jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Clelia Middleton | Basile F. E. Curchod | Thomas J. Penfold
Source: ChemRxiv (preprint, 2024-02-09)
Field: Materials Science — Materials Chemistry

Partial Density of States Representation for Accurate Deep Neural Network Predictions of X-ray Spectra The performance of a Machine Learning (ML) algorithm for chemistry is highly contingent upon the architect’s choice of input representation. This work introduces the partial density of states (p-DOS) descriptor: a novel, quantum- inspired structural representation which encodes relevant electronic information for machine learning models seeking to simulate X-ray spectroscopy. p-DOS uses a minimal basis set in conjunction with a guess (non-optimised) electronic configuration to extract and then discretise the density of states (DOS) of the absorbing atom to form the input vector. We demonstrate that while the electronically-focused p-DOS performs well in isolation, optimal performance is achieved when supplemented with nuclear structural information imparted via a geometric representation. p-DOS provides a description of the key electronic properties of a system which is not only concise and computationally efficient, but also independent of molecular size or choice of basis set. It can be rapidly generated, facilitating its application with large training sets. Its performance is demonstrated using a wide variety of examples at the sulphur K-edge, including the prediction of ultrafast X-ray spectroscopic signal associated with photoexcited 2(5H)-thiophenone. These results highlight the potential for ML models developed using p-DOS to contribute to the interpretation and predic- tion of experimental results made possible by emergent cutting-edge technologies, especially X-ray free electron lasers. Representation (politics) ; Artificial neural network ; Spectral line ; Statistical physics ; Physics ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Political science ; Quantum mechanics ; Law Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Bingyu Guo | Zehui Cheng | Shuangqi Hu
Source: Heliyon (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Neural network-based prediction of auto-ignition temperature of ternary mixed liquids Auto-ignition temperature (AIT) is one of the crucial exponents in the design of fire and explosion safety measures. Therefore, in this study, quantitative structure-property relationship approach was used to predict the AIT of ternary hybrid liquids based on molecular structure information. The optimal molecular descriptors were calculated and filtered using Mordred software. Twelve mixing rules were proposed for calculating molecular descriptors of mixtures. A prediction model for the AIT value of binary liquid mixtures was developed, validated and evaluated using a back propagation neural network (BPNN) and a one-dimensional convolutional neural network (1DCNN). The relative contribution and positive and negative correlations between individual molecular descriptors and AIT in the model were interpreted using the shapley additive explanations method. The results show that BPNN and 1DCNN models using mixing rule 1 have the best fitting ability, stability and prediction ability. The determination coefficient of the BPNN and 1DCNN models in the training set were 0.996 and 0.992, the root mean square errors were 3.613 °C and 5.284 °C, the mean absolute errors were 2.483 °C and 4.144 °C, the nash efficiency coefficient was 0.996 and 0.992, respectively, the willmott index was 0.999 and 0.998. and the values of the top three molecular descriptors of relative contribution, SssCH<sub>2</sub>, SsOH and SsCH<sub>3</sub>, were negatively correlated with the AIT values. The BPNN and 1DCNN models provide an accurate and reliable method for predicting ternary mixing liquid AIT. Ternary operation ; Artificial neural network ; Mathematics ; Mean squared error ; Mixing (physics) ; Molecular descriptor ; Binary number ; Root mean square ; Correlation coefficient ; Biological system ; Computer science ; Thermodynamics ; Materials science ; Artificial intelligence ; Statistics ; Quantitative structure–activity relationship ; Machine learning ; Physics Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Free Radicals and Antioxidants
Authors: Siti Mariatul Hazwa Mohd Huzir | Saıful Nizam Tajuddin | Zakiah Mohd Yusoff | Nurlaila Ismail | Ali Abd Almisreb | Mohd Nasir Taib
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-01-26)
Field: Chemistry — Organic Chemistry

Agarwood oil quality identification using artificial neural network modelling for five grades Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds. Agarwood ; Artificial neural network ; Identification (biology) ; Artificial intelligence ; Incense ; Mathematics ; Machine learning ; Engineering ; Computer science ; Botany ; Biology ; Geography Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Biological Stains and Phytochemicals
Authors: Richard M. Rowan-Robinson | Zhaoyuan Leong | S. Carpio | Chunyoung Oh | Nicola Morley
Source: AIP Advances (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Material informatics for functional magnetic material discovery Functional magnetic materials are used in a wide range of “green” applications, from wind turbines to magnetic refrigeration. Often the magnetic materials used contain expensive and/or scarce elements, making them unsuitable for long term solutions. Further, traditional material discovery is a slow and costly process, which can take over 10 years. Material informatics is a growing field, which combines informatics, machine learning (ML) and high-throughput experiments to rapidly discover new materials. To prove this concept, we have devised a material informatics workflow and demonstrated the core components of natural language processing (NLP) to extract data from research papers to create a functional magnetic material database, machine learning with semi-heuristic models to predict compositions of soft magnetic materials, and high-throughput experimental evaluation using combinatorial sputtering and high-throughput magneto-optic Kerr effect (MOKE) magnetometry. This material informatics workflow provides a quicker, cheaper route to functional magnetic materials discovery. Workflow ; Computer science ; Materials informatics ; Informatics ; Throughput ; Artificial intelligence ; Machine learning ; Database ; Health informatics ; Engineering ; Engineering informatics ; Electrical engineering Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Machine Learning in Materials Science
Authors: Jordan S. Dialpuri | Jon Agirre | Kathryn D Cowtan | Paul S. Bond
Source: Nucleic Acids Research (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

<i>NucleoFind</i>: a deep-learning network for interpreting nucleic acid electron density Nucleic acid electron density interpretation after phasing by molecular replacement or other methods 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 obtained after molecular replacement, the positions of the phosphate group, sugar ring and nitrogenous base group can be predicted with high accuracy. On average, 78% of phosphate atoms, 85% of sugar atoms and 83% of base atoms are positioned in predicted density after giving NucleoFind maps produced following successful molecular replacement. NucleoFind can use the wealth of context these predicted maps provide to build more accurate and complete nucleic acid models automatically. Nucleic acid ; Context (archaeology) ; Biology ; Electron density ; Resolution (logic) ; Base (topology) ; Crystallography ; Computational biology ; Artificial intelligence ; Biological system ; Electron ; Computer science ; Biochemistry ; Physics ; Mathematics ; Chemistry Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Saptarshi Dutta | P. S. Robi
Source: Mechanics of Time-Dependent Materials (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

Prediction of creep behavior of Zr-Nb alloy under dual-phase condition using data driven models Creep ; Materials science ; Alloy ; Dual (grammatical number) ; Phase (matter) ; Metallurgy ; Physics Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Material Properties and Failure Mechanisms
Authors: Ryuji Otsuka | Kōji Shimizu | Hitoshi Wakabayashi | Satoshi Watanabe
Source: Applied Physics Express (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Investigating the atomic structures and electronic properties of WS<sub>2</sub> thin films with sulfur vacancies via a neural network potential-aided first-principles study Transition metal dichalcogenides are promising materials for high-performance electronics, whereas the impact of defects on their electronic properties remains elusive. Here, we employ neural network potentials (NNPs) constructed from density functional theory (DFT) data to investigate defect-laden WS 2 thin films. Molecular dynamics simulations reveal that at low defect concentrations (S/W ratio of 1.9), single sulfur vacancies are predominant. Conversely, at high defect concentrations (S/W ratio of 1.7), complex defects with short lifetimes appear. Additionally, DFT results indicate that the band gap persists at S/W = 1.9 but disappears at 1.7, aligning with observed device degradation at high defect concentrations. Sulfur ; Thin film ; Materials science ; Artificial neural network ; Chemical physics ; Nanotechnology ; Optoelectronics ; Computer science ; Chemistry ; Artificial intelligence ; Metallurgy 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Danish Khan | Alastair J. A. Price | Maximilian L. Ach | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2024-02-22)
Field: Materials Science — Materials Chemistry

Adaptive hybrid density functionals Exact exchange contributions are known to crucially affect electronic states, which in turn govern covalent bond formation and breaking in chemical species. Empirically averaging the exact exchange admixture over compositional degrees of freedom, hybrid density functional approximations have been widely successful, yet have fallen short to reach high level quantum chemistry accuracy, primarily due to delocalization errors. We propose to `adaptify` hybrid functionals by generating optimal admixture ratios of exact exchange on the fly, i.e. specifically for any chemical compound, using extremely data efficient quantum machine learning models that carry negligible overhead. The adaptive Perdew-Burke-Ernzerhof based hybrid density functional (aPBE0) is shown to yield atomization energies with sufficient accuracy to effectively cure the infamous spin gap problem in open shell systems, such as carbenes. aPBE0 further improves energetics, electron densities, and HOMO-LUMO gaps in organic molecules drawn from the QM9 and QM7b data set. Obtained with aPBE0 in a large basis, we present a revision of the entire QM9 data set (revQM9) with an estimated quality vastly superior to the original containing on average, stronger covalent binding, larger band-gaps, more localized electron densities, and larger dipole-moments. While aPBE0 is applicable in the equilibrium regime, outstanding limitations include covalent bond dissociation when going beyond the Coulson-Fisher point. Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; History and advancements in chemistry
Authors: John Adebisi | Sesham Srinu | Varqa Mitonga
Source: Journal of Digital Food Energy & Water Systems (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation Recently, every aspect of human existence has been affected by modern technologies including agriculture. The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. This work uses a deep learning algorithm to investigate the classification of potato diseases as a case study which can be leveraged on by other agricultural products for reference. Potatoes play a vital role in global agriculture, constituting a significant portion of fresh produce consumption. In southern Africa, potatoes hold particular importance, comprising 39% of total fresh produce consumption. However, the industry faces challenges from diseases such as early blight and late blight, resulting in substantial yield losses. Early detection is crucial, prompting the exploration of Convolutional Neural Networks (CNNs) for their disease classification capabilities. CNNs have shown excellent results in plant disease classification based on image data set. This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. To this end, five CNN models were trained on the Plant Village dataset for analysis. The models were compared using various performance metrics such as sensitivity, F1-Score, precision and recall. Based on the analysis the most suitable CNN model for FPGA implementation was selected. The chosen model was optimized for size using quantization technique. Subsequently the model was converted to Hardware Description Language (HDL) using Vitis High level synthesis (HLS) tool performance in terms of resources required to implement the model in System on Chip (SoC) was analysed. Computer science ; Artificial intelligence ; Chip ; Deep learning ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Telecommunications Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Ryo Murakami | Taisuke Sasaki | Hideki Yoshikawa | Yoshitaka Matsushita | Keitaro Sodeyama | Tadakatsu Ohkubo | Hiroshi Shinotsuka | Kenji Nagata
Source: Science and Technology of Advanced Materials Methods (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray diffraction (XRD) is an effective technique for analysing crystal structures and other microstructural features that have information that can explain material properties. Therefore, the fully automated extraction of peak features from XRD data without the bias of an analyst is a significant challenge. This study aimed to establish an efficient and robust approach for constructing peak feature tables that follow ML standards (ML-ready) from XRD data. We challenge peak feature extraction in the situation where only the peak function profile is known a priori, without knowledge of the measurement material or crystal structure factor. We utilized Bayesian estimation to extract peak features from XRD data and subsequently performed Bayesian regression analysis with feature selection to predict the material property. The proposed method focused only on the tops of peaks within localized regions of interest (ROIs) and extracted peak features quickly and accurately. This process facilitated the rapid extracting of major peak features from the XRD data and the construction of an ML-ready feature table. We then applied Bayesian linear regression to the maximum energy product [Formula: see text], using the extracted peak features as the explanatory variable. The outcomes yielded reasonable and robust regression results. Thus, the findings of this study indicated that 004 peak height and area were important features for predicting [Formula: see text]. Feature selection ; Feature (linguistics) ; Bayesian probability ; Feature extraction ; Artificial intelligence ; Bayesian inference ; Diffraction ; Linear regression ; Computer science ; Pattern recognition (psychology) ; Materials science ; Data mining ; Machine learning ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Ram Prakash | Saarthak Kapse | Umar Ameen | Chandan Hegde
Source: International Journal of Science and Research Archive (article, 2024-10-13)
Field: Chemistry — Analytical Chemistry

Predicting food adulterants in milk using Support Vector Machine (SVM) Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety. Support vector machine ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Computer science ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Food Safety and Hygiene
Authors: Zhiyu Shi | Aditya Lele | Ahren W. Jasper | Stephen J. Klippenstein | Yiguang Ju
Source: The Journal of Physical Chemistry A (article, 2024-04-20)
Field: Materials Science — Materials Chemistry

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO<sub>2</sub> → H<sub>2</sub> + <sup>3</sup>O<sub>2</sub> reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials. Ab initio ; Trajectory ; Computer science ; Artificial intelligence ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Data Processing Techniques
Authors: Jing Wu | Hao Zhang | Junjie Zhang | Xingzhi Liu | Guangzhao Qin | Te‐Huan Liu | Ronggui Yang
Source: Journal of Materiomics (article, 2024-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Significant phonon localization and suppressed transport in silicon-doped gallium oxide: A study using a unified neural network interatomic potential Monoclinic gallium oxide (β-Ga2O3) is a fourth-generation semiconductor with great application potential in high-power microelectronics. Recent studies indicate that the electrical conductivity of β-Ga₂O₃ can be substantially enhanced through silicon (Si) doping. However, the effects on thermal transport, especially by considering the practical nanostructures within the crystal, have not yet been explored. To address this gap, we have developed a unified neural network potential for investigating the unexplored phonon transport of the β-(SixGa1–x)2O3 with varying doping levels. Our atomistic simulations showed that compared to intrinsic β-Ga2O3, the room-temperature thermal conductivities respectively decreased by 36.5%, 33.5%, and 38.8% along the a, b, and c axes in β-SiGa511O768, and by 79.6%, 74.9%, and 77.8% in β-SiGa7O12. The significant degradation in phonon transport is attributed to increased lattice anharmonicity, reduced sound velocity, and most importantly, induced phonon localization due to Si substitutions. A quantitative analysis reveals that the localization primarily occurs in phonons with frequencies exceeding 2.5 THz. The vibration is confined to a region around the Si atom, extending only to its second-nearest neighbors. Phonon ; Materials science ; Silicon ; Doping ; Microelectronics ; Condensed matter physics ; Gallium ; Anharmonicity ; Semiconductor ; Gallium nitride ; Thermal conductivity ; Optoelectronics ; Nanotechnology ; Physics Ga2O3 and related materials ; ZnO doping and properties ; Thermal Expansion and Ionic Conductivity