jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marina S. Leite
Source:  (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Accelerating the understanding of halide perovskites through machine learning Machine learning (ML) is a powerful tool to accelerate the development of halide perovskite materials and devices. We apply ML models varying from echo state networks to statistical models to classify and predict physical properties such as hole transport layer electrical conductivity, halide perovskite photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use in situ environmental optical measurements to predict the optical behavior of Cs-FA perovskites for 50+ hours, upon materials’ exposure to moisture. Halide ; Perovskite (structure) ; Computer science ; Materials science ; Optoelectronics ; Chemistry ; Chemical engineering ; Engineering ; Inorganic chemistry Machine Learning in Materials Science ; Expert finding and Q&A systems ; Seismology and Earthquake Studies
Authors: Mohd Nor Azmi Ab Patar | Nur Aini Sabrin Manssor | M. R. Isa | Nur Auni Izzati Jusoh | Mohd Juzaila Abd Latif | Praveena Nair Sivasankaran | Jamaluddin Mahmud
Source: BioResources (article, 2024-04-17)
Field: Materials Science — Polymers and Plastics

Quantifying and predicting the tensile properties of silicone reinforced with Moringa oleifera bark fibers, To obtain a better understanding of using Moringa oleifera bark (MOB) as a reinforcement in a silicone matrix, this study aimed to define the mechanical properties of this new material under uniaxial tension. Composite samples of 0 wt%, 4 wt%, 8 wt%, 12 wt%, and 16 wt% MOB powder were produced. The tensile properties were quantified mathematically using the neo-Hookean hyperelastic model. The collected data were employed to establish multiple inputs of an artificial neural network (ANN) to predict its material constant via MATLAB. The result showed that the material constant for the 16 wt% fiber content sample was 63.9% higher than pure silicone. This was supported by the tensile modulus testing, which indicated that the modulus increased as the fiber content increased. However, the elongation ratio (λ) of the MOB-silicone biocomposite decreased slightly compared to the pure silicone. Lastly, the prediction of the material constant using an ANN recorded a 2.03% percentage error, which showed that it was comparable to the mathematical modelling. Therefore, the inclusion of MOB fibers into silicone produced a stiffer material and gradually improved the composite. Furthermore, the network that had multiple inputs (weighting, load, and elongation) was more reliable to produce precise predictions. Moringa ; Ultimate tensile strength ; Materials science ; Composite material ; Bark (sound) ; Silicone ; Pulp and paper industry ; Traditional medicine ; Biology ; Medicine ; Engineering ; Ecology Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials
Authors: Ashwini Verma | Kavita Joshi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Pctpro: A Machine Learning Model for Rapid Prediction of Pressure-Composition-Temperature (Pct) Isotherms Download ; Computer science ; Composition (language) ; Artificial intelligence ; World Wide Web ; Art Machine Learning in Materials Science ; thermodynamics and calorimetric analyses ; Phase Equilibria and Thermodynamics
Authors: Wise Herowati | Muhamad Akrom | Novianto Nur Hidayat | T. Sutojo
Source: Journal of Multiscale Materials Informatics (article, 2024-04-29)
Field: Chemistry — Organic Chemistry

Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces. Quinoline ; Pyridine ; Corrosion ; Chemistry ; Combinatorial chemistry ; Nuclear chemistry ; Computer science ; Materials science ; Organic chemistry Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods ; Synthesis of Tetrazole Derivatives
Authors: Ivan Dudakov | С. В. Савельев | Iurii M. Nevolin | Artem Mitrofanov | Vadim Korolev | Yulia G. Gorbunova
Source: ChemRxiv (preprint, 2024-11-25)
Field: Chemistry — Inorganic Chemistry

Examining proton conductivity of metal-organic frameworks by means of machine learning The tunable structure of metal–organic frameworks (MOFs) is an ideal platform to meet contradictory requirements for proton exchange membranes: a key component of fuel cells. Nonetheless, rational design of proton-conducting MOFs remains a challenge owing to the intricate structure–property relationships that govern the target performance. In the present study, the modeling of quantities available for hundreds of MOFs was scaled up to many thousands of entities using supervised machine learning. The experimental dataset was curated to train multimodal transformer-based networks, which integrated crystal-graph, energy grid, and global-state embeddings. Uncertainty-aware models revealed superprotonic conductors among synthesized MOFs that have not been previously investigated for the application in question, thus highlighting magnesium-containing frameworks with aliphatic linkers as high-confidence candidates for experimental validation. Furthermore, classifiers trained on the activation energy threshold effectively discriminated between well-known proton conduction mechanisms, thereby providing physical insights beyond the black-box routine. Thus, our findings prove high potential of data-driven materials design, which is becoming a valuable addition to experimental studies on proton-conducting MOFs. Proton ; Conductivity ; Metal-organic framework ; Metal ; Artificial intelligence ; Materials science ; Computer science ; Machine learning ; Chemistry ; Physics ; Metallurgy ; Organic chemistry ; Nuclear physics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Kehan Guo | Yili Shen | Gisela Abigail Gonzalez-Montiel | Yi Huang | Yujun Zhou | Mihir Surve | Zhichun Guo | Payel Das | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source:  (article, 2024-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction To Generation and Beyond The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data—termed Spectroscopy Machine Learning (SpectraML)—remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy—from early pattern recognition to the latest foundation models capable of advanced reasoning—and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions like synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we release an open-source repository containing curated datasets and code implementations. Our survey serves as a roadmap for researchers, guiding advancements at the intersection of spectroscopy and AI. Intersection (aeronautics) ; Key (lock) ; TRACE (psycholinguistics) ; Taxonomy (biology) ; Applications of artificial intelligence Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Water Quality Monitoring and Analysis
Authors: Avishek Singh | Nirmal Ganguli
Source: arXiv (Cornell University) (preprint, 2024-05-24)
Field: Materials Science — Materials Chemistry

Unsupervised Deep Neural Network Approach To Solve Fermionic Systems Solving the Schrödinger equation for interacting many-body quantum systems faces computational challenges due to exponential scaling with system size. This complexity limits the study of important phenomena in materials science and physics. We develop an Artificial Neural Network (ANN)-driven algorithm to simulate fermionic systems on lattices. Our method uses Pauli matrices to represent quantum states, incorporates Markov Chain Monte Carlo sampling, and leverages an adaptive momentum optimizer. We demonstrate the algorithm's accuracy by simulating the Heisenberg Hamiltonian on a one-dimensional lattice, achieving results with an error in the order of $10^{-4}$ compared to exact diagonalization. Furthermore, we successfully model a magnetic phase transition in a two-dimensional lattice under an applied magnetic field. Importantly, our approach avoids the sign problem common to traditional Fermionic Monte Carlo methods, enabling the investigation of frustrated systems. This work demonstrates the potential of ANN-based algorithms for efficient simulation of complex quantum systems, opening avenues for discoveries in condensed matter physics and materials science. Artificial neural network ; Deep neural networks ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Nicolas Sadoune | Ke Liu | Han Yan | Ludovic D. C. Jaubert | Nic Shannon | Lode Pollet
Source: arXiv (Cornell University) (preprint, 2024-02-16)
Field: Materials Science — Materials Chemistry

Human-machine collaboration: ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice Machine learning algorithms thrive on large data sets of good quality. Here we show that they can also excel in a typical research setting with little data of limited quality, through an interplay of insights coming from machine, and human researchers. The question we address is the unsolved problem of ordering out of a spin-liquid phase described by an emergent rank-2 U(1) gauge theory, as described by [H. Yan et al., Phys. Rev. Lett. 124, 127203 (2020)]. Published Monte Carlo simulations for this problem are consistent with a strong first-order phase transition, but were too noisy for the form of low-temperature order to be identified. Using a highly-interpretable machine learning approach based on a support vector machine with a tensorial kernel (TKSVM), we re-analyze this Monte Carlo data, gaining new information about the form of order that could in turn be interpreted by traditionally-trained physicists. We find that the low-temperature ordered phase is a form of magnetic order analogous to a smectic liquid crystal. This arises due to a subtle thermal order-by-disorder mechanism, that can be understood from the fluctuations of the tensor electric field of the parent rank-2 gauge theory. These results were obtained by a back-and-forth process which closely resembles a collaboration between human researchers and machines. We argue that this "collaborative" approach may provide a blueprint for solving other problems that have not yielded to human insights alone. Phase transition ; Computer science ; Artificial intelligence ; Lattice (music) ; Monte Carlo method ; Statistical physics ; Machine learning ; Tensor (intrinsic definition) ; Physics ; Theoretical physics ; Algorithm ; Condensed matter physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Expert finding and Q&A systems
Authors: Kurniadinur Kurniadinur | Teuku Rizky Noviandy | Ghazi Mauer Idroes | Noor Atinah Ahmad | Irvanizam Irvanizam | Muhammad Subianto | Rinaldi Idroes
Source: Infolitika Journal of Data Science (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research. Kovats retention index ; Artificial neural network ; Index (typography) ; Particle swarm optimization ; Essential oil ; Chromatography ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Gas chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Mansoor Ani Najeeb Nellikkal | Ahmed H. Mokhtar | David Serban | D. G. Porter | Stephen P. Collins | A. Bombardi | Mary Newton
Source: physica status solidi (a) (article, 2024-11-06)
Field: Materials Science — Polymers and Plastics

Three‐Dimensional Imaging of the Structural Phase Transition in a Single Vanadium Dioxide Nanocrystal Vanadium dioxide (VO 2 ) is a strongly correlated material that exhibits a number of structural phase transitions (SPT) near to room temperature of considerable utility for various technological applications. When reduced to the nanoscale, a foreknowledge of surface and interface properties of VO 2 during the SPT can facilitate the development of devices based on VO 2 . Herein, it is shown that Bragg coherent X‐ray diffractive imaging (BCDI) combined with machine learning is an effective means to recover three‐dimensional images of a single VO 2 nanocrystal during a temperature‐induced SPT from a room‐temperature monoclinic phase to a high‐temperature rutile phase. The findings reveal the coexistence of multiple phases within the nanocrystal throughout the transition, along with missing density which indicates the presence of a newly formed rutile phase. Nanocrystal ; Vanadium dioxide ; Phase transition ; Vanadium ; Materials science ; Nanotechnology ; Phase (matter) ; Chemistry ; Metallurgy ; Condensed matter physics ; Thin film ; Physics ; Organic chemistry Transition Metal Oxide Nanomaterials ; Ga2O3 and related materials ; Catalysis and Oxidation Reactions
Authors: Jordan S. Dialpuri | Jon Agirre | Kevin Cowtan | Paul S. Bond
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

<i>NucleoFind</i>: a deep-learning network for interpreting nucleic acid electron density Nucleic acid electron density interpretation after molecular replacement remains a difficult problem for computer programs to deal with.Programs tend to rely on time-consuming and computationally exhaustive searches to recognise characteristic features.We present NucleoFind, a deep-learning-based approach to interpreting and segmenting electron density.Using an electron density map from X-ray crystallography, the positions of the phosphate group, sugar ring and nitrogenous base group can be predicted with high accuracy.On average, 84 % of phosphate atoms, 86 % of sugar atoms and 90 % of base atoms are located by the models in maps produced following protein molecular replacement.The wealth of context these predicted maps provide can then be used to automatically build more accurate and complete nucleic acid molecular models in a short amount of time. Nucleic acid ; Computer science ; Artificial intelligence ; Chemistry ; Computational biology ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Martin Kubáň | Santiago Rigamonti | Claudia Draxl
Source: arXiv (Cornell University) (preprint, 2024-03-15)
Field: Materials Science — Materials Chemistry

MADAS -- A Python framework for assessing similarity in materials-science data Computational materials science produces large quantities of data, both in terms of high-throughput calculations and individual studies. Extracting knowledge from this large and heterogeneous pool of data is challenging due to the wide variety of computational methods and approximations, resulting in significant veracity in the sheer amount of available data. Here, we present MADAS, a Python framework for computing similarity relations between material properties. It can be used to automate the download of data from various sources, compute descriptors and similarities between materials, analyze the relationship between materials through their properties, and can incorporate a variety of existing machine learning methods. We explain the design of the package and demonstrate its power with representative examples. Python (programming language) ; Computer science ; Similarity (geometry) ; Data science ; Programming language ; Artificial intelligence Machine Learning in Materials Science
Authors: Liangmin Wang | Jinyan Lang | Zi'ang Xia | Baoming Xu | Xinhui Wang | Heng Zhang
Source: Polymers for Advanced Technologies (article, 2024-06-01)
Field: Materials Science — Polymers and Plastics

Modeling and prediction of flame retardant performance of alkyl phosphinate using artificial neural network and adaptive neural fuzzy inference system Abstract The flame retardancy of polymer materials is closely linked to the types and compositions of flame retardants used. However, due to the complex relationships involved, it is difficult to quantify and predict flame retardancy using linear equations and models. Alkyl phosphinate flame retardants have emerged as promising candidates for their environmentally friendly, low toxicity, and excellent flame retardant properties. In this study, we aimed to develop a novel, low‐cost alkyl phosphinate flame retardant by predicting the quantitative impact of flame retardant composition on flame retardancy. To establish prediction models for the limiting oxygen index (LOI) value of alkyl phosphinate flame retardants, we utilized artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) programs, namely ANN, ANFIS‐grid partition (GP), and ANFIS‐subtractive clustering (SC). The prediction models were trained and validated, and their reliability was assessed based on the root mean square error (RMSE), coefficient of determination ( R 2 ), and mean absolute percentage error (MAPE) criteria. Our results indicate that the ANFIS‐SC prediction model exhibits the highest reliability, with high accuracy and ideal prediction results. Phosphinate ; Artificial neural network ; Adaptive neuro fuzzy inference system ; Fire retardant ; Materials science ; Alkyl ; Inference ; Neuro-fuzzy ; Artificial intelligence ; Fuzzy logic ; Machine learning ; Computer science ; Fuzzy control system ; Organic chemistry ; Composite material ; Chemistry Flame retardant materials and properties ; Fire dynamics and safety research
Authors: Qiyu Yang | Congzi Xia | Yuanwu Cui | Xiaoqiuyan Zhang | Tianyu Zhang | Yong Huang | Diwei Liu | Yanyu Wei | Yubin Gong | Guangyou Fang | Zhenyou Wang | XueQuan Chen | Min Hu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fusion of Terahertz Spectroscopy and Raman Spectroscopy Combined with Support Vector Machine to Distinguish Different Pericarpium Citri Reticulatae Species Raman spectroscopy ; Spectroscopy ; Terahertz spectroscopy and technology ; Terahertz radiation ; Support vector machine ; Analytical Chemistry (journal) ; Fusion ; Materials science ; Chemistry ; Physics ; Optics ; Computer science ; Artificial intelligence ; Optoelectronics ; Chromatography ; Astronomy ; Philosophy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhen Zhang | Z Y Zhang | Juan Du | Julian Cheng | Ping Chen | Yanli Ma | C T Wang
Source: Journal of Physics Conference Series (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Prediction of fragment impact energy release overpressure based on machine learning Abstract The overpressure within a quasi-sealed chamber serves as a crucial metric for assessing the energy release behavior and impact potential of energetic fragments. Combining one-dimensional shock wave theory and material microparameters, different machine learning models were trained to predict the overpressure generated by different energetic fragments. The final results demonstrate that the RF model achieved the highest prediction accuracy, with an RMSE error of 0.0251 MPa. Notably, the mathematical expression generated by symbolic regression only incorporated four features. After iterative training of symbolic regression using four features, the prediction accuracy was notably higher. This accuracy surpassed that of the DT model and KNN model, which were trained with eight features. However, it still fell slightly below the accuracy of the GBDT model, which was also trained with eight features. Overpressure ; Fragment (logic) ; Energy (signal processing) ; Impact energy ; Environmental science ; Computer science ; Artificial intelligence ; Engineering ; Mathematics ; Structural engineering ; Physics ; Statistics ; Nuclear physics ; Algorithm High-Velocity Impact and Material Behavior ; Transportation Safety and Impact Analysis ; Nuclear Materials and Properties