ability to respect Euclidean symmetries and periodic boundary conditions. Despite strong empirical performance, their expressive power in periodic, symmetry-constrained settings remains poorly understood. This work characterizes the intrinsic computational and expressive limits of EGNNs for crystalline-structure prediction through a circuit-complexity lens. We analyze the computations carried out by EGNN layers acting on node features, atomic coordinates, and lattice matrices, and prove that, under polynomial precision, embedding width $d=O(n)$ for $n$ nodes, $O(1)$ layers, and $O(1)$-depth, $O(n)$-width MLP instantiations of the message/update/readout maps, these models admit a simulation by a uniform $\mathsf{TC}^0$ threshold-circuit family of polynomial size (with an explicit constant-depth bound). Situating EGNNs within $\mathsf{TC}^0$ provides a concrete ceiling on the decision and prediction problems solvable by such architectures under realistic resource constraints and clarifies which architectural modifications (e.g., increased depth, richer geometric primitives, or wider layers) are required to transcend this regime. The analysis complements Weisfeiler-Lehman style results that do not directly transfer to periodic crystals, and offers a complexity-theoretic foundation for symmetry-aware graph learning on crystalline systems. Equivariant map ; Embedding ; Lattice (music) ; Euclidean geometry ; Computation ; Graph ; Complexity class ; Artificial neural network ; Homogeneous space Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Memory and Neural Computing
Authors: Sergio Contreras Arredondo | Chenyu Tang | Radu A. Talmazan | Alberto Megías | Cheng Giuseppe Chen | Christophe Chipot
Source: ArXiv.org (preprint, 2025-07-23)
Field: Chemistry — Physical and Theoretical Chemistry

From Atoms to Dynamics: Learning the Committor Without Collective Variables This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach opens new avenues for understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes. Key (lock) ; Function (biology) ; Identification (biology) ; Atom (system on chip) ; Perceptron ; Variable (mathematics) ; Artificial neural network History and advancements in chemistry
Authors: Aulia Ishak | Fazida Hanim Hashim | Maizom Hassan | Ahmad Faris Seman | Asraf Mohamed Moubark
Source:  (article, 2025-10-04)
Field: Chemistry — Analytical Chemistry

Detection and Classification of Pesticide Residues in Pak Choy Using Raman Spectroscopy This study investigates the use of Raman spectroscopy combined with machine learning for detecting and classifying pesticide residues on Pak Choy (Brassica rapa subsp. chinensis). Raman spectroscopy enables rapid, non-destructive molecular analysis, and its integration with machine learning provides a promising approach for food safety monitoring. To date, no published work has applied Raman spectroscopy to pesticide detection on Pak Choy leaves. A total of 52 samples were prepared under four case studies: (1) pesticide concentration levels, (2) spraying frequency on laboratory-grown Pak Choy, (3) spraying frequency on hydroponic Pak Choy, and (4) residue reduction through vinegar and baking soda washing. Raman spectra were preprocessed, deconvoluted, and statistically analyzed. Classification models including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) were developed using MATLAB's Classification Learner. Results showed that pesticide residues on non-living Pak Choy leaves (Case Studies 1, 2, and 4) were classified with 100% accuracy using the Linear Discriminant model. Living hydroponic plants (Case Study 3) achieved 77% accuracy with the Linear SVM model. Two Raman peaks, 1348 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{c m}^{-\mathbf{1}}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1593 \mathbf{c m}^{-\mathbf{1}}$</tex>, were identified as biomarkers for Cypermethrin detection. These findings demonstrate that Raman spectroscopy, combined with machine learning, offers a practical solution for pesticide residue monitoring. The approach has potential for portable, on-site applications as a rapid, cost-effective complement to conventional laboratory methods such as GC-MS and HPLC. Raman spectroscopy ; Pesticide residue ; Support vector machine ; Pesticide ; Linear discriminant analysis ; Residue (chemistry) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Smart Agriculture and AI
Authors: Bing Wang | Wei Zhou | Yongsheng Ren | J. Xu | Shu Zhan | Wenhui Ma
Source: Computational Materials Science (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Local angle information propagation model based on dual scale for crystal property prediction Dual (grammatical number) ; Scale (ratio) ; Property (philosophy) ; Materials science ; Crystal (programming language) ; Scale model ; Statistical physics ; Computer science ; Physics ; Geography ; Engineering ; Aerospace engineering ; Cartography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Andrew
J. White (21373959) | Marco Gibaldi (13822259) | Jake Burner (6824633) | R. Alex Mayo (1671949) | Tom K. Woo (1332735)
Source: Figshare (article, 2025-05-16)
Field: Chemistry — Inorganic Chemistry

High Structural ErrorRates in “Computation-Ready”MOF Databases Discovered by Checking Metal Oxidation States “Computation-ready” metal–organic framework (MOF) databases provide essential raw data for high-throughput computational screening (HTS) and machine-learning approaches to materials discovery. However, the structural fidelity of these databases remains largely unquantified. We introduce MOSAEC, an algorithm that detects chemically invalid structures based on metal oxidation states. MOSAEC was manually validated against 14,796 MOF structures from the popular CoRE database and found to flag erroneous structures with 96% accuracy. Examination of 14 leading experimental and hypothetical MOF databases containing >1.9 million structures reveals structural error rates exceeding 40% in most cases. Analysis of 8 recent HTS studies which highlighted top-performing candidates shows that 52% of these structures were chemically invalid. Core (optical fiber) ; Raw data ; Data structure ; Key (lock) ; High fidelity Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Maotao Liu | Qun Liu | Xu Gong | Yunsong Luo | Guoyin Wang
Source: Neurocomputing (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Mol-L2: Transferring text knowledge with frozen language models for molecular representation learning Computer science ; Natural language processing ; Representation (politics) ; Artificial intelligence ; Knowledge representation and reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Faizan Khan | Vishal Singh Pawak | Chandra Shekhar | Venkateshwar Rao Dugyala | Tarak Mondal | Manigandan Sabapathy
Source: Preprints.org (preprint, 2025-07-28)
Field: Materials Science — Materials Chemistry

Kinetic Insights into Methylene Blue Degradation via Platinum-Coated Polystyrene Rough Particle Catalysts: An Interfacial Catalytic Approach Platinum-coated polystyrene (PT-PS) rough colloids were employed as catalysts to degrade methylene blue (MB), a representative organic pollutant, at the air-water interface. The particles were synthesized and examined using electron and light microscopy together with complementary analytical techniques to verify their morphology and surface features. Degradation tests were carried out at the air–water interface using an interface-trapping configuration under controlled circulation. At 50 RPM (Reynolds number of 1686) and a particle packing fraction of 0.8, MB was fully degraded within 30 minutes, while the same treatment in bulk solution required 75 minutes. The reaction followed the Langmuir–Hinshelwood model with a rate constant of 0.018 min$^{-1}$. Kinetic predictions were evaluated using an Artificial Neural Network (ANN) model, giving a root mean square error (RMSE) of 5.5 and a correlation coefficient (R$^{2}$) of 0.9656. The approach allows rapid MB degradation while enabling catalyst recovery and low reagent consumption, offering a practical route for wastewater treatment. Catalysis ; Methylene blue ; Polystyrene ; Platinum ; Particle (ecology) ; Materials science ; Degradation (telecommunications) ; Kinetic energy ; Chemical engineering ; Metal particle ; Particle size ; Chemistry ; Composite material ; Photocatalysis ; Organic chemistry ; Computer science ; Physics ; Polymer Catalytic Processes in Materials Science
Authors: Yasuhide Nonaka | Kensuke Takaki | Yusuke Kobayashi | J. Haruyama
Source: AIP Advances (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine learning for predicting physical parameters of atom-vacancy defects from low-frequency noise in few-atom layer MoS2 The rapid advancement of deep learning (DL) has significantly expanded its application across various fields, including physics and materials science. DL facilitates the prediction of physical parameters of materials without the need for time-consuming and labor-intensive experiments. Atom-vacancy defects, present in various materials responsible for intriguing physical phenomena, exhibit diverse physical parameters and are a prime example of such applications. These tendencies are even more pronounced in two-dimensional (2D) (atomically) thin van der Waals layers. However, the correlation between different types of atomic defects and their properties remains an area of active exploration. Herein, we propose an innovative DL model and predict the structural parameters of atom-vacancy defects solely from low-frequency noises, arising from stochastic capture and emission of charges through them, in few-atom layer semiconductors molybdenum disulfides (MoS2). The predicted parameters show good agreement with experimental results. This methodology enables the accurate extraction of structural parameters of various materials, significantly reducing error rates by suppressing excess learning and demonstrating the high potential of DL. Atom (system on chip) ; Vacancy defect ; Noise (video) ; Layer (electronics) ; Atomic physics ; Materials science ; Physics ; Computer science ; Condensed matter physics ; Nanotechnology ; Artificial intelligence ; Embedded system Machine Learning in Materials Science ; 2D Materials and Applications ; Semiconductor materials and interfaces
Authors: Yi-Fan Hou (15195026) | Cheng Wang (102692) | Pavlo O. Dral (545078)
Source: Figshare (article, 2025-04-14)
Field: Chemistry — Spectroscopy

Accurate and AffordableSimulation of Molecular InfraredSpectra with AIQM Models Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, and hence, various theoretical approaches have been developed to simulate and predict the IR spectra. However, the theoretical approaches based on quantum chemical calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (e.g., semiempirical methods orders of magnitude faster than DFT). Here, we introduce a new approach, based on the universal machine learning (ML) models of the AIQM series targeting CCSD(T)/CBS level, that can deliver molecular IR spectra with accuracy close to DFT (compared to the experiment) and the speed close to a semiempirical GFN2-xTB method. This approach is based on the harmonic oscillator approximation with the frequency scaling factors fitted to experimental data. While the benchmarks reported here are focused on harmonic IR spectra, our implementation supports anharmonic spectra simulations via molecular dynamics and VPT2. These implementations are available in MLatom as described in https://github.com/dralgroup/mlatom and can be performed online via a web browser. Anharmonicity ; Scaling ; Molecular dynamics ; Spectral line ; Harmonic ; Infrared ; Infrared spectroscopy ; Density functional theory Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Diullio
P. Santos (22770713) | Jefferson R. Dias-Silva (22770716) | Luiz H. K. Q. Júnior (22770719) | Heibbe C. B. de Oliveira (9226241)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

ThermoPred: AI-EnhancedQuantum Chemistry Data Setand ML Toolkit for Thermochemical Properties of API-Like Compoundsand Their Degradants In this work, we present an open-access quantum-chemistry database of more than 14,500 API-like molecules and their degradation products, all optimized at the M06-2<i>X</i>/6-31G(d) compound model. The data set delivers a comprehensive suite of thermochemical and quantum descriptorsincluding Gibbs free energy, enthalpy, electronic energy, vibrational frequencies and Cartesian geometriestailored for large-scale modeling. Leveraging these data, we trained and validated three machine-learning models (XGBoost, Random Forest and Multi-Layer Perceptron) to enable rapid, accurate prediction of Gibbs free energy and enthalpy. These models are bundled in ThermoPred, an open-source Python package that offers a scalable, computationally efficient alternative to traditional quantum-chemical calculations. All data sets, models and source code are freely available to support reproducibility and foster community-driven development. Gibbs free energy ; Set (abstract data type) ; Quantum chemical ; Python (programming language) ; Suite ; Quantum chemistry ; Spurious relationship ; Data set ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Abdullahi Maina | jisieke Chiazor Faustina | Chukwu Onwuka
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Exploring Machine Learning Models for Predicting Efficiency in Carbazole-Based DSSC Sensitizers This study explores the application of machine learning models in predicting the efficiency of carbazole-based dye-sensitized solar cell (DSSC) sensitizers. The research evaluates multiple machine learning algorithms to model structure–property relationships and optimize photovoltaic performance parameters. The results demonstrate the potential of data-driven approaches in accelerating material discovery and improving predictive accuracy in DSSC efficiency modeling. This work contributes to the growing integration of artificial intelligence in materials science and renewable energy research. Renewable energy ; Photovoltaic system ; Efficient energy use ; Predictive modelling ; Work (physics) ; Energy (signal processing) ; Solar energy ; Support vector machine Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Chemical and Physical Properties of Materials
Authors: Héctor Lobato | Ignacio Trojaola | Felipe Garitaonandia | Jon Haitz Badiola | Pablo Larreategi | Aizeti Burgoa
Source: Composites Communications (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Smart design of thermoplastic vulcanizate products: Linking process to performance via interpretable machine learning Interpretability ; Design of experiments ; Molding (decorative) ; Feature (linguistics) ; Thermoplastic ; Artificial neural network ; Component (thermodynamics) ; Stochastic gradient descent ; Process (computing) Machine Learning in Materials Science ; Manufacturing Process and Optimization