jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikhail G. Andronov | Varvara Voinarovska | Natalia Andronova | Michael Wand | Djork-Arné Clevert | Jürgen Schmidhuber
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Reagent prediction with a molecular transformer improves reaction data quality Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions. In particular, reagent prediction for arbitrary reactions, a crucial aspect of condition recommendation, has been largely overlooked in cheminformatics until recently. Here we employ the Molecular Transformer, a state-of-the-art model for reaction prediction and single-step retrosynthesis, to tackle this problem. We train the model on the US patents dataset (USPTO) and test it on Reaxys to demonstrate its out-of-distribution generalization capabilities. Our reagent prediction model also improves the quality of product prediction: the Molecular Transformer is able to substitute the reagents in the noisy USPTO data with reagents that enable product prediction models to outperform those trained on plain USPTO. This makes it possible to improve upon the state-of-the-art in reaction product prediction on the USPTO MIT benchmark. Reagent ; Transformer ; Combinatorial chemistry ; Computer science ; Quality (philosophy) ; Chemistry ; Process engineering ; Organic chemistry ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Zahra Rajabimashhadi | R. Naghizadeh | Ashkan Zolriasatein | Sonia Bagheri | Claudio Mele | Carola Esposito Corcione
Source: Polymers (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Hydrophobic, Mechanical, and Physical Properties of Polyurethane Nanocomposite: Synergistic Impact of Mg(OH)2 and SiO2 Polyurethane (PU) is one of the most well-known polymer coatings because of its favorable characteristics, which include its low density, nontoxicity, nonflammability, longevity, adhesion, simple manufacture, flexibility, and hardness. However, PU does come with several major drawbacks, among which are poor mechanical properties as well as low thermal and chemical stability, particularly in the high-temperature mode, where becomes gets flammable and loses adhesion ability. The limitations have inspired researchers to develop a PU composite to improve the weaknesses by adding different reinforcements. Magnesium hydroxide, having the ability to be produced with exceptional properties such as flammability, has consistently attracted the interest of researchers. Additionally, silica nanoparticles with high strength and hardness are one of the excellent reinforcements of polymers these days. The hydrophobic, physical, and mechanical properties of pure polyurethane and the composite type (nano, micro, and hybrid) fabricated with the drop casting method were examined in this study. 3-Aminopropyl triethoxysilane was applied as a functionalized agent. To confirm that hydrophilic particles turned into hydrophobic, FTIR analysis was carried out. The impact of size, percentage, and kind of fillers on different properties of PU/Mg(OH)2-SiO2 was then investigated using different analyses including spectroscopy and mechanical and hydrophobicity tests. The resultant observations demonstrated that different surface topographies can be obtained from the presence of particles of different sizes and percentages on the hybrid composite's surface. Surface roughness allowed for exceptionally high water contact angles, which confirmed the hybrid polymer coatings' superhydrophobic properties. According to the particle size and content, the distribution of fillers in the matrix also improved the mechanical properties. Materials science ; Composite material ; Contact angle ; Polyurethane ; Nanocomposite ; Triethoxysilane ; Polymer ; Composite number ; Thermal stability ; Wetting ; Chemical engineering Magnesium Oxide Properties and Applications ; Polymer composites and self-healing ; Silicone and Siloxane Chemistry
Authors: Man‐Fai Ng | Yongming Sun | Zhi Wei Seh
Source: Energy Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-inspired battery material innovation Data-driven machine learning is a proven technique for battery material discovery and enables the development of sustainable next-generation batteries. Battery (electricity) ; Computer science ; Artificial intelligence ; Physics ; Power (physics) ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Akshay Ajagekar | Fengqi You
Source: npj Computational Materials (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Molecular design with automated quantum computing-based deep learning and optimization Abstract Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility. Computer science ; Bayesian optimization ; Property (philosophy) ; Artificial intelligence ; Task (project management) ; Probabilistic logic ; Chemical space ; Deep learning ; Machine learning ; Optimization problem ; Energy (signal processing) ; Algorithm ; Systems engineering ; Engineering Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Muhammad Ishfaq | Tayyaba Mubashir | Safaa N. Abdou | Mudassir Hussain Tahir | Mohamed Ibrahim Halawa | Mohamed M. Ibrahim | Yulin Xie
Source: Heliyon (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications. Polymer ; Absorption (acoustics) ; Process (computing) ; Absorbance ; Photoacoustic imaging in biomedicine ; Computer science ; Similarity (geometry) ; Materials science ; Nanotechnology ; Biological system ; Artificial intelligence ; Chemistry ; Optics ; Physics ; Image (mathematics) ; Chromatography ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Photoacoustic and Ultrasonic Imaging
Authors: Zhanpeng Sun | Zijun Qi | Kang Liang | Xiang Sun | Zhaofu Zhang | Lijie Li | Qijun Wang | Guoqing Zhang | Gai Wu | Wei Shen
Source: Applied Physics Letters (article, 2023-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A neuroevolution potential for predicting the thermal conductivity of <b> <i>α</i> </b>, <b> <i>β</i> </b>, and <b> <i>ε</i> </b>-Ga2O3 Ga2O3 is an ultrawide-bandgap semiconductor with a variety of crystal configurations, which has the potential for a variety of applications, especially in power electronics and ultraviolet optoelectronics. However, there has been no single interatomic potential reported for Ga2O3 polymorphs in terms of molecular dynamics prediction of thermal conductivity. Here, one interatomic potential has been developed based on neural networks, which has the clear advantages of consuming less computational power than density functional theory and has high accuracy in predicting the thermal conductivity of the three polymorphs of Ga2O3. Using the neuroevolution potential, the thermal conductivity values at 300 K have been predicted. Hence, the κ[average-α] was 67.2% that of β-Ga2O3, and the κ[average-ε] was only 26.4% that of β-Ga2O3. The possible reasons for the discrepancies in thermal conductivity values in various crystal types and orientations have been explored. As a result, it could be shown that the contribution of low-frequency phonons to thermal conductivity was very significant in Ga2O3, and a unit cell with low symmetry and high atomic number would negatively impact the thermal conductivity of the material. In this work, a scheme has been proposed for accurately predicting the thermal conductivity of Ga2O3 and a relatively accurate value of the thermal conductivity of ε-Ga2O3 has been achieved, which could also provide an atomic-scale perspective for the insight into the thermal conductivity differences among α, β, and ε-Ga2O3. Thermal conductivity ; Phonon ; Materials science ; Interatomic potential ; Molecular dynamics ; Conductivity ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry ; Physical chemistry ; Composite material Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Tianzhao Hu | Juan Li | Yuzuo Wang | Shaorui Chen | Tong Yu | Hui–Ming Cheng | Zhenhua Sun | Qun Xu | Feng Li
Source: Joule (article, 2023-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Coupling between cathode and anode in hybrid charge storage Anode ; Cathode ; Coupling (piping) ; Capacitor ; Materials science ; Electrochemistry ; Electrolyte ; Energy storage ; Electrode ; Matching (statistics) ; Electrical engineering ; Voltage ; Optoelectronics ; Electronic engineering ; Chemistry ; Engineering ; Power (physics) ; Physics ; Metallurgy ; Thermodynamics Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Kevin K. Huguenin-Dumittan | Philip Loche | Ni Haoran | Michele Ceriotti
Source: The Journal of Physical Chemistry Letters (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrostatic or dispersion interactions. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body nonbonded interactions in the data-driven modeling of matter. Lode ; Multipole expansion ; Computer science ; Locality ; Equivariant map ; Scaling ; Extension (predicate logic) ; Theoretical computer science ; Set (abstract data type) ; Range (aeronautics) ; Statistical physics ; Theoretical physics ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Programming language ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Anjana S. Desai | Aparna Ashok | Zehra Edis | Samir Haj Bloukh | Mayur Gaikwad | Rajendra Patil | Brajesh Pandey | Neeru Bhagat
Source: International Journal of Molecular Sciences (review, 2023-02-20)
Field: Materials Science — Materials Chemistry

Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R<sup>2</sup>. The obtained high value of R<sup>2</sup> and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments. Mean squared error ; Cytotoxicity ; Random forest ; Viability assay ; Zeta potential ; Machine learning ; Biological system ; Nanoparticle ; Silver nanoparticle ; Support vector machine ; Computer science ; Artificial intelligence ; Mathematics ; Chemistry ; Nanotechnology ; Materials science ; Statistics ; In vitro ; Biology ; Biochemistry Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Jing Wu | Jiyuan Yang | Yuan-Jinsheng Liu | Duo Zhang | Yudi Yang | Yuzhi Zhang | Linfeng Zhang | Shi Liu
Source: Physical review. B./Physical review. B (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Universal interatomic potential for perovskite oxides With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parametrized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parametrized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field (UniPero) that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental temperature-driven phase transition sequences for several markedly different ferroelectric oxides, including a six-element ternary solid solution $\mathrm{Pb}({\mathrm{In}}_{1/2}{\mathrm{Nb}}_{1/2}){\mathrm{O}}_{3}\text{--}\mathrm{Pb}({\mathrm{Mg}}_{1/3}{\mathrm{Nb}}_{2/3}){\mathrm{O}}_{3}\text{--}{\mathrm{PbTiO}}_{3}$. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD. Perovskite (structure) ; Force field (fiction) ; Interatomic potential ; Molecular dynamics ; Materials science ; Solid solution ; Transferability ; Physics ; Chemical physics ; Crystallography ; Computer science ; Chemistry ; Computational chemistry ; Machine learning ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Multiferroics and related materials
Authors: Z H Qiao | Shengzhi Dong | Qing Li | Xiangming Lu | Renjie Chen | Shuai Guo | Aru Yan | Wei Li
Source: Journal of Alloys and Compounds (article, 2023-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Performance prediction models for sintered NdFeB using machine learning methods and interpretable studies Neodymium magnet ; Process (computing) ; Generalization ; Interpretation (philosophy) ; Computer science ; Magnet ; Work (physics) ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics ; Mechanical engineering ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Muhamad Akrom | T. Sutojo | Ayu Pertiwi | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Journal of Physics Conference Series (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds Abstract Corrosion is a major concern for the industrial and academic sectors because it causes significant losses in many fields. Currently, there is a great deal of interest in the topic of material damage control using organic chemicals. Pyridine and quinoline are potential corrosion inhibitors because they are non-toxic, inexpensive, and efficient in various corrosive conditions. Experimental studies in searching for candidate corrosion inhibitor candidates require a lot of time, cost, and labor intensive. Using a machine learning (ML) strategy based on a quantitative structure-property relationship (QSPR) model, we evaluate gradient boosting regressor (GBR), support vector regression (SVR), and k-nearest neighbor (KNN) algorithms as predictive models to investigate corrosion inhibition efficiency (CIE) of pyridine-quinoline compounds in this study. We found that the GBR model, when compared with the SVR and KNN models as well as models from the literature for the pyridine-quinoline compound dataset, has the best predictive performance based on the metric coefficient of determination (R 2 ) and root mean square error (RMSE). Overall, our study provides a new perspective on how the ML model can estimate the effectiveness of corrosion prevention on iron surfaces by organic inhibitor compounds. Quantitative structure–activity relationship ; Quinoline ; Corrosion ; Pyridine ; Mean squared error ; Metric (unit) ; Molecular descriptor ; Predictive modelling ; Support vector machine ; Computer science ; k-nearest neighbors algorithm ; Corrosion inhibitor ; Artificial intelligence ; Machine learning ; Biological system ; Materials science ; Mathematics ; Chemistry ; Engineering ; Statistics ; Organic chemistry ; Metallurgy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Allison B. Esselman | Nathan Heath Patterson | Lukasz G. Migas | Martin Dufresne | Katerina Djambazova | Madeline E. Colley | Raf Van de Plas | Jeffrey M. Spraggins
Source: Journal of the American Society for Mass Spectrometry (article, 2023-06-15)
Field: Chemistry — Spectroscopy

Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and <i>k</i>-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise <i>k</i>-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease. Mass spectrometry imaging ; MALDI imaging ; Microscopy ; Chemistry ; Molecular imaging ; Resolution (logic) ; Glomerulus ; Renal glomerulus ; Image resolution ; Kidney Glomerulus ; Mass spectrometry ; Artificial intelligence ; Matrix-assisted laser desorption/ionization ; Kidney ; Pathology ; Computer science ; Biology ; Chromatography ; In vivo ; Medicine Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Proteomics Techniques and Applications