jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng-Shan Chen | Yao Cai | Yuhong Yang | Zongliang Huang | Hao-Zhu Zhang | YewChung Sermon Wu | Ming-Hsien Li | Ming‐Yu Kuo | Hsiang Chen | Yung‐Hui Li
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Photodetector based on bis-2,6-[2-(2-oxoindolin-3-ylidene)malononitrile]naphthalene derivatives/zinc oxide nanorod heterostructures with machine vision observation and artificial intelligence pattern recognition Zinc oxide (ZnO)-based photodetectors have made significant progress in the field of broadband photodetection in recent years due to their ease of integration with low-bandgap semiconductors. Although the intrinsic bandgap of 3.2 eV for pure ZnO limits its response to wavelengths outside the ultraviolet (UV) light region, researchers have successfully extended its detection range into the visible light region through material modifications and heterojunction designs. This study investigates the use of the bis-2,6-[2-(2-oxoindolin-3-ylidene)malononitrile]naphthalene derivative (teven-518)/ZnO nanocomposite as a photodetector. The molecular structure of teven-518 possesses a high electron affinity that is further enhanced by the strong electron-withdrawing nature of the malononitrile groups, which improves electron transport capability. Additionally, the introduction of the naphthalene unit enhances molecular coplanarity, resulting in a rigid and planar structure that promotes π-π stacking between molecules, thereby improving the charge carrier mobility. On the other hand, the C2C6 side chains in the molecule provide moderate intermolecular interactions, enabling uniform film formation during solution processing. Through the integration of code development and model training, along with material characterization and photodetection results, this study confirms that the addition of teven-518 with ZnO nanorods contributes to the enhancement of ZnO-based photodetector performance, showing significant improvements in both machine learning-based recognition and model evaluation. Photodetection ; Photodetector ; Nanorod ; Heterojunction ; Stacking ; Ultraviolet ; Band gap ; Charge carrier ZnO doping and properties ; Ga2O3 and related materials ; Organic Electronics and Photovoltaics
Authors: A. L. Goncharov | I. S. Chulkov | H. M. Kozyrev
Source: Russian Electrical Engineering (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

A Hardware and Software System for Determining Thermoelectric Properties of Industrial Metal Alloys The article considers the development of a setup for studying the thermo-EMF of metallic materials from industrially produced alloys for various purposes. The setup allows measuring the thermo-EMF using thermocouples made of the alloys under study, synchronously with temperature measurements. An integral method with subsequent mathematical processing to calculate the Seebeck coefficient is used for the measurement. The error of the mathematical processing is estimated, and the values of the Seebeck coefficient are verified against the data of other studies in which an accurate differential measurement method was used. Temperature curves of the Seebeck coefficient are obtained for 73 grades of industrial alloys, including alloyed steels, nickel-based alloys, titanium, and some refractory metals. Analysis of the obtained data using machine learning methods makes it possible to identify generalized features of thermo-EMF changes for the studied groups of materials and classify them by the type of thermo-EMF curves. A model for predicting the Seebeck coefficient for new materials based on the experimental data regression is developed and trained. To increase prediction accuracy, the measured values are used not only as a training model response, but also as additional features along with data on the alloy chemical composition. Thermoelectric effect ; Software ; Thermoelectric materials ; Engineering ; Electrical engineering ; Materials science ; Mechanical engineering ; Computer science ; Embedded system ; Electrical resistivity and conductivity ; Physics ; Operating system Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Zhang Sun-Cheng | Han Tong-Wei | Wang Ru-Meng | Yang Yan-Tao | Xiaoyan Zhang
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction and optimization of negative Poisson’s ratio in rhombic perforated graphene based on machine learning Tuning graphene’s properties through structural design has received significant attention. However, the complex nonlinear relationship between geometric parameters of structural design and performance needs further exploring to accurately predict the performance of graphene and speed up the optimization of its structural design. This study introduces periodic rhombic perforations to effectively achieve the structural design of graphene with negative Poisson’s ratio (NPR). The mechanisms underlying the NPR effect are analyzed, and a data-driven machine learning model based on a backpropagation neural network (BPNN) is developed to efficiently predict and design perforated graphene structures exhibiting NPR. By constructing a Poisson’s ratio dataset for rhombic perforated graphene structures through molecular dynamics simulations and employing an optimized BPNN model for predictive analysis, it is found that the perforation spacing ratio (IS) has the most significant effect on the Poisson’s ratio of rhombic perforated graphene, while the perforation aspect ratio (AR) and unit cell size (&lt;i&gt;L&lt;/i&gt;) have relatively weak influence. The study further investigates the influence of various perforation geometric parameters on the NPR behavior of graphene. It is found that reducing IS and increasing AR can enhance the negative Poisson’s ratio effect. The machine learning predictions closely align with molecular dynamics simulation results, demonstrating the effectiveness and reliability of this approach for Poisson’s ratio prediction. By integrating rhombic perforation design with machine learning technologies, this research provides an efficient framework for optimizing the NPR effect in graphene, and theoretical support for its application in smart materials and flexible electronics. Graphene ; Poisson distribution ; Materials science ; Nanotechnology ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Graphene research and applications ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Bohdan Y. Semeniuk | Oleh Feia
Source:  (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Implementation of a methodology for crystal structure prediction using genetic algorithms integrated into the Python ASE library This work is dedicated to the development and implementation of a methodology for crystal structure prediction using genetic algorithms integrated into the Python ASE library. Crystal structure prediction plays a critical role in materials science, chemistry, and nanotechnology, enabling the discovery of novel compounds with tailored properties. By combining the flexibility of ASE with the speed of classical relaxers and the accuracy of DFTbased methods, our approach significantly reduces computational costs while maintaining predictive reliability. The methodology was validated on polymorphs of silica $(\mathrm{SiO}_2)$, where our system successfully recovered both global and local minima of the energy landscape. We also explore the integration of neural network relaxers such as MACE and AIMNet2 to further accelerate the search process. This study lays the groundwork for efficient, scalable, and accurate predictive modeling of crystalline materials. Python (programming language) ; Computer science ; Algorithm ; Programming language Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Mineral Processing and Grinding
Authors: Bingbing Zhang | Kenneth Wu | Chang Zhang | Hang Xiao | Liangliang Zhu
Source: AIP Advances (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Improving SLICES crystal representation through CHGNet integration and parameter tuning In the rapidly evolving field of materials science, the inverse design of crystals has become increasingly significant with the widespread adoption of generative models. However, current crystal representation methods face persistent challenges in maintaining both invertibility and invariance. To bridge this gap, we present an enhanced approach to the simplified line-input crystal-encoding system (SLICES) representation by incorporating the Crystal Hamiltonian Graph Neural Network (CHGNet) machine learning force field model. Comprehensive evaluations across multiple datasets (MP-20, MP21-40, and MOF) using different neighbor recognition algorithms (EconNN and CrystalNN) show that our approach outperforms the original in reconstructing structures with fewer than 20 atoms per unit cell, achieving up to a 1.34% improvement in reconstruction rate (from 92.55% to 93.89%). Furthermore, through systematic optimization of key parameters, including bond scaling, Δx, and lower bounds of lattice scaling, we achieved enhanced reconstruction performance. Our research represents a step forward in developing more efficient solutions for crystal inverse design utilizing the SLICES representation framework. Representation (politics) ; Computer science ; Materials science ; Crystal (programming language) ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Changhong Zhang | Jie Liang | Yi Li | Rongmei Chen | Yuanhao Miao | Zhenyu Wang | Lei Zhang | Haoyuan Li | Hanshen Xin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Application and Acceleration of Machine Learning Potential Construction Using Farthest Point Sampling: A Case Study of Carbon Nanotubes Acceleration ; Sampling (signal processing) ; Point (geometry) ; Carbon nanotube ; Computer science ; Environmental science ; Industrial engineering ; Artificial intelligence ; Engineering ; Nanotechnology ; Mathematics ; Materials science ; Physics ; Computer vision ; Classical mechanics ; Geometry Machine Learning in Materials Science
Authors: A.D. LIFANOV | A.A. FATYKHOVA | K.YU. NIKITINA
Source: Herald Of Technological University (article, 2025-01-01)
Field: Materials Science — General Materials Science

MODELING OF ADHESIVE COMPOUNDS OF POLYMERS IN CONTACT WITH METALLS In modern materials science, polymer-based synthetic materials are increasingly being used, including using composite materials. These materials are widely used as adhesive compounds in contact with metals. Ensuring the reliability of modern materials in harsh operating conditions determines the use of multifunctional polymer materials with a set of properties and capable of performing several important functions. At the same time, polymer materials can provide high physical and mechanical properties, adhesion, protection from aggressive media, high temperatures, vibration, atmospheric action, and possess special technological and operational properties. The article develops models for predicting the surface energy characteristics of polymers in adhesive compounds with metals. The processes of creating adhesive compounds based on polymer materials are directly related to the possibility of predicting and regulating interphase and intermolecular acid-base interactions between the components of adhesive compositions. In the course of the work, regression and classification models were built using ridge regression, the nearest neighbor method, the support vector method, artificial neural networks and the random forest algorithm, where the predicted values were the acidic and basic parameters of free surface energy and the label of the polymer belonging to the acid/basic class. It is shown that the obtained models are able to predict acidic and basic properties of polymers equally well. Linear representations of SMILES were chosen as representations of polymer molecules. Data on the surface energy characteristics of polymers were collected manually based on publications in the period from 1969 to 2022. Morgan's structural keys and molecular fingerprints were chosen as descriptors. The structure-property dependency models were built using ridge regression methods, the nearest neighbor method (kNN), the support vector machine (SVM) method and artificial neural networks using the example of a multilayer perceptron (MLP). We have shown that the k-nearest neighbor method has the best predictive ability. The statistical characteristics of the constructed kNN model for our sample have the following values: ACC = 0.89, SPC = 0.92, SEN = 0.86, BA = 0.89, Accuracy = 0.92, F1-measure=0.86. Adhesive ; Polymer ; Geology ; Materials science ; Nanotechnology ; Composite material Material Properties and Applications ; Mechanical and Thermal Properties Analysis
Authors: Paul Fuchs | Weilong Chen | Stephan Thaler | Julija Zavadlav
Source: ArXiv.org (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Machine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to specific MLP architectures, lack integration with standard MD packages, or are not parallelizable across GPUs. To address these challenges, we present chemtrain-deploy, a framework that enables model-agnostic deployment of MLPs in LAMMPS. chemtrain-deploy supports any JAX-defined semi-local potential, allowing users to exploit the functionality of LAMMPS and perform large-scale MLP-based MD simulations on multiple GPUs. It achieves state-of-the-art efficiency and scales to systems containing millions of atoms. We validate its performance and scalability using graph neural network architectures, including MACE, Allegro, and PaiNN, applied to a variety of systems, such as liquid-vapor interfaces, crystalline materials, and solvated peptides. Our results highlight the practical utility of chemtrain-deploy for real-world, high-performance simulations and provide guidance for MLP architecture selection and future design. Scalability ; Exploit ; Parallelizable manifold ; Software deployment ; Artificial neural network ; Software ; Variety (cybernetics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Amin Ahmadisharaf (19329937) | Adri C. T. van Duin (7330610) | Bin Liu (5899) | Dylan Evans (10525667) | Sadra Sabouri (22557715) | Jeffrey Comer (1278954)
Source: Figshare (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

ReaxFF ParameterSet for Boron Clusters and IcosahedralBoron Crystals: Comparison with Density Functional Theory and Machine-LearningPotentials Icosahedral boron materials, which include regular icosahedra of 12 boron atoms have gained increasing attention due to their potential applications as superhard materials, semiconductors, and energy storage media. However, the synthesis of high quality crystals of these materials has been a major barrier to the development of these applications. To enable computational prediction of synthesis conditions yielding high-quality icosahedral boron crystals, herein we tested and refined a set of ReaxFF parameters for the nucleation and growth of such crystals. We focused on matching the relative energies of small boron clusters obtained by density functional theory since such small clusters and similar motifs are likely present in crystal nuclei and at the interface of growing crystals. Using a training set of B<sub>80</sub> clusters, including a low-energy core–shell structure containing a B<sub>12</sub> icosahedron core and a high-energy single-shell structure produced in preliminary ReaxFF simulations, the ReaxFF parameter set was refined to better reproduce energies calculated by density functional theory (DFT). Among existing ReaxFF parameter sets and the machine-learning interatomic potentials MACE-MP-0, MACE-MP-0b3, MACE-MPA-0, PFP v7.0.0, and SevenNet-MF-ompa, only our new parameter set and PFP v7.0.0 correctly ranked these B<sub>80</sub> clusters. This refinement led to improved agreement with DFT for a test set of 58 clusters consisting of 8–103 boron atoms. Furthermore, our refined parameter set yielded greater local icosahedral structure than the previously existing ReaxFF parameter set for larger scale simulations of crystallization from supercooled liquid boron. Additionally, simulations of solid boron in contact with molten nickel using our refined ReaxFF parameters yielded a boron solubility value that agrees moderately well with experimental expectations, while the previous boron parameters gave a value that was much too low. ReaxFF ; Icosahedral symmetry ; Density functional theory ; Boron ; Molecular dynamics ; Nucleation Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Machine Learning in Materials Science
Authors: Zlatar, Matija
Source: CER (University of Belgrade, Institute of Chemistry, Technology and Metallurgy) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Reproducibility as a Foundation for Reliable Computational Quantum Chemistry Data Artificial intelligence (AI) and machine learning (ML) are transforming spectroscopy and chemical physics, yet their success depends on the availability of reliable and reproducible data. Computational quantum chemistry provides a powerful source of such data when workflows are transparent, systematically benchmarked, and rigorously documented. Reproducible protocols ensure that calculated spectra and molecular properties can be trusted, reused, and integrated into AI/ML models without hidden biases and inconsistencies. By embedding reproducibility into computational practice, quantum chemical results become trustworthy resources that can be directly reused, compared, and expanded within machine learning frameworks. Beyond technical rigor, reproducibility is framed as a driver of equity and participation: by lowering barriers to entry, reproducible workflows empower broader engagement in global science. Reproducibility is not only a methodological necessity but also a cultural transformation, aligning with open science principles and the UN Sustainable Development Goals. Reproducibility ; Workflow ; Trustworthiness ; Equity (law) ; Foundation (evidence) ; Quantum chemical ; Quantum computer ; Embedding Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Santhoshkumar Bhogi | Padmavathi Vustelamuri | S. Suganya | Uma Varma | Utpal Nath | Raji Thomas
Source: Oriental Journal Of Chemistry (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Crystallography and it's Role in Molecular Structures Crystallography has proven to be among the strongest methods of atomic precise intelligibility of the molecular structures. The spatial structure of atoms, bond lengths and conformational dynamics can be solved by examining how X-rays, neutrons, or electrons, interact with crystalline samples, by analyzing the diffraction patterns. This approach has produced revolutionary effects in chemistry, biology, materials science and drug design, leading to the discovery of the structure of DNA in two strands (the double-helix) and the structure of complex protein folding. Not only does crystallography contribute to the theoretical understanding, but also has practical uses in pharmaceuticals, nanotechnology and crystallographic informatics. Nonetheless, the industry is limited to the fact that high-quality crystals are needed, the dynamic or non-crystalline or amorphous materials are not widely applicable, and dynamic systems cannot be studied. The next steps include combining crystallography with the related technique such as the use of a cryo-electron microscope, the use of machine learning-based structure prediction, and in situ crystallography to address these limitations and expand its use in practical problem solving. Crystal structure ; Theoretical chemistry ; Diffraction ; X-ray crystallography ; Amorphous solid ; Supramolecular chemistry ; Structural biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Physical and Chemical Molecular Interactions
Authors: Manos Kirtas | Loukia Avramelou | Carlos Alonso‐Ramos | Sara Rodrı́guez | Aitor V. Velasco | N. Passalis | C. Pappas | Nikos Pleros | Anastasios Tefas
Source:  (article, 2025-03-17)
Field: Chemistry — Spectroscopy

Optics-informed deep learning training for classifying gas absorption spectra on the edge In recent years, the analysis of gases in the air has been crucial, as sensing and monitoring are essential for understanding air quality. However, air monitoring is challenging since it requires battery powered components due to the necessity of deploying sensors and processors in a distributed manner outside with limited power resources. This fueled the development of miniaturized silicon photonic spectrometers, which are able to extract gas absorption spectra with minimum power consumption. However, such analog devices are sensitive to fabrication imperfections, environmental conditions, and temperature, making their use impractical outside of laboratory conditions. Recent approaches apply spatial heterodyne Fourier transform (SHFT) spectrometers enabling the passive calibration with Deep Learning techniques. Although deep learning models are known for their ability to learn the underlying behaviour from noisy data, their complexity hinders their application when lower power consumption is required. To this end, neuromorphic photonics promises several advantages in the deployment of neural networks, enabling high-speed operations with low power consumption. Integrating neural networks into photonic hardware leads to additional training challenges, due to the easily saturated photonic activation functions. In this work, we propose an optics-informed training pipeline for classifying gas absorption spectra utilizing photonic neural networks, taking into account their transfer functions. We provide an extensive experimental study that shows the potential ability of photonic neural networks to be deployed in such scenarios, demonstrating the usage of various photonic activation functions. Enhanced Data Rates for GSM Evolution ; Training (meteorology) ; Absorption (acoustics) ; Computer science ; Artificial intelligence ; Absorption spectroscopy ; Absorption edge ; Spectral line ; Optics ; Materials science ; Optoelectronics ; Physics ; Astronomy Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Air Quality Monitoring and Forecasting