jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuji Ikeda | Axel Forslund | Pravir Kumar | Yongliang Ou | Jong Hyun Jung | Andreas Köhn | Blazej Grabowski
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled-cluster data, particularly CCSD(T), have emerged as a promising route to achieve chemical accuracy beyond the limits of density functional theory (DFT) and to incorporate non-empirical van der Waals (vdW) interactions. Most existing approaches are, however, still not straightforwardly applicable for systems with extended covalent networks such as covalent organic frameworks (COFs) due to the limited availability of CCSD(T) for periodic systems. Here we present a methodology to train MLIPs with CCSD(T) accuracy for these systems. The approach uses the Δ-learning method with a dispersion-corrected tight-binding baseline. This strategy enables training on compact molecular fragments while preserving transferability toward the periodic systems. Dispersion interactions are accounted for by adding vdW-bound multimers in the training set, and the combination with a vdW-aware tight-binding baseline allows the formally local MLIP to attain CCSD(T)-level accuracy even for systems dominated by long-range vdW forces. The resulting potential yields root-mean-square energy errors below 0.4 meV/atom on training and test sets and reproduces electronic total atomization energies, bond lengths, harmonic vibrational frequencies, and inter-molecular interaction energies for benchmark molecular systems. We apply the method to a prototypical quasi-two-dimensional COF composed of carbon and hydrogen. The COF structure, inter-layer binding energies, and hydrogen absorption are analyzed at CCSD(T) accuracy. The developed methodology opens a practical route to large-scale atomistic simulations for systems with extended covalent networks and vdW interactions with chemical accuracy. van der Waals force ; Benchmark (surveying) ; Covalent bond ; Ab initio ; Non-covalent interactions ; Density functional theory ; Transferability ; Hydrogen bond ; Computation ; Harmonic Machine Learning in Materials Science ; Crystallography and molecular interactions ; Surface Chemistry and Catalysis
Authors: Iskandar Raufov
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

State of the Art and Future Perspectives of DFT Applications in Functional Materials Analysis in Tajikistan Key (lock) ; Density functional theory ; State (computer science) ; Stability (learning theory) ; Sustainable energy ; Energy (signal processing) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Heusler alloys: electronic and magnetic properties
Authors: Juwon Jung | Leeju Hwang | Nagyeong Kim | Kibaek Kim | Seri Kim | Jongkyoon Park | Won Chegal | Yong Jai Cho | Young‐Joo Kim
Source: Nanophotonics (article, 2025-12-01)
Field: Materials Science — Surfaces, Coatings and Films

AI‐based analysis algorithm incorporating nanoscale structural variations and measurement‐angle misalignment in spectroscopic ellipsometry Spectroscopic ellipsometry (SE) is a powerful, non-destructive technique for nanoscale structural characterization. However, conventional SE data analysis typically assumes perfectly periodic specimen structures, overlooking fabrication-induced structural variations and thereby reducing the accuracy of predicted structural parameters. We have developed an enhanced analysis framework that explicitly accounts for both nanoscale structural variations and measurement-angle misalignment by introducing the concept of an average Mueller matrix (MM), which represents statistical distributions of nanoscale structures. In addition, we introduce a high-throughput MM-generation neural network that enables rapid data preparation by approximating rigorous coupled-wave analysis (RCWA) simulations for large numbers of specimens across a broad range of structural parameters. The model achieves a mean-squared error of 9.99 × 10<sup>-8</sup> MSE when validated against RCWA-simulated MM data for one-dimensional SiO<sub>2</sub> nanogratings. Finally, we apply our analysis framework to experimentally measured MM data, achieving highly accurate dimensional predictions with errors below 0.4 nm when compared with structural parameters measured by scanning electron microscopy (SEM). We believe that this analysis algorithm significantly advances the potential for high-precision SE-based metrology in semiconductor, photonic, and display manufacturing. Nanoscopic scale ; Metrology ; Ellipsometry ; Range (aeronautics) ; Nanometrology ; Atomic force microscopy ; Nanomaterials ; Experimental data Optical Coatings and Gratings ; Optical Polarization and Ellipsometry ; Electron and X-Ray Spectroscopy Techniques
Authors: Nisha Geng | Giacomo Scillla | Eva Zurek
Source: ArXiv.org (preprint, 2025-02-10)
Field: Materials Science — Materials Chemistry

Theoretical Predictions of MB5N5: Atom-Stuffed Boronitride Clathrate Cages Derived from the High-Pressure Superhydride This study investigates 198 MX5Y5 (X, Y = B, C, or N) clathrate-like structures derived from MH10 superhydrides using high-throughput Density Functional Theory (DFT) geometry optimizations and phonon calculations. A wide variety of electropositive and electronegative encapsulated atoms were considered. From all of the studied systems only 34 MB5N5 phases were found to be dynamically stable at ambient pressure. The highest 1-atmosphere superconducting critical transition temperature was predicted for FB5N5. However, ab initio molecular dynamics simulations revealed that all of the identified superconducting phases decompose by 300~K at 1~atm, while only eleven semiconducting phases remained thermally stable. Our findings underscore the critical role of kinetic and thermal stability in predicting viable superconductors. The electronic structure of the MB5N5 compounds were rationalized in terms of electron donating and withdrawing intercalants, and machine-learning based predictions of their mechanical properties were compared with those of an empty boronitride cage. Clathrate hydrate ; Atom (system on chip) ; High pressure ; Materials science ; Crystallography ; Chemical physics ; Chemistry ; Physics ; Thermodynamics ; Computer science ; Hydrate Boron and Carbon Nanomaterials Research ; Energetic Materials and Combustion ; Boron Compounds in Chemistry
Authors: Volkan Erol
Source: Preprints.org (preprint, 2025-08-13)
Field: Materials Science — Materials Chemistry

AI-Driven Drug Design MVP Integrating DEEPScreen and Related Tools Drug discovery is a complex, time-consuming, and costly process, often taking over a decade and billions of dollars to bring a new drug to market. Recent advances in artificial intelligence (AI) and deep learning (DL) have enabled significant improvements in early-stage drug-target interaction (DTI) prediction, reducing both time and cost . This paper presents a Minimum Viable Product (MVP) framework integrating ligand-based deep learning approaches, such as DEEPScreen, with structure-based virtual screening using GNINA and AutoDock Vina. The proposed system enables researchers to quickly match candidate small molecules with protein or enzyme targets, facilitating hit identification and prioritization. We discuss model architectures, dataset preparation, integration strategies, and potential applications in academic and industrial drug discovery settings. Drug ; Computer science ; Data science ; Medicine ; Pharmacology Machine Learning in Materials Science
Authors: Alauddin Ahmed
Source: PRX Energy (article, 2025-11-03)
Field: Chemistry — Inorganic Chemistry

Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs Storing hydrogen safely and compactly is a central challenge for clean transportation, grid support, and industrial decarbonization. Metal-organic frameworks (MOFs) are compelling candidates because of their ultrahigh internal surface areas and large pore volumes, yet the design space is effectively infinite, making exhaustive synthesis and simulation impractical. We introduce a physics-informed analytical modeling approach that uses deterministic sparsity-promoting symbolic regression with explicit physical constraints to produce a family of closed-form equations for fast, interpretable, and deployable prediction of usable hydrogen storage in MOFs. On a curated dataset of 88 400 MOFs, the method discovers compact, physically consistent structure-property relationships using as few as one property (void fraction for volumetric capacity) or two properties (void fraction and pore volume for gravimetric capacity) selected from seven descriptors: single-crystal density, gravimetric surface area, volumetric surface area, void fraction, pore volume, largest cavity diameter, and pore-limiting diameter. Equations using one to three properties achieve design-grade accuracy ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msup> <a:mi>R</a:mi> <a:mn>2</a:mn> </a:msup> </a:math> &gt; 0.99 for gravimetric capacity and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msup> <c:mi>R</c:mi> <c:mn>2</c:mn> </c:msup> </c:math> &gt; 0.95 for volumetric capacity), while additional descriptors provide marginal gains for high-capacity edge cases, matching black-box machine learning performance and providing mechanistic insights into why some MOFs excel and others do not. Because evaluation is simulation-free and effectively costless, the models integrate directly into high-throughput screening, active learning workflows, and generative design to prioritize syntheses and avoid dead ends, establishing a generalizable path to interpretable and deployable surrogates for adsorption-based energy materials. Gravimetric analysis ; USable ; Grid ; Volume (thermodynamics) ; Porosity ; Surface (topology) ; Hydrogen storage Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysts for Methane Reforming
Authors: ShyamSundar . P.Magar
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-06-15)
Field: Chemistry — Analytical Chemistry

“AgriVision” a Flutter -Based Application for RealTime Detection of Pesticides Residues on Fruits .” Mobile technology offers a promising solution to this Abstract : “AgriVision” is a mobile application developed using Flutter, aimed at tackling the serious concern of pesticide residue on fruits. It employs real-time image analysis and machine learning algorithms to identify chemical contaminants present on fruit surfaces, helping ensure compliance with health and food safety standards. By using the smartphone's camera, users can scan fruits, and the app instantly analyzes and reports the potential presence of hazardous substances. Its intuitive design and mobile nature make it a valuable tool for farmers, sellers, and consumers to evaluate fruit safety and encourage more sustainable farming practices. The project highlights how mobile platforms and artificial intelligence can be merged to deliver effective and scalable solutions in agriculture. Future developments may include linking the app to a centralized database, implementing predictive analytics to monitor pesticide usage patterns, and extending compatibility to a wider range of crops. Keywords: AgriVision, Flutter app, instant detection, chemical residues, fruit safety, agricultural innovation, mobile technology, AI in agriculture, image-based analysis, smart farming, food hygiene, CNN-based detection, pesticide awareness. Flutter ; Pesticide ; Computer science ; Environmental science ; Engineering ; Aerospace engineering ; Agronomy ; Biology ; Aerodynamics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Pesticide Residue Analysis and Safety
Authors: Jianjun Ding | Niansong Liu | G. K. L. Wang | Naiyu Guo | Chao Sun
Source: Sensors (article, 2025-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detection of Pesticide Residues Using Three-Dimensional SERS Substrate Based on CNTs/Ag/AgNWs/SiO2 In response to the shortcomings of traditional surface-enhanced Raman spectroscopy (SERS) substrates, such as short shelf life, poor uniformity, and low selectivity, this study innovatively proposed a three-dimensional composite substrate of CNTs/Ag/AgNWs/SiO<sub>2</sub>. This substrate demonstrates excellent SERS enhancement effects, with a detection limit of 10<sup>-12</sup> mol/L for the probe molecule Rhodamine 6G (R6G) and an enhancement factor (EF) of 8.947 × 10<sup>8</sup>. Further experiments confirmed the substrate's superior uniformity and stability. The enhancement mechanism was investigated using both experimental methods and the Finite Difference Time Domain (FDTD) approach. When commonly used pesticide thiram was used as the target analyte, the detection limit of the substrate reached 0.1 mg/L, which is significantly lower than the pesticide residue standards of China and the European Union. Additionally, the genetic algorithm (GA)-optimized Back Propagation (BP) neural network was introduced for the quantitative analysis of thiram concentrations. The experimental results indicated that the GA-BP algorithm achieved the training prediction accuracy of 92.5% for thiram, demonstrating good network performance. This method shows good selectivity and has broad application prospects in the detection of toxic chemicals, environmental pollutants, and food additives. Substrate (aquarium) ; Nanotechnology ; Materials science ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Francesc Sabanés Zariquiey (7249526) | Stephen E. Farr (21019621) | Stefan Doerr (4332823) | Gianni De Fabritiis (279560)
Source: Figshare (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

QuantumBind-RBFE:Accurate Relative Binding Free EnergyCalculations Using Neural Network Potentials Accurate prediction of protein–ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on the TensorNet architecture for small molecules that broadens the applicability to diverse drug-like compounds, including all important chemical elements and supporting charged molecules. Using established benchmarks, we show overall improved accuracy and correlation in binding affinity predictions compared with GAFF2 for molecular mechanics and ANI2-x for NNPs. Slightly less accuracy but comparable correlations with OPLS4. We also show that we can run the NNP simulations at 2 fs time step, at least two times larger than previous NNP models, providing significant speed gains. The results show promise for further evolutions of free energy calculations using NNPs while demonstrating its practical use already with the current generation. The code and NNP model are publicly available for research use. Energy (signal processing) ; Artificial neural network ; Affinities ; Binding affinities ; Binding energy ; Current (fluid) ; Code (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yasunobu Ando
Source: Vacuum and Surface Science (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Accelerating the Data Generation, Accumulation and Utilization via Machine Learning Data science has emerged as the fourth scientific method, following experiment, theory, and computation. Since the launch of the Material Genome Initiative in 2011, data-driven approaches such as materials informatics and process informatics have been actively applied in materials. To integrate data science into traditional research, it is essential to understand research activities as a data cycle consisting of three phases : data generation, accumulation, and utilization. This cyclic process enhances the efficiency and scope of scientific research. To illustrate the impact of data science in materials research, this paper introduces Bayesian optimization for autonomous experimental system, machine learning potentials, personal databases using JSON format, and high-throughput automatic spectral analysis. These approaches contribute to the advancement of materials science through data-driven methodologies, accelerating the data cycle. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ignacio Pickering (17409430) | Jinze Xue (5400851) | Kate Huddleston (20828923) | Nicholas Terrel (22453747) | Adrian E. Roitberg (122734)
Source: Figshare (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

TorchANI 2.0: AnExtensible, High-Performance Libraryfor the Design, Training, and Use of NN-IPs In this work, we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. TorchANI 2.0 builds upon the foundation of its predecessor, while addressing its limitations and introducing new features. These changes greatly enhance its extensibility, performance, and suitability as a framework for developing models ready for molecular dynamics applications. These improvements include the introduction of a modular system to add arbitrary pairwise potentials to models, CUDA-accelerated optimization for faster and more memory-efficient calculation of local atomic features, and a batched system for better performance of network ensembles, among others. Our benchmarks demonstrate that TorchANI 2.0 achieves significant speedup over previous versions in both training and inference, and the library enhancements allow users to train physically constrained models that better represent important qualities of chemical systems. We demonstrate this by introducing three new ANI models that incorporate these features and evaluating their capabilities. Modular design ; Pairwise comparison ; Speedup ; Software ; Deep learning ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: K. A. L. Lima | José A.S. Laranjeira | Nicolas F. Martins | Júlio R. Sambrano | Alexandre C. Dias | Luiz A. Ribeiro Junior | Douglas S. Galvao
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a distinctive geometric arrangement. DP structural, thermodynamic, mechanical, electronic, and optical properties were evaluated using density functional theory and a machine learning interatomic potential trained explicitly for this material. The formation energy of -7.98 eV/atom indicates high thermodynamic stability, further supported by the absence of imaginary phonon modes and the preservation of structural integrity up to 1000 K in ab initio molecular dynamics simulations. Mechanical analysis reveals high in-plane stiffness with directional dependence: Young's modulus values of 469.09 GPa and 600.41 GPa along the x and y directions, respectively. Electronic band structure and projected density of states analyses confirm the DP semiconducting character. Calculations of carrier mobility using the deformation potential theory reveal pronounced anisotropy, with maximum values reaching up to $30.6 \times 10^4$ cm$^2$/V$\cdot$s (electrons, e) and $8.4 \times 10^4$ cm$^2$/V$\cdot$s (holes, h), much higher than the observed for other 2D materials. DP also exhibits anisotropic optical absorption in the visible and ultraviolet spectrum, highlighting its potential for optoelectronic applications. Density functional theory ; Phonon ; Anisotropy ; Ab initio ; Deformation (meteorology) ; Electronic band structure ; Electronic structure ; Absorption (acoustics) Machine Learning in Materials Science
Authors: Kamyar Barakati | Yu Liu | Utkarsh Pratiush | Boris N. Slautin | Sergei V. Kalinin
Source: ArXiv.org (preprint, 2025-02-23)
Field: Materials Science — Materials Chemistry

Rewards-based image analysis in microscopy Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing. Hyperspectral imaging ; Workflow ; Pattern recognition (psychology) ; Representation (politics) ; Curse of dimensionality ; Key (lock) ; Feature (linguistics) ; Dimensionality reduction ; Image (mathematics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques