jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deperias Kerre | Anne Laurent | Kenneth Maussang | Dickson Odhiambo Owuor
Source: Data in Brief (article, 2024-12-23)
Field: Chemistry — Spectroscopy

An instruction dataset for extracting quantum cascade laser properties from scientific text Quantum Cascade Lasers (QCL) are promising semiconductor lasers, compact and powerful, but of complex design. Availability of structured data of the QCL properties can support data mining activities that seek to understand the relationship between these properties, for instance between the design and performance features. The main open source of QCL data is in scientific text which in most cases is usually unstructured. One of the ways to extract and organize this data is by utilizing Information Extraction techniques. These techniques can accelerate the process of curating QCL properties data from scientific articles for further analysis. One of the main challenges in developing machine learning algorithms for extraction of QCL properties from text is lack of quality training data for these algorithms. Large Language Models (LLMs) have demonstrated great capabilities in materials property extraction from text. They however experience challenges with domain specific properties, for instance the heterostructure and design types in the QCL domain hence for adaptation. In this paper, we present an original instruction dataset for training and evaluation of LLMs for QCL properties extraction from text. The data is generated by augmenting sample sentences from scientific articles with GPT-3.5 instruct with a few shot strategy. The dataset then is manually annotated with the help of QCL experts and is composed of 1300 rows of training examples consisting of an Instruction, Input Text and the Output. Cascade ; Computer science ; Quantum cascade laser ; Laser ; Information retrieval ; Research article ; Natural language processing ; Physics ; Chemistry ; Library science ; Optics ; Chromatography Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Text Analysis Techniques
Authors: Leheng Chen | Chuang Zhang | Jin Zhao
Source: Physical review. E (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Modeling heat conduction with dual-dissipative variables: A mechanism-data fusion method Many macroscopic non-Fourier heat conduction models have been developed in the past decades based on Chapman-Enskog, Hermite, or other small perturbation expansion methods. These macroscopic models have achieved great success in capturing non-Fourier thermal behaviors in solid materials, but most of them are limited by small Knudsen numbers and incapable of capturing highly nonequilibrium or ballistic thermal transport. In this paper, we provide a different strategy for constructing macroscopic non-Fourier heat conduction modeling, that is, using data-driven deep-learning methods combined with nonequilibrium thermodynamics instead of small perturbation expansion. We present the mechanism-data fusion method, an approach that seamlessly integrates the rigorous framework of conservation-dissipation formalism (CDF) with the flexibility of machine learning to model non-Fourier heat conduction. Leveraging the conservation-dissipation principle with dual-dissipative variables, we derive an interpretable series of partial differential equations, fine tuned through a training strategy informed by data from the phonon Boltzmann transport equation. Moreover, we also present the inner-step operation to narrow the gap from the discrete form to the continuous system. Through numerical tests, our model demonstrates excellent predictive capabilities across various heat conduction regimes, including diffusive, hydrodynamic, and ballistic regimes, and displays its robustness and precision even with discontinuous initial conditions. Thermal conduction ; Non-equilibrium thermodynamics ; Dissipative system ; Statistical physics ; Dissipation ; Fourier transform ; Lattice Boltzmann methods ; Physics ; Partial differential equation ; Fourier series ; Boltzmann equation ; Computer science ; Mechanics ; Mathematical analysis ; Mathematics ; Thermodynamics Thermal properties of materials ; Model Reduction and Neural Networks ; Nanofluid Flow and Heat Transfer
Authors: Da Xu | William C. Thomas | Audrey A. Burnim | Nozomi Ando
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-17)
Field: Chemistry — Inorganic Chemistry

Conformational Landscapes of a Class I Ribonucleotide Reductase Complex during Turnover Reveal Intrinsic Dynamics and Asymmetry Abstract Understanding the structural dynamics associated with enzymatic catalysis has been a long-standing goal of biochemistry. With the advent of modern cryo-electron microscopy (cryo-EM), it has become conceivable to redefine a protein’s structure as the continuum of all conformations and their distributions. However, capturing and interpreting this information remains challenging. Here, we use classification and deep-learning-based analyses to characterize the conformational heterogeneity of a class I ribonucleotide reductase (RNR) during turnover. By converting the resulting information into physically interpretable 2D conformational landscapes, we demonstrate that RNR continuously samples a wide range of motions while maintaining surprising asymmetry to regulate the two halves of its turnover cycle. Remarkably, we directly observe the appearance of highly transient conformations needed for catalysis, as well as the interaction of RNR with its endogenous reductant thioredoxin also contributing to the asymmetry and dynamics of the enzyme complex. Overall, this work highlights the role of conformational dynamics in regulating key steps in enzyme mechanisms. Ribonucleotide reductase ; Asymmetry ; Class (philosophy) ; Dynamics (music) ; Chemistry ; Biophysics ; Physics ; Biology ; Biochemistry ; Philosophy ; Particle physics ; Epistemology ; Protein subunit Metal-Catalyzed Oxygenation Mechanisms ; Photosynthetic Processes and Mechanisms ; Advanced Electron Microscopy Techniques and Applications
Authors: Marie Oestreich | Erinç Merdivan | Michael Lee | Joachim L. Schultze | Marie Piraud | Matthias Becker
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-21)
Field: Materials Science — Materials Chemistry

DrugDiff - small molecule diffusion model with flexible guidance towards molecular properties With the cost/yield-ratio of drug development becoming increasingly unfavourable, recent work has explored machine learning to accelerate early stages of the development process. Given the current success of deep generative models across domains, we here investigated their application to the property-based proposal of new small molecules for drug development. Specifically, we trained a latent diffusion model - DrugDiff - paired with predictor guidance to generate novel compounds with a variety of desired molecular properties. The architecture was designed to be highly flexible and easily adaptable to future scenarios. Our experiments showed successful generation of unique, diverse and novel small molecules with targeted properties. The code is available at https://github.com/MarieOestreich/DrugDiff. Diffusion ; Molecule ; Small molecule ; Business ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Thermodynamics ; Organic chemistry ; Biochemistry Lanthanide and Transition Metal Complexes ; Metal complexes synthesis and properties ; Magnetism in coordination complexes
Authors: Darío Fernando Guamán Lozada | María José Tobar Heredia | Mayra Zambrano-Vinueza | Roister Alexis Pesantes Ortiz | Marlon Moscoso-Martínez | Paúl Marcelo Manobanda Pinto
Source: Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) (article, 2024-04-15)
Field: Materials Science — Polymers and Plastics

Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach Introduction: This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency. Materials and Methods: A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance. Results: Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes. Discussion: The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes. Conclusion: The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood. Artificial neural network ; Waviness ; Predictive modelling ; Computer science ; Machine learning ; Process (computing) ; Genetic algorithm ; Artificial intelligence ; Statistical model ; Engineering ; Mechanical engineering Textile materials and evaluations ; Advanced machining processes and optimization
Authors: A. K. Nair | Carlos Manuel Da Silva | Cristina H. Amon
Source:  (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Thermal Conductivity Prediction in Two-Dimensional TiS2/MoS2 Van Der Waals Heterostructures Abstract Two-dimensional (2D) materials and heterostructures display unique thermal characteristics compared to their bulk counterparts. However, the accurate estimation of the thermal conductivity of 2D materials, particularly of 2D van der Waals heterostructures, presents significant challenges for both computational and experimental methods. In this study, we propose a computationally efficient approach to investigate the thermal conductivity of 2D TiS2/MoS2 van der Waals heterostructures. Our approach utilizes machine-learning interatomic potentials (MLIPs) to predict the thermal conductivity of the heterostructure. This approach effectively incorporates intralayer interactions by utilizing moment tensor potentials (MTP) trained with computationally inexpensive density functional theory (DFT)-based datasets. These datasets are generated from ab-initio molecular dynamics (AIMD) trajectories over less than 1 ps, while the interlayer van der Waals interactions are calibrated using the D3-dispersion correction method. By explicitly incorporating the missing dispersion contribution into the MTP, this method provides greater accuracy in predicting interlayer interactions than the widely applied Lennard-Jones (LJ) potential. Finally, molecular dynamics (MD) simulations are conducted to determine the thermal conductivity of the TiS2/MoS2 heterostructures using the derived potential parameters. This study enhances our understanding of thermal transport in van der Waals (vdW) heterostructures, leveraging MLIPs to explore new nanostructured materials with superior thermal conductivity. Thermal conductivity ; van der Waals force ; Heterojunction ; Materials science ; Conductivity ; Thermal ; Computer science ; Condensed matter physics ; Optoelectronics ; Composite material ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Zihan Xia | Chun Zhao | Qiuyan Du | Zhi Yang | Zhang Ming Shuai | Qiao Liang
Source:  (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Advancing Photochemistry Simulation in WRF-Chem V4.0: Artificial Intelligence PhotoChemistry (AIPC) Scheme with Multi-Head Self-Attention Algorithm Atmospheric photochemistry is essential for simulating atmospheric composition, impacting air quality and climate change. However, conventional numerical schemes of photochemistry within atmospheric models are computationally expensive, leading to simplifications or omissions of critical processes in weather and climate models. Previous attempts to leverage artificial intelligence (AI) scheme to reduce computational costs have faced obstacles such as the curse of dimensionality and error propagation, and most have been limited to box models without coupling into numerical models. Here, we develop an innovative AI PhotoChemistry (AIPC) scheme coupled into an atmospheric model (WRF-Chem). With Multi-Head Self-Attention algorithm (MHSA), we simulate 74 chemical species and 229 reactions following the SAPRC-99 mechanism. This marks the first implementation of a sophisticated photochemical mechanism within one unified AI model, enabling fast, accurate, and stable simulations without needing individual AI model for each species as previous works. Comparative analysis reveals that the AIPC scheme outperforms previous AI schemes using Multi-Layer Perceptron and Residual Neural Network algorithms, offering superior accuracy and computational efficiency. Moreover, fine-tuning learning rate and broadening network width within the MHSA algorithm are more effective for improving the AIPC scheme’s performance than adjusting batch size or increasing network depth. When coupling AIPC into WRF-Chem, this hybrid model with both physics and AI schemes reproduces the spatiotemporal distributions of various species on monthly time scale, and achieves substantial speed enhancement with ~8 times faster than conventional scheme. This advancement lays the groundwork for future development of weather and climate models with sophisticated chemical processes. Scheme (mathematics) ; Photodissociation ; Artificial neural network ; Artificial light Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Francesco Belli | Eva Zurek
Source: arXiv (Cornell University) (preprint, 2024-06-19)
Field: Chemistry — Physical and Theoretical Chemistry

Efficient Modelling of Anharmonicity and Quantum Effects in PdCuH$_2$ with Machine Learning Potentials Quantum nuclear effects and anharmonicity impact a wide range of functional materials and their properties. One of the most powerful techniques to model these effects is the Stochastic Self-Consistent Harmonic Approximation (SSCHA). Unfortunately, the SSCHA is extremely computationally expensive, prohibiting its routine use. We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. Our method leverages an upscaling procedure that allows for the treatment of supercells of up to thousands of atoms with practically minimal computational effort. The protocol is applied to PdCuH$_x$ ($x = 0-2$) compounds, chosen because previous experimental studies have reported superconducting critical temperatures, $T_\text{c}$s, as high as 17~K at ambient pressures in an unknown hydrogenated PdCu phase. We identify a $P4/mmm$ PdCuH$_2$ structure, which is shown to be dynamically stable only upon the inclusion of quantum fluctuations, as being a key contributor to the measured superconductivity. For this system, our methodology is able to reduce the computational expense for the SSCHA calculations by $\sim$96\%. The proposed protocol opens the door towards the routine inclusion of quantum nuclear motion and anharmonicity in materials discovery. Anharmonicity ; Quantum ; Physics ; Statistical physics ; Computer science ; Quantum mechanics Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Jingjin He | Ruowei Yin | Changxin Wang | Chuanbao Liu | Dezhen Xue | Yanjing Su | Lijie Qiao | Turab Lookman | Yang Bai
Source: Journal of Materiomics (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Compositional design of compounds with elements not in training data using supervised learning An issue of current interest in the use of machine learning models to predict compositions of materials is their reliability in predicting outcomes with elements not included in the training data. We show that the phase diagram of the ceramic (Ba1−x−yCaxSry)(Ti1−u−v−wZruSnvHfw)O3 can be accurately predicted if the feature values of unknown elements do not exceed the range of values for existing elements in the training data. In particular, we employ physical features as descriptors and compositions as weights to show that by excluding an element, such as Zr, Sn or Hf from the training set and treating it as an unknown element, the machine learning model accurately predicts the property only if the feature values of the unknown element does not exceed the range of values of existing elements in the training set. By adding a small amount of data for the unknown element restores the prediction accuracy. We demonstrate this for BaTiO3 ceramics doped with rare earth elements where the prediction accuracy is restored if the physical feature space is suitably enlarged with training data. The prediction error increases with the Euclidean distance of the testing sample relative to the nearest training sample in the physical feature space. Our work provides an effective strategy for extending machine learning models for material compositions beyond the scope of available data. Feature (linguistics) ; Range (aeronautics) ; Feature vector ; Computer science ; Machine learning ; Reliability (semiconductor) ; Artificial intelligence ; Sample (material) ; Property (philosophy) ; Set (abstract data type) ; Euclidean distance ; Element (criminal law) ; Ceramic ; Data mining ; Pattern recognition (psychology) ; Materials science ; Physics Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides
Authors: Juliana Quintana-Rojas | Rafael Amaya-Gómez | Nicolás Ratkovich
Source: Algorithms (article, 2024-09-17)
Field: Chemistry — Organic Chemistry

Compatibility Model between Encapsulant Compounds and Antioxidants by the Implementation of Machine Learning The compatibility between antioxidant compounds (ACs) and wall materials (WMs) is one of the most crucial aspects of the encapsulation process, as the encapsulated compounds’ stability depends on the affinity between the compounds, which is influenced by their chemical properties. A compatibility model between the encapsulant and antioxidant chemicals was built using machine learning (ML) to discover optimal matches without costly and time-consuming trial-and-error experiments. The attributes of the required antioxidant and wall material components were recollected, and two datasets were constructed. As a result, a tying process was performed to connect both datasets and identify significant relationships between parameters of ACs and WMs to define the compatibility or incompatibility of the compounds, as this was necessary to enrich the dataset by incorporating decoys. As a result, a simple statistical analysis was conducted to examine the indicated correlations between variables, and a Principal Component Analysis (PCA) was performed to reduce the dimensionality of the dataset without sacrificing essential information. The K-nearest neighbor (KNN) algorithm was used and designed to handle the classification problems of the compatibility of the combinations to integrate ML in the model. In this way, the model accuracy was 0.92, with a sensitivity of 0.84 and a specificity of 1. These results indicate that the KNN model performs well, exhibiting high accuracy and correctly classifying positive and negative combinations as evidenced by the sensitivity and specificity scores. Compatibility (geochemistry) ; Computer science ; Chemistry ; Biological system ; Biochemical engineering ; Artificial intelligence ; Materials science ; Composite material ; Engineering ; Biology Free Radicals and Antioxidants
Authors: Joseph C. Bear | Jeremy K. Cockcroft
Source: Chemistry - A European Journal (article, 2024-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

The Changing Phases of Bromopentafluorobenzene As part of a much larger study on non-covalent interactions in binary adducts, we have explored the solid-state structures of bromopentafluorobenzene (C<sub>6</sub>F<sub>5</sub>Br) using differential scanning calorimetry (DSC), variable-temperature powder X-ray diffraction (VT-PXRD), and single-crystal X-ray diffraction (SXD). DSC data initially indicated a single solid-state phase below the freezing point, but revealed additional weak transitions upon heating. The crystal structures of three solid-state phases have been solved. The SXD data showed that phases I and IV are centrosymmetric, whilst phase II is polar. However, the structure of phase III remains elusive due to the changing phase behaviour of C<sub>6</sub>F<sub>5</sub>Br that is determined as much as by kinetics as thermodynamics. The results underline the need for multiple analytical techniques to study non-covalent interactions and offer valuable data for refining computational models in crystal structure prediction and machine learning. A comparison with the iodinated counterpart is also made. Differential scanning calorimetry ; Powder diffraction ; Diffraction ; Phase (matter) ; Crystallography ; Materials science ; Phase transition ; Solid-state ; Thermodynamics ; Melting point ; Covalent bond ; Crystal structure ; Chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Optics Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure ; Material Dynamics and Properties