jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Наталя Гончарова | Johannes Hoja
Source: arXiv (Cornell University) (preprint, 2024-10-14)
Field: Chemistry — Inorganic Chemistry

Nudged-Elastic Band Calculations of Polymorph Transitions and Solid-State Reactions in Molecular Crystals The modeling of solid-state transformations, such as polymorphic transitions and chemical reactions in molecular crystals, is vital for many applications including drug design or the development of new synthesis methods. However, a description via nudged-elastic band (NEB) calculations faces several crucial challenges. First, the automatic initial pathway generation based on a linear interpolation often fails for periodic systems, leading to unrealistic geometries and atomic collisions. Second, the necessary system sizes are typically beyond the scope of density functional theory (DFT) calculations in terms of computational cost, but the associated accuracy is vitally needed. To address these issues, we introduce a hybrid interpolation method that combines linear interpolation for cell parameters with spherical linear interpolation (SLERP) for molecular structures or intramolecular fragments, ensuring smooth and realistic transitions. Moreover, we train and benchmark machine-learned force fields (MLFFs) based on the SO3krates equivariant neural network architecture to accelerate NEB calculations while retaining near-DFT accuracy. We apply our approach to two polymorph transitions and a solid-state Diels-Alder reaction and show that our new interpolation method reliably produces viable initial pathways. The MLFFs are trained on PBE+MBD reference data and reproduce DFT lattice energies with a mean absolute error of 0.4 kJ/mol along the minimum-energy paths. These results highlight the potential of combining advanced interpolation techniques with MLFFs to enable automated, accurate, and efficient exploration of solid-state transformations in molecular crystals. Solid-state ; Materials science ; Chemical physics ; Crystallography ; State (computer science) ; Condensed matter physics ; Molecular physics ; Physical chemistry ; Chemistry ; Physics ; Computer science Inorganic Fluorides and Related Compounds ; Solid-state spectroscopy and crystallography ; Chemical Thermodynamics and Molecular Structure
Authors: Yingqi Zhao | Kang Zhan | Pei-Lin Xin | Zaoping Chen | Shuai Li | Francesco De Angelis | Jian‐An Huang
Source: arXiv (Cornell University) (preprint, 2024-12-25)
Field: Chemistry — Analytical Chemistry

Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy. Proline ; Chemistry ; Molecule ; Biophysics ; Computer science ; Biochemistry ; Biology ; Amino acid ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mass Spectrometry Techniques and Applications
Authors: S.Silvia Priscila | Deepak K. Sinha | Biswaranjan Senapati | S. Suman Rajest | Debabrata Datta | S. Seema
Source: International Journal of Critical Infrastructures (article, 2024-03-23)
Field: Materials Science — Materials Chemistry

Ensemble Machine Learning Regression Technique to Select the Type of Concrete as Radiation Shielding Material Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas. Radiation shielding ; Regression analysis ; Electromagnetic shielding ; Engineering ; Computer science ; Forensic engineering ; Machine learning ; Electrical engineering Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies
Authors: Jiexin Shi | Rachel B. Getman
Source: ChemRxiv (preprint, 2024-10-23)
Field: Materials Science — Materials Chemistry

Prediction of Hydration energies of Adsorbates at Pt(111) and Liquid Water Interfaces using Machine Learning Aqueous-phase heterogeneous catalysis has many applications, including biomass reforming, Fischer-Tropsch synthesis, and electrocatalysis. Developing accurate models for these systems is essential for gaining mechanistic understanding and making predictions of activity and selectivity under reaction conditions. However, molecular modeling of solid-liquid interfaces is computationally demanding. To address this, we carried out machine learning analysis on an existing dataset comprising energies and free energies of solvation for 90 adsorbates on a Pt(111) surface. These adsorbates include intermediates from the decomposition of methane, methanol, ethylene glycol, and glycerol. We investigated the structure-property relationship by combining molecular descriptors with machine learning models. Eight machine learning approaches were compared. In general, machine learning models outperform molecular dynamics for computing the same properties and achieve RMSE < 0.1 eV for predicting the energies and free energies of solvation, which is within the standard error within the original dataset. R2 for energies of solvation are in general above our threshold value of 0.8 but only 0.72 for free energies of solvation. To achieve better regression for free energies of solvation, the dataset should be expanded. However, our machine learning model still outperforms molecular dynamics for predicting free energies of solvation. Feature importance analysis shows that while hydrogen bonds between water and the adsorbates contribute most strongly to machine learning model performance, a combination of different types of features is important to achieve strong predictive performance. Liquid water ; Materials science ; Thermodynamics ; Physics Machine Learning in Materials Science
Authors: Johannes Sandberg
Source: elib (German Aerospace Center) (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Atomic Scale Simulations for Liquid Metals and Alloys : Machine Learning Potentials and Feature Selection Comprendre la relation entre les interactions à l’échelle atomique et les propriétés macroscopiques des matériaux est l’un des objectifs principaux en science des matériaux. Dans ce but, les simulations de dynamique moléculaire sont un outil puissant, permettant de suivre directement la trajectoire de phase des atomes et sont de ce fait bien adaptées pour les processus dynamiques tels que la diffusion et les premiers stades de la crystallisation. Cette approche nécessite cependant une modélisation précise des interactions entre les atomes. Les potentiels interatomiques empiriques sont limités en précision et reproduisent souvent assez mal certains résultats expérimentaux. Les simulations ab initio, fondés sur une description quantique des interactions, est sévèrement limitée en termes de taille et de temps simulés, ce qui limite son utilisation pour de nombreuses applications. Au cours de la dernière décennie, les potentiels basés sur apprentissage machine, entrainés sur des données de simulation ab initio, sont devenus une méthode importante pour permettre des simulations de grande ampleur avec une précision proche des calculs ab initio. Néanmoins, les méthodes ab initio reposent sur des approximations, nécessitant à un certain point une connexion aux résultats expérimentaux.Dans cette thèse, je développe un potentiel par apprentissage machine pour pour les métaux purs et l'alliage binaire liquide Al-Ni. Pour ce faire, une procédure qui va de la construction de l'ensemble de données d'entraînement à la conception et la formation du potentiel au moyen d’un réseau de neurones de haute dimension proposé par Behler-Parrinello. Les potentiels sont validés par comparaison avec l’expérience et appliqués à l'étude de la nucléation cristalline homogène à partir du liquide en surfusion, par simulation de dynamique moléculaire de grande échelle, bien au-delà de la portée de la simulation ab initio. Un résultat significatif de ce travail est l'élucidation des origines du chemin de nucléation dans la phase cubique centrée B2 de Al-Ni à la composition équiatomique. Un accent particulier est également mis sur le développement d'une méthode de sélection adaptative des descripteurs pour cette approche appelée adaptative group lasso afin de permettre la construction de potentiels plus efficaces et plus explicables. Dans ce cadre, un potentiel pour le bore, qui sert de système modèle particulièrement complexe, utile pour l'évaluation des descripteurs et l'apprentissage machine pour les potentiels. Statistical analysis ; Liquid phase ; Context (archaeology) Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Songyou Wang | Kai Chen | Riyi Yang | Zhefeng Wang | Wuyan Zhao | Youmin Xu | Huaijun Sun | Chao Zhang | Kai‐Ming Ho | C. Z. Wang | Wan-Sheng Su
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A New Accurate, Efficient and Transferable Deep Learning Interatomic Potential for Carbon with Advanced Applications Interatomic potential ; Computer science ; Carbon fibers ; Nanotechnology ; Materials science ; Engineering physics ; Chemistry ; Physics ; Molecular dynamics ; Algorithm ; Computational chemistry Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Ralf Meyer | Daniel B. K. Chu | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2024-10-12)
Field: Materials Science — Materials Chemistry

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, our molecular graph featurization method for machine learning various spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used machine learning methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary transition metal complexes, the best MBE models achieve mean absolute errors of 2.75 kcal/mol on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30-40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit mean absolute errors of 4.00 kcal/mol (i.e., a 0.73 kcal/mol reduction) on the spin-splitting energies and 0.53 eV (i.e., a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces. Octahedron ; Transition (genetics) ; Transition metal ; Computer science ; Materials science ; Crystallography ; Chemistry ; Crystal structure Machine Learning in Materials Science
Authors: Vladimir Shirobokov | Grigory V. Karsakov | Valentin A. Milichko
Source:  (article, 2024-04-05)
Field: Chemistry — Inorganic Chemistry

Exploring metal-organic framework phase change materials via machine learning approach Phase (matter) ; Computer science ; Artificial intelligence ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning and ELM ; Advanced Battery Technologies Research
Authors: Wissam A. Saidi
Source:  (article, 2024-08-18)
Field: Materials Science — Materials Chemistry

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations. Computer science Machine Learning in Materials Science
Authors: Hermin Hardyanti Utami | Muhammad Aqdar Fitrah | Yusriadi Yusriadi | Ardiansah Ardiansah | Arminas Arminas | Mega Fia Lestari | Sherly Towolioe
Source: Jurnal Pena Sains (article, 2024-10-31)
Field: Materials Science — Polymers and Plastics

MACHINE LEARNING MODELS FOR PREDICTING STRESS VALUE IN THE TENSILE STRENGTH OF BIOFILMS FROM STARCH AND HAIR WASTE Biofilms, structured communities of microorganisms, have emerged as a subject of significant interest across various industries due to their unique biodegradable and sustainable characteristics. Hair waste is an incredibly rich source of keratin, and this abundance makes it a promising candidate as a fundamental building block for the development of biodegradable plastics. This study focuses on sustainable biofilms derived from biodegradable materials, specifically a unique combination of starch and hair waste. Machine Learning models, implemented in RapidMiner, were utilized to predict the tensile strength of these biofilms, with the goal of enhancing quality control in their production. Neural Networks and Deep Learning methods were employed to compare their predictive capabilities, assessing both their strengths and limitations. Through rigorous data collection, feature identification, and detailed data analysis, critical factors influencing the quality of the biofilms were identified. The results revealed the remarkable predictive accuracy of the Neural Net model, particularly for Ratio 40, while the performance of the Deep Learning model varied across different ratios. The lower RMSE of the Neural Net model indicated a more precise alignment between the predicted and actual values, distinguishing it as the superior model. This research contributes to the advancement of sustainable biofilm development, offering eco-friendly solutions through the use of unconventional materials. Both models offer valuable predictive capabilities, and the choice between them may depend on the specific requirements and contexts of the application. In conclusion, the performance of the Neural Net and Deep Learning models in predicting stress in tensile strength varies across different ratios. Ultimate tensile strength ; Starch ; Stress (linguistics) ; Materials science ; Composite material ; Food science ; Chemistry Textile materials and evaluations
Authors: Johannes Drechsel | Henry Barth | Lars Rebenklau
Source: IMAPSource Proceedings (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Partial Discharge Characterization of Ceramic Power Electronics Circuit Carriers Assisted by Machine Learning This paper presents an approach for transferring knowledge about partial discharges in polymer insulators to ceramic insulators with the aid of machine learning. It is shown how various machine-learnable features can be generated from partial discharge measurement data and processed in varying artificial neural networks for classification. It is found that polymer- based partial discharges can be classified using this method. In addition, the Long Short-Term Memory based artificial neural network enables partial discharge cause finding and thus fault detection in ceramic power electronics substrates. Partial discharge ; Artificial neural network ; Ceramic ; Characterization (materials science) ; Electronics ; Fault (geology) ; Power (physics) ; Computer science ; Power electronics ; Electrical engineering ; Materials science ; Artificial intelligence ; Electronic engineering ; Engineering ; Voltage ; Nanotechnology ; Composite material ; Physics ; Geology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: M. Wolińska | Aron Walsh | Antoine Cully
Source: arXiv (Cornell University) (preprint, 2024-03-06)
Field: Materials Science — Materials Chemistry

Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} algorithms to the field of crystal structure prediction. The objective of these algorithms is to identify a diverse set of high-performing solutions, which has been successful in a range of fields such as robotics, architecture and aeronautical engineering. As these methods rely on a high number of evaluations, we employ machine-learning surrogate models to compute the interatomic potential and material properties that are used to guide optimisation. Consequently, we also show the value of using neural networks to model crystal properties and enable the identification of novel composition--structure combinations. In this work, we specifically study the application of the MAP-Elites algorithm to predict polymorphs of TiO$_2$. We rediscover the known ground state, in addition to a set of other polymorphs with distinct properties. We validate our method for C, SiO$_2$ and SiC systems, where we show that the algorithm can uncover multiple local minima with distinct electronic and mechanical properties. Diversity (politics) ; Property (philosophy) ; Space (punctuation) ; Algorithm ; Quality (philosophy) ; Computer science ; Physics ; Political science ; Epistemology Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: А.Б. Лаптев | A. M. Kogan | E.V. Nikolaev | А.А. Рогачев | Zh. V. Ihnatovich | Yu. V. Matveenko
Source: Proceedings of the National Academy of Sciences of Belarus Physical-Technical Series (article, 2024-04-03)
Field: Materials Science — General Materials Science

Mathematical modeling of the creation process and aging of polymer composite materials Based on the analysis of the literature on the possibility of using neural networks to create new materials with high functional properties, the article considers a solution to the problem of determining the operational stability of polymeric composite materials by creating physical and chemically sound mathematical prediction models. Epoxy resins of the UP-637 and EA brands with an isophorone diamine hardener were chosen as the matrix of the model composite material, and oligobutadiene rubber of the SKN-10 KTR brand was chosen as the modifier. It justifies directions of work necessary for development of new materials creation methodology with optimal characteristics, building a model for changing the properties of materials at variation of composition and implementation of full-scale mathematical modeling of physical and chemical processes of polymer composite materials aging at changing level and time of climatic factors influence. Verification of the obtained dependence of service characteristics on the composition of the material and the level of influencing climatic factors was carried out on the basis of data from full-scale tests in a temperate climate. The proposed methodology for modelling the properties of polymer composite materials will reduce the development time of new materials and allow creation of polymer composites based on epoxy resin containing fillers of various natures (carbon, mineral and polymer) with high performance parameters. Composite number ; Process (computing) ; Materials science ; Polymer ; Polymer science ; Composite material ; Process engineering ; Computer science ; Engineering Material Properties and Applications ; Innovations in Concrete and Construction Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Weiyi Xia | Wei-Shen Tee | P. C. Canfield | F. Garcia | Raquel A. Ribeiro | Yongbin Lee | Liqin Ke | Rebecca Flint | Cai‐Zhuang Wang
Source: arXiv (Cornell University) (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X (X=Bi, Pb, In and Ag) ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance of Ce-based intermetallic compounds in technological applications. Ternary operation ; Computer science ; Artificial intelligence ; Machine learning ; Programming language Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Computational Drug Discovery Methods