jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chandrajit Pal | Samikshan Das | Amitava Akuli | Sudip Kumar Adhikari | Aniruddha Dey
Source: Informatica (article, 2024-09-02)
Field: Chemistry — Analytical Chemistry

Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature The process of cocoa hybridization produces new types that have unique chemical properties that impact the manufacturing of chocolate yet are resistant to a number of plant illnesses. Image analysis is a valuable tool for visually differentiating between cocoa beans, deep learning (DL) has become the standard way for image processing. Nevertheless, these techniques necessitate a substantial quantity of data and meticulous hyperparameter adjustment. In this paper, we compare machine learning and deep learning models because it takes a lot of images to cover the wide range of agricultural products. Specifically, we extract features from images using a series of image processing techniques, and then we use both traditional machine learning methods (KNN, Decision tree, SVM, and Random Forest) and Convolutional Neural Networks (proposed Cocoa-Net and RESNET 50) to classify the cocoa beans into four categories: large, medium, small, and rejected. Since each method offers strong classification performance and has potential for use in the classification of food, they were all chosen. To test these methods, a dataset including 200 samples of fragmented images was utilised. Studies that compare various approaches are also carried out. Two optimisation techniques: Univariate Selection and Feature Importance are used to optimise the retrieved features before the machine learning deep learning models are trained. The Adam optimizer is used to optimise the proposed Cocoa-Net model. K-fold cross validation is used to assess trained models, and mean cross validation scores are then computed for performance analysis. The empirical result show that, the proposed Cocoa-Net model predicts with the highest mean accuracy score of 0.83 overall, while the Random Forest Classifier score of 0.75. Feature (linguistics) ; Pattern recognition (psychology) ; Image (mathematics) ; Net (polyhedron) ; Artificial intelligence ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Food Chemistry and Fat Analysis ; Smart Agriculture and AI
Authors: Jun Li | Wenqi Fang | Shangjian Jin | Tengdong Zhang | Yanling Wu | Xiaodan Xu | Yong Liu | Dao‐Xin Yao
Source: arXiv (Cornell University) (preprint, 2024-09-12)
Field: Materials Science — Materials Chemistry

A deep learning approach to search for superconductors from electronic bands Energy band theory is a foundational framework in condensed matter physics. In this work, we employ a deep learning method, BNAS, to find a direct correlation between electronic band structure and superconducting transition temperature. Our findings suggest that electronic band structures can act as primary indicators of superconductivity. To avoid overfitting, we utilize a relatively simple deep learning neural network model, which, despite its simplicity, demonstrates predictive capabilities for superconducting properties. By leveraging the attention mechanism within deep learning, we are able to identify specific regions of the electronic band structure most correlated with superconductivity. This novel approach provides new insights into the mechanisms driving superconductivity from an alternative perspective. Moreover, we predict several potential superconductors that may serve as candidates for future experimental synthesis. Superconductivity ; Computer science ; Deep learning ; Artificial intelligence ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: David F. Nippa | Alex T. Müller | Kenneth Atz | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: ChemRxiv (preprint, 2024-05-07)
Field: Chemistry — Physical and Theoretical Chemistry

Simple User-Friendly Reaction Format Leveraging the increasing volume of chemical reaction data can enhance synthesis planning and improve suc- cess rates. However, machine learning applications for retrosynthesis planning and forward reaction prediction tools depend on having readily available, high-quality data in a structured format. While some public and licensed reaction databases are available, they frequently lack essential information about reaction condi- tions. To address this issue and promote the principles of findable, accessible, interoperable, and reusable (FAIR) data reporting and sharing, we introduce the Simple User-Friendly Reaction Format (SURF). SURF standardizes the documentation of reaction data through a structured tabular format, requiring only a basic understanding of spreadsheets. This format enables chemists to record the synthesis of molecules in a format that is both human- and machine-readable, making it easier to share and integrate directly into machine- learning pipelines. SURF files are designed to be interoperable, easily imported into relational databases, and convertible into other formats. This complements existing initiatives like the Open Reaction Database (ORD) and Unified Data Model (UDM). At Roche, SURF plays a crucial role in democratizing FAIR reaction data sharing and expediting the chemical synthesis process. Simple (philosophy) ; User Friendly ; Computer science ; World Wide Web ; Database ; Human–computer interaction ; Programming language Various Chemistry Research Topics ; Chemical Synthesis and Analysis ; Genetics, Bioinformatics, and Biomedical Research
Authors: Peter Minch | Romakanta Bhattarai | Kamal Choudhary | Trevor David Rhone
Source: Physical Review Materials (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Predicting magnetic properties of van der Waals magnets using graph neural networks We study two-dimensional (2D) magnetic materials using state-of-the-art machine learning models that use a graph-theory framework. We find that representing materials as graphs allows us to better learn structure-property relationships by leveraging both the chemical properties of the constituent atoms and the connectivity between those atoms. Graph neural network models are capable of predicting global properties of crystal structure (i.e., graphwise properties) and local properties of the constituent atoms (i.e., nodewise properties). We embed physical constraints into our model by simultaneously making predictions of local and global properties. In particular, we use the atomistic line graph neural network (ALIGNN) architecture. We train the ALIGNN model on data comprising local and global magnetic moments of 314 2D structures of the form $\mathrm{Cr}{A}^{\text{ii}}{B}^{\text{i}}{B}^{\text{ii}}{X}_{6}$, based on monolayer ${\mathrm{Cr}}_{2}{\mathrm{Ge}}_{2}{\mathrm{Te}}_{6}$, calculated from first principles. By learning the relationships between both local and global magnetic properties, we demonstrate an improvement over models that only consider global magnetic properties. Materials science ; van der Waals force ; Magnet ; Artificial neural network ; Condensed matter physics ; Artificial intelligence ; Computer science ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Machine Learning and ELM ; Ferroelectric and Negative Capacitance Devices
Authors: Jonathan G. M. Conn | Abdullah Ahmad | David Scott Palmer
Source: Liquids (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems Understanding the interactions between solutes and solvents is vital in many areas of the chemical sciences. Solvation free energy (SFE) is an important thermodynamic property in characterising molecular solvation and so accurate prediction of this property is sought after. The One-Dimensional Reference Interaction Site Model (RISM) is a well-established method for modelling solvation, but it is known to yield large errors in the calculation of SFE. In this work, we show that a single machine learning free energy functional for RISM can accurately model solvation thermodynamics in multiple solvents. A convolutional neural network is trained on solvation free energy density functions calculated by RISM for small organic molecules in approximately 100 different solvent systems. We achieve an average RMSE of 1.41 kcal/mol and an R2 of 0.89 across all solvent systems. We also compare the performance for the most and least commonly represented solvents and show that higher accuracy is generally seen with higher volumes of data, with RMSE values of 0.69–1.29 kcal/mol and R2 values of 0.78–0.97 for solvents with more than 50 data points. We have shown that machine learning can greatly improve solvation free energy predictions in RISM, while demonstrating that the methodology is generalisable across solvent systems. This represents a significant step towards a universal machine learning SFE functional for RISM. Solvation ; Energy (signal processing) ; Chemistry ; Computer science ; Computational chemistry ; Solvent ; Physics ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Yasser GadelHak | Ayyaz Muhammad | Marwa El‐Azazy | Ahmed S. El‐Shafie | Mohamed F. Shibl | Rehab Mahmoud
Source: ChemBioEng Reviews (review, 2024-07-18)
Field: Chemistry — Organic Chemistry

Computer‐Aided Design of Large‐Scale Nanomaterials Synthesis Processes: A Detailed Review Abstract Efforts from the scientific community and the private sector are required to lower the costs of large‐scale production of nanomaterials (NMs) to enhance their commercialization. In this work, the computer‐aided process design of large‐scale NM synthesis procedures is comprehensively reviewed. Moreover, a generalized process flow diagram for all large‐scale production processes was constructed by surveying numerous scalable experimental procedures reported in the literature. Previous studies reporting simulation cases of large‐scale production processes of NMs and nanocomposites (NCs) are also reviewed based on the type of material produced, e.g., oxides, sulfides, carbonaceous materials, organic materials, metals, and other types of NMs. Finally, technical insights from classical chemical engineering specializations, such as altering process configurations, optimizing process variables, integrating chemical processes, utilizing renewable energy sources, conducting computational calculations, employing machine learning techniques, and studying the process's environmental impact, are reviewed for large‐scale NMs and NCs synthesis. Scale (ratio) ; Nanomaterials ; Computer science ; Nanotechnology ; Systems engineering ; Engineering ; Materials science ; Physics Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Layered Double Hydroxides Synthesis and Applications
Authors: Eleonora Casillo | Benon P. Maliszewski | César A. Urbina‐Blanco | Thomas Scattolin | Catherine S. J. Cazin | Steven P. Nolan
Source: Chemical Communications (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning directed discovery and optimisation of a platinum-catalysed amide reduction The discovery and optimisation of reaction conditions leading to the reduction of amides, a fundamental large-scale industrial reaction, is achieved using a machine learning (ML) platform and a platinum catalyst. The optimisation leads to the discovery of a new platinum-based catalytic system that displays unexpectedly high performance. The approach enables rapid and high conversions at ppm-level catalyst loadings. Reduction (mathematics) ; Amide ; Platinum ; Chemistry ; Combinatorial chemistry ; Drug discovery ; Computer science ; Catalysis ; Organic chemistry ; Biochemistry ; Mathematics Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Machine Learning in Materials Science
Authors: Michael A. Bellucci | Lina Yuan | Grahame R. Woollam | Bing Wang | Liwen Fang | Yunfei Zhou | Chandler Greenwell | Sivakumar Sekharan | X. Ling | Guangxu Sun
Source: Molecular Pharmaceutics (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Templated Nucleation of Clotrimazole and Ketoprofen on Polymer Substrates The use of different template surfaces in crystallization experiments can directly influence the nucleation kinetics, crystal growth, and morphology of active pharmaceutical ingredients (APIs). Consequently, templated nucleation is an attractive approach to enhance crystal nucleation kinetics and preferentially nucleate desired crystal polymorphs for solid-form drug molecules, particularly large and flexible molecules that are difficult to crystallize. Herein, we investigate the effect of polymer templates on the crystal nucleation of clotrimazole and ketoprofen with both experiments and computational methods. Crystallization was carried out in toluene solvent for both APIs with a template library consisting of 12 different polymers. In complement to the experimental studies, we developed a computational workflow based on molecular dynamics (MD) and derived descriptors from the simulations to score and rank API-polymer interactions. The descriptors were used to measure the energy of interaction (EOI), hydrogen bonding, and rugosity (surface roughness) similarity between the APIs and polymer templates. We used a variety of machine learning models (14 in total) along with these descriptors to predict the crystallization outcome of the polymer templates. We found that simply rank-ordering the polymers by their API-polymer interaction energy descriptors yielded 92% accuracy in predicting the experimental outcome for clotrimazole and ketoprofen. The most accurate machine learning model for both APIs was found to be a random forest model. Using these models, we were able to predict the crystallization outcomes for all polymers. Additionally, we have performed a feature importance analysis using the trained models and found that the most predictive features are the energy descriptors. These results demonstrate that API-polymer interaction energies are correlated with heterogeneous crystallization outcomes. Crystallization ; Nucleation ; Polymer ; Materials science ; Template ; Crystallization of polymers ; Chemistry ; Nanotechnology ; Organic chemistry ; Composite material Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Angela F. Harper | Tabea Huss | S. Kocher | Christoph Scheurer
Source: ChemRxiv (preprint, 2024-04-16)
Field: Chemistry — Spectroscopy

Tracking Li atoms in real-time with ultra-fast NMR simulations We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material Li3PS4 (LPS) by calculating quadrupolar frequencies of 7Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes. Therefore only a few machine learning force field schemes are able to capture the time and length scales required for accurate comparison with experimental results. By using a new class of machine learning interatomic potentials, known as ultra-fast potentials (UFPs), we are able to efficiently access timescales beyond the microsecond regime. In tandem, we have developed a machine learning model for predicting the full 7Li electric field gradient (EFG) tensors in LPS. By combining the long timescale trajectories from the UFP with our model for 7Li EFG tensors, we are able to extract the autocorrelation function (ACF) for 7Li quadrupolar frequencies during Li diffusion. We extract the decay constants from the ACF for both crystalline β-LPS and amorphous LPS, and find that the predicted Li hopping rates are on the same order of magnitude as those predicted from the Li dynamics. This demonstrates the potential for machine learning to finally make predictions on experimentally relevant timescales and temperatures, and opens a new avenue of NMR crystallography: using machine learning dynamical NMR simulations for accessing polycrystalline and glass ceramic materials. Microsecond ; Molecular dynamics ; Solid-state nuclear magnetic resonance ; Diffusion ; Chemistry ; Chemical physics ; Amorphous solid ; Computational physics ; Materials science ; Nuclear magnetic resonance ; Physics ; Computational chemistry ; Crystallography ; Thermodynamics ; Optics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: Max C. Gallant | Matthew J. McDermott | Bryant Y. Li | Kristin A. Persson
Source: Chemistry of Materials (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO<sub>3</sub>, CaZrN<sub>2</sub>, and YMnO<sub>3</sub> found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids <i>in silico</i>. Computer science ; Statistical physics ; Biological system ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Caihong Lv | Yuewei Zheng | Zhihao Guan | Jun Qian | Houbin Li | Xinghai Liu
Source: Frontiers of Chemical Science and Engineering (article, 2024-07-05)
Field: Materials Science — Materials Chemistry

Research of synthesis and neural network training on double quantum dot colorimetric fluorescent probe for freshness detection Fluorescence ; Quantum dot ; Artificial neural network ; Training (meteorology) ; Nanotechnology ; Optoelectronics ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Physics ; Optics ; Meteorology Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Kashish Narang | Jayant Kumar | Shreya Saxena | Meena Chaudhary | Narender | Gunjan Chandwani
Source:  (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Smart Computing Optimization of the Bactericidal Potential of ZnO Nanoparticles: A Support Vector Machine Approach ZnO nanoparticles are tiny particles of zinc oxide, typically ranging from 1 to 100 nanometers, exhibiting unique physical and chemical properties suitable for applications in various fields, including optoelectronics, catalysis, and antibacterial coatings. The optimization of ZnO nanoparticle synthesis faces challenges in precisely controlling particle size, morphology, and dispersity, hindering the fine-tuning required for specific applications. The analysis of ZnO nanoparticle optimization involves scrutinizing parameters like precursor concentration and reaction conditions, aiming to finely control particle size and morphology for tailored applications. SVM (support vector machine) in machine learning can be applied to model the relationship between synthesis parameters and desired antibacterial properties of ZnO nanoparticles. The future scope of optimizing ZnO nanoparticles for antibacterial properties lies in further tailoring synthesis parameters to enhance both efficacy and biocompatibility. Advanced research may focus on fine-tuning surface modifications and doping strategies to improve antibacterial activity while minimizing potential cytotoxic effects, paving the way for the development of advanced antibacterial agents with applications in healthcare, water treatment, and other fields. Support vector machine ; Computer science ; Nanoparticle ; Artificial intelligence ; Nanotechnology ; Materials science Advanced Nanomaterials in Catalysis ; Water Quality Monitoring and Analysis ; Diverse Scientific Research Studies
Authors: Dario Baratella | Omar Abou El Kheir | Marco Bernasconi
Source: Acta Materialia (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Crystallization kinetics in Ge-rich Ge<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e1384"><mml:msub><mml:mrow/><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math>Te alloys from large scale simulations with a machine-learned interatomic potential A machine-learned interatomic potential for Ge-rich Ge x Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge x Te alloy. Large scale molecular dynamics simulations have suggested a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess. Scalable Vector Graphics ; Germanium ; Kinetics ; Materials science ; Crystallization ; Thermodynamics ; Computer science ; Physics ; World Wide Web ; Silicon ; Metallurgy Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Thin-Film Transistor Technologies
Authors: Lakshmi Manasa Birada | Vyshnavi Pullela | Thella Babu Rao
Source: Journal of Bio- and Tribo-Corrosion (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Triboinformatic Machine Learning Model for Frictional Behavior and Wear Volume Loss Prediction of SS316L Alloy Clad with WC/NiCrBSi Alloy ; Volume (thermodynamics) ; Materials science ; Metallurgy ; Mechanical engineering ; Composite material ; Engineering ; Thermodynamics Metal Alloys Wear and Properties ; Advanced materials and composites ; Metal and Thin Film Mechanics