jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonni Firdaus | Usman Ahmad | I Wayan Budiastra | I Dewa Made Subrata
Source: Jurnal Keteknikan Pertanian (article, 2024-08-07)
Field: Chemistry — Analytical Chemistry

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6). Macro ; Artificial neural network ; Field (mathematics) ; Nutrient ; Environmental science ; Agricultural engineering ; Soil nutrients ; Agronomy ; Soil science ; Computer science ; Artificial intelligence ; Engineering ; Mathematics ; Soil water ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Hongjin Wang | Chenxing Luo | Renata M. Wentzcovitch
Source: arXiv (Cornell University) (preprint, 2024-09-24)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning potential for serpentines Serpentines are layered hydrous magnesium silicates (MgO$\cdot$SiO$_2\cdot$H$_2$O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory (DFT) calculation with the r$^2$SCAN meta-GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high-temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polymorphism of antigorite with periodicity $m$ = 13--24, which is believed to be all the naturally existent antigorite species. We found that antigorite with $m$ larger than 21 appears more stable than lizardite at low temperatures. This machine learning potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves. Psychology ; Artificial intelligence ; Business ; Computer science Various Chemistry Research Topics
Authors: Samuel G. Espley | Samuel Allsop | David Buttar | Simone Tomasi | Matthew N. Grayson
Source: ChemRxiv (preprint, 2024-07-08)
Field: Materials Science — Materials Chemistry

Distortion/Interaction Analysis via Machine Learning Machine learning (ML) models have provided a highly efficient pathway to quantum mechanical accurate reaction barrier predictions. Previous approaches have, however, stopped at prediction of these barriers instead of developing predictive capabilities in reactivity analysis tasks such as distortion/interaction-activation strain analysis. Such methods can provide insight into reactivity trends and ultimately guide rational reaction design. In this work we present the novel application of ML to the rapid and accurate prediction of distortion and interaction DFT energies across four datasets (three existing and one new dataset). We also show how our models can accurately predict on unseen, high impact literature examples where DFT-level distortion/interaction analysis has previously been used to explain reactivity trends for cycloadditions. This work thus provides support for ML to be utilised further in reactivity analysis across different reaction classes at a fraction of the cost of traditional methods such as DFT. Reactivity (psychology) ; Distortion (music) ; Computer science ; Fraction (chemistry) ; Work (physics) ; Machine learning ; Artificial intelligence ; Biological system ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Ángel Yanguas-Gil | Jeffrey W. Elam
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Self-Driving Tool, Digital Twin, and Machine Learning Algorithms for the Real Time Optimization of the ALD Growth of Multicomponent Thin Films Using in Situ Techniques The design of optimal sequences for multicomponent ALD processes can be a time-consuming procedure: transient effects can appear each time that one switches to a different precursor, making it hard to predict beforehand the desired composition. Consequently, researchers usually resort to trial runs to calibrate the film composition, and combination of precursors with well-behaved nucleation behaviors and fast transients are highly prized. While effective for processes involving two different precursors, this empirical approach makes it hard to extend ALD to explore more complex materials involving ternary or quaternary compounds. In this work we explore an alternative approach that uses machine learning integrated with in-situ techniques to optimize the composition of multicomponent films. Our approach relies on digital twins to train, optimize, and benchmark algorithms that build the sequence of ALD cycles in real time from the feedback of in-situ data. These models allow us to generate a wide range of processes with different types of nucleation behaviors, providing the ideal testbed for the development of robust algorithms. We then transfer these algorithms into our experimental reactors for the exploration and optimization of processes involving two and three different types of oxide materials. Our results show that it is possible to drive the optimization of binary and ternary ALD processes using solely the growth per cycle of the individual processes as input, with the algorithm being robust across different nucleation behaviors. The main limitation of the proposed approach is that, for techniques showing a net change in the material, such as quartz crystal microbalance, it is not possible to consider systems that exhibit a substantial amount of etching, such the ZnO/Al 2 O 3 process involving diethyl zinc and trimethyaluminum precursors. This would not be a limitation for techniques providing direct compositional information, such as X-ray fluorescence, or those focusing on materials properties, such as in-situ spectroscopic ellipsometry. This research is based on the work supported by the Laboratory Directed Research and Development (LDRD) funding from the Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. DOE under Contract No. DE-AC02-06CH11357. Algorithm ; In situ ; Computer science ; Optimization algorithm ; Materials science ; Chemistry ; Mathematics ; Mathematical optimization Catalytic Processes in Materials Science
Authors: Xujian Wang | Junmei Wang | Xiongwu Wu | Bernard R. Brooks
Source: ChemRxiv (preprint, 2024-11-07)
Field: Materials Science — Materials Chemistry

Accurate Free Energy Calculation viaMultiscale Simulations Driven by HybridMachine Learning and Molecular MechanicsPotentials Our study focused on the implementation and testing of machine learning interatomic potentials (MLIPs) into the AMBER software suite. This implementation enables us to perform a novel type of molecular dynamics simulation utilizing the hybrid machine learning/molecular mechanics (ML/MM) potentials. To underpin the capabilities of ML/MM simulations, we first validated our implementation at a fundamental physical level by confirming energy and momentum conservation laws. The successful validation indicates that our implementation is able to produce adequate and physically interpretable samplings. Building upon this, for the first time to the best of our knowledge, we proposed an ML/MM-compatible thermodynamic integration (TI) protocol to tackle real-world challenges, such as solvation free energy calculation. Our results demonstrate that this computational protocol can predict hydration free energies with an accuracy of less than 1.00 kcal/mol compared to experimental data, paving the way for the use of ML/MM in multiscale simulations to addressing future drug design problems. Moreover, by applying ML/MM in molecular dynamics simulations of protein-ligand complexes, we demonstrated that the adequate samplings enable us to accurately reproduce experimental binding free energies. Thus, our implementation can offer new insights into biomolecular systems using the ML/MM "microscope". Last, we demonstrated that our implementation can achieve nanosecond timescale simulations daily after significant effort being put to improve the code performance. In a conclusion, we have successfully implemented ML/MM potential to AMBER software package after overcoming limitations in current multi-scale simulations including low computational efficiency. We have advanced TI theory allowing us to accurately predict free energies with ML/MM potentials. Energy (signal processing) ; Statistical physics ; Computer science ; Molecular mechanics ; Classical mechanics ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Abdalaziz Rashid Al-Maeeni | Д. Деркач | A. Ustyuzhanin
Source: Moscow University Physics Bulletin (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Engineering Point Defects in MoS$${}_{\textbf{2}}$$ for Tailored Material Properties Using Large Language Models The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generation optoelectronic and high-tech applications. Building upon prior work on machine learning-driven material design, this study focuses on the systematic introduction and manipulation of point defects in MoS $${}_{2}$$ to tailor their properties. Leveraging a comprehensive dataset generated via density functional theory (DFT) calculations, we explore the effects of various defect types and concentrations on the material characteristics of TMDCs. Our methodology integrates the use of pretrained large language models to generate defect configurations, enabling efficient predictions of defect-induced property modifications. This research differs from traditional methods of material generation and discovery by utilizing the latest advances in transformer model architecture, which have proven to be efficient and accurate discrete predictors. In contrast to high-throughput methods where configurations are generated randomly and then screened based on their physical properties, our approach not only enhances the understanding of defect-property relationships in TMDCs but also provides a robust framework for designing materials with bespoke properties. This facilitates the advancement of materials science and technology. Point (geometry) ; Materials science ; Computer science ; Engineering physics ; Physics ; Mathematics ; Geometry 2D Materials and Applications ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Vasile-Adrian Surdu | Romuald Győrgy
Source: Inorganics (article, 2024-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase Relations in the Pseudo-Binary BiFeO3–EuFeO3 System in the Subsolidus Region Derived from X-Ray Diffraction Data—A Machine Learning Approach BiFeO3 and EuFeO3 are some of the most studied ferrites and part of the larger category of multiferroic and magnetic compounds. The instabilities reported for BiFeO3 that hinder its use in practical applications can be overcome by substitution with rare-earth ions, such as Eu3+, on the Bi3+ site. This paper reports on the phase relations in the BiFeO3-EuFeO3 pseudo-binary system, which were not established previously. Solid-state reactions were employed to prepare different compositions according to the nominal formula Bi1−xEuxFeO3 (where x = 0, 0.05, 0.10, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1). Phase equilibria were studied at different temperatures between 800 and 1200 °C from X-ray diffraction (XRD) data. The analysis of the XRD patterns by machine learning approaches revealed eight defined clusters and four unclustered points. The validation test showed that most of the points could belong to several clusters and thus, traditional identification was employed. Phase identification and quantification by traditional approaches revealed six crystallization zones on the diagram. Although the machine learning approach offers speed in the process of classification of XRD patterns, validation by the traditional method was necessary for the construction of the phase diagram with high accuracy. Crystallization ; Phase diagram ; Diffraction ; Phase (matter) ; Binary number ; Materials science ; Multiferroics ; X-ray crystallography ; Powder diffraction ; Identification (biology) ; Diagram ; Computer science ; Crystallography ; Artificial intelligence ; Analytical Chemistry (journal) ; Chemistry ; Thermodynamics ; Physics ; Mathematics ; Ferroelectricity ; Optics ; Chromatography ; Database Multiferroics and related materials
Authors: Ping Li | Xueyang Li | Junsheng Feng | Jinyang Ni | Zhi‐Xin Guo | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2024-01-02)
Field: Materials Science — Materials Chemistry

Origin of zigzag antiferromagnetic orders in XPS3 (X= Fe, Ni) monolayers Recently, two monolayer magnetic materials, i.e., FePS3 and NiPS3, have been successfully fabricated. Despite that they have the same atomic structure, the two monolayers exhibit distinct magnetic properties. FePS3 holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while NiPS3 exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model which can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies which failed to fully consider the spin-orbit coupling effect, we find that the AFM-ZZ ground state in FePS3 is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor exchange interactions, and combining single-ion anisotropy. Whereas, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in NiPS3. We additionally calculate spin-wave spectrum of AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of AFM-ZZ ground state in two-dimensional materials. Antiferromagnetism ; Zigzag ; Condensed matter physics ; Ground state ; Monolayer ; Hamiltonian (control theory) ; Ferromagnetism ; k-nearest neighbors algorithm ; Anisotropy ; Magnetic structure ; Materials science ; Exchange interaction ; Physics ; Magnetic field ; Nanotechnology ; Quantum mechanics ; Magnetization ; Computer science ; Mathematics ; Geometry 2D Materials and Applications ; Physics of Superconductivity and Magnetism ; Iron-based superconductors research
Authors: Jian Liu | Ryan Jacobs | Bo Guan | Yoosuf N. Picard | Tao Yang | Richard Pineault | Gregory Hackett | Harry Abernathy | Dane Morgan
Source:  (article, 2024-10-06)
Field: Materials Science — Materials Chemistry

Material Discovery and Design Principles of Perovskite Oxides for Reversible Solid Oxide Cells (R-SOC) Reversible solid oxide cells (R-SOCs) are highly efficient devices for energy conversion and storage, capable of operating for both hydrogen utilization and production. In fuel cell mode, an R-SOC consumes hydrogen or natural gas to generate electricity, while in electrolysis mode, it produces hydrogen from steam. The discover of new materials with rapid oxygen surface exchange kinetics and enduring stability is crucial for the economically viable commercialization of R-SOCs. To facilitate this pursuit, we conducted extensive Density Functional Theory (DFT) calculations and developed Machine Learning (ML) models to predict critical catalytic properties essential for R-SOCs, such as oxygen surface exchange/diffusivity, and area-specific resistance (ASR). BaCoxFeyZrzO3-d(BFCZ)(x+y+z=1) emerged as a promising family of electrode materials with high activity and stability, validated through systematic experimental study. Moreover, a robust numerical multiphysics model was developed to optimize materials and microstructure parameters, providing the ability to predict the performance of functional R-SOCs. Materials science ; Multiphysics ; Hydrogen storage ; Perovskite (structure) ; Oxide ; Hydrogen ; Electrolysis ; Density functional theory ; Solid oxide fuel cell ; Chemical engineering ; Process engineering ; Nanotechnology ; Anode ; Electrode ; Chemistry ; Thermodynamics ; Composite material ; Engineering ; Metallurgy ; Organic chemistry ; Physical chemistry ; Computational chemistry Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Dayakrishna Nailwal | M. V. Sunil Krishna | Alok Ranjan | Jia Yue
Source: arXiv (Cornell University) (preprint, 2024-05-30)
Field: Chemistry — Spectroscopy

Modeling of Nitric Oxide Infrared radiative flux in lower thermosphere: a machine learning perspective Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies. Thermosphere ; Perspective (graphical) ; Infrared ; Flux (metallurgy) ; Nitric oxide ; Radiative transfer ; Radiative flux ; Atmospheric sciences ; Physics ; Materials science ; Chemistry ; Computer science ; Optics ; Astronomy ; Ionosphere ; Artificial intelligence Spectroscopy and Laser Applications ; Radiative Heat Transfer Studies ; Infrared Target Detection Methodologies
Authors: Ricardo Gabriel Valencia Albornoz | Diego A. Oyarzún | Karl Burgess
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-25)
Field: Chemistry — Spectroscopy

Optimisation of surfactin yield in <i>Bacillus</i> using active learning and high-throughput mass spectrometry Abstract Integration of machine learning and high throughput measurements are essential to drive the next generation of the design-build-test-learn (DBTL) cycle in synthetic biology. Here, we report the use of active learning in combination with metabolomics for optimising production of surfactin, a complex lipopeptide resulting from a non-ribosomal assembly pathway. We designed a media optimisation algorithm that iteratively learns the yield landscape and steers the media composition toward maximal production. The algorithm led to a 160% yield increase after three DBTL runs as compared to an M9 baseline. Metabolomics data helped to elucidate the underpinning biochemistry for yield improvement and revealed Pareto-like trade-offs in production of other lipopeptides from related pathways. We found positive associations between organic acids and surfactin, suggesting a key role of central carbon metabolism, as well as system-wide anisotropies in how metabolism reacts to shifts in carbon and nitrogen levels. Our framework offers a novel data-driven approach to improve yield of biological products with complex synthesis pathways that are not amenable to traditional yield optimisation strategies. Graphical abstract Yield (engineering) ; Surfactin ; Metabolomics ; Throughput ; Biochemical engineering ; Synthetic biology ; Bacillus subtilis ; Lipopeptide ; Computational biology ; Chemistry ; Systems biology ; Computer science ; Machine learning ; Biology ; Bacteria ; Engineering ; Chromatography ; Materials science Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Metabolomics and Mass Spectrometry Studies