jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Umer Shahzad | Hadi M. Marwani | Mohsin Saeed | Abdullah M. Asiri | Md. Reazuddin Repon | Raed H. Althomali | Mohammed M. Rahman
Source: The Chemical Record (review, 2023-11-20)
Field: Materials Science — Materials Chemistry

Progress and Perspectives on Promising Covalent‐Organic Frameworks (COFs) Materials for Energy Storage Capacity In recent years, a new class of highly crystalline advanced permeable materials covalent-organic frameworks (COFs) have garnered a great deal of attention thanks to their remarkable properties, such as their large surface area, highly ordered pores and channels, and controllable crystalline structures. The lower physical stability and electrical conductivity, however, prevent them from being widely used in applications like photocatalytic activities and innovative energy storage and conversion devices. For this reason, many studies have focused on finding ways to improve upon these interesting materials while also minimizing their drawbacks. This review article begins with a brief introduction to the history and major milestones of COFs development before moving on to a comprehensive exploration of the various synthesis methods and recent successes and signposts of their potential applications in carbon dioxide (CO<sub>2</sub> ) sequestration, supercapacitors (SCs), lithium-ion batteries (LIBs), and hydrogen production (H<sub>2</sub> -energy). In conclusion, the difficulties and potential of future developing with highly efficient COFs ideas for photocatalytic as well as electrochemical energy storage applications are highlighted. Supercapacitor ; Electrochemical energy storage ; Nanotechnology ; Energy storage ; Photocatalysis ; Materials science ; Hydrogen storage ; New energy ; Lithium (medication) ; Electrochemistry ; Engineering ; Chemistry ; Mechanical engineering ; Electrode ; Physics ; Power (physics) Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Jaclyn Dunne | Jake Griner | Martin J. Romeo | Jade K. Macdonald | Carsten Krieg | Mark J. Lim | Gargey Yagnik | Kenneth J. Rothschild | Richard R. Drake | Anand S. Mehta | Peggi M. Angel
Source: Analytical and Bioanalytical Chemistry (article, 2023-10-16)
Field: Chemistry — Spectroscopy

Evaluation of antibody-based single cell type imaging techniques coupled to multiplexed imaging of N-glycans and collagen peptides by matrix-assisted laser desorption/ionization mass spectrometry imaging The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with single cell spatial omics methods allows for a comprehensive investigation of single cell spatial information and matrisomal N-glycan and extracellular matrix protein imaging. Here, the performance of the antibody-directed single cell workflows coupled with MALDI-MSI are evaluated. Miralys™ photocleavable mass-tagged antibody probes (MALDI-IHC, AmberGen, Inc.), GeoMx DSP® (NanoString, Inc.), and Imaging Mass Cytometry (IMC, Standard BioTools Inc.) were used in series with MALDI-MSI of N-glycans and extracellular matrix peptides on formalin-fixed paraffin-embedded tissues. Single cell omics protocols were performed before and after MALDI-MSI. The data suggests that for each modality combination, there is an optimal order for performing both techniques on the same tissue section. An overall conclusion is that MALDI-MSI studies may be completed on the same tissue section as used for antibody-directed single cell modalities. This work increases access to combined cellular and extracellular information within the tissue microenvironment to enhance research on the pathological origins of disease. Mass spectrometry imaging ; MALDI imaging ; Chemistry ; Mass spectrometry ; Glycan ; Matrix-assisted laser desorption/ionization ; Extracellular matrix ; Molecular imaging ; Mass cytometry ; Matrix (chemical analysis) ; Biochemistry ; Chromatography ; Desorption ; Biology ; Glycoprotein Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Jiwon Sun | Seungpyo Kang | Joonchul Kim | Kyoungmin Min
Source: ACS Applied Materials & Interfaces (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of promising ASSBs, solid-state electrolytes (SSEs) are essential to achieve structural integrity. Thus, in this study, a machine-learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. The well-known Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> structure was used as a base material, and 73 chemical elements were substituted on La and Zr sites, leading to 5329 potential structures. First, the elasticity database and machine learning descriptors were adopted from previous studies. Subsequently, the machine-learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by first-principles calculations for validation. Furthermore, the active learning process demonstrated that it can effectively decrease prediction uncertainty. Finally, the ionic conductivity of the mechanically superior materials was predicted to suggest optimal SSE candidates. Then, ab initio molecular dynamics simulations are followed for confirmation of diffusion behavior for materials classified as superionic; 10 new tetragonal-phase garnet SSEs are verified with superior mechanical and ionic conductivity properties. We believe that the current model and the constructed database will become a cornerstone for the development of next-generation SSE materials. Materials science ; Ionic conductivity ; Tetragonal crystal system ; Ionic bonding ; Electrolyte ; Fast ion conductor ; Ab initio ; Lithium (medication) ; Machine learning ; Nanotechnology ; Computer science ; Phase (matter) ; Ion ; Artificial intelligence ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Baiqing Li | Shimin Su | Chan Zhu | Jie Lin | Xinyue Hu | Lebin Su | Zhunzhun Yu | Kuangbiao Liao | Hongming Chen
Source: Journal of Cheminformatics (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R<sup>2</sup> of 0.712) was obtained on our in-house data. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction. Computer science ; Workflow ; Throughput ; Artificial intelligence ; Artificial neural network ; Graph ; Machine learning ; Deep learning ; Data mining ; Database ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Run Han | Zhuyifan Ye | Yunsen Zhang | Yaxin Cheng | Ying Zheng | Defang Ouyang
Source: Asian Journal of Pharmaceutical Sciences (article, 2023-04-16)
Field: Materials Science — Biomaterials

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future. Liposome ; Zeta potential ; Molecular dynamics ; Drug delivery ; Biological system ; Chemistry ; Dispersity ; Molecular descriptor ; Nanotechnology ; Computer science ; Materials science ; Computational chemistry ; Stereochemistry ; Organic chemistry ; Quantitative structure–activity relationship ; Nanoparticle Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Advanced Drug Delivery Systems
Authors: Hilal Daglar | Hasan Can Gülbalkan | Nitasha Habib | Özce Durak | Alper Uzun | Seda Keskın
Source: ACS Applied Materials & Interfaces (article, 2023-03-27)
Field: Chemistry — Inorganic Chemistry

Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF<sub>4</sub>]/MOF Composites for CO<sub>2</sub>/N<sub>2</sub> Separation Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-<i>n</i>-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF<sub>4</sub>]) with a large variety of MOFs for CO<sub>2</sub> and N<sub>2</sub> adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF<sub>4</sub>]/MOF composites. The most important features that affect the CO<sub>2</sub>/N<sub>2</sub> selectivity of composites were extracted from ML and utilized to computationally generate an IL/MOF composite, [BMIM][BF<sub>4</sub>]/UiO-66, which was not present in the original material data set. This composite was finally synthesized, characterized, and tested for CO<sub>2</sub>/N<sub>2</sub> separation. Experimentally measured CO<sub>2</sub>/N<sub>2</sub> selectivity of the [BMIM][BF<sub>4</sub>]/UiO-66 composite matched well with the selectivity predicted by the ML model, and it was found to be comparable, if not higher than that of all previously synthesized [BMIM][BF<sub>4</sub>]/MOF composites reported in the literature. Our proposed approach of combining molecular simulations with ML models will be highly useful to accurately predict the CO<sub>2</sub>/N<sub>2</sub> separation performances of any [BMIM][BF<sub>4</sub>]/MOF composite within seconds compared to the extensive time and effort requirements of purely experimental methods. Ionic liquid ; Selectivity ; Materials science ; Tetrafluoroborate ; Composite number ; Adsorption ; Composite material ; Physical chemistry ; Organic chemistry ; Catalysis ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: A. I. Gircha | Aleksey S. Boev | K. Avchaciov | П. О. Федичев | Aleksey K. Fedorov
Source: Scientific Reports (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Hybrid quantum-classical machine learning for generative chemistry and drug design Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications. chEMBL ; Chemical space ; Computer science ; Autoencoder ; Drug discovery ; Boltzmann machine ; Restricted Boltzmann machine ; Artificial intelligence ; Quantum chemical ; Deep learning ; Generative model ; Quantum ; Quantum chemistry ; Theoretical computer science ; Generative grammar ; Machine learning ; Chemistry ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Vahid Attari | Danial Khatamsaz | Douglas Allaire | Raymundo Arróyave
Source: Acta Materialia (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders Materials science ; Microstructure ; Thermal conductivity ; Inverse ; Material Design ; Thermal diffusivity ; Artificial intelligence ; Phase (matter) ; Deep learning ; Generative model ; Autoencoder ; Machine learning ; Generative grammar ; Computer science ; Thermodynamics ; Mathematics ; Composite material ; Physics ; Geometry Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Jinzhe Zeng | Yujun Tao | Timothy J. Giese | Darrin M. York
Source: Journal of Chemical Theory and Computation (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery. Protonation ; Deprotonation ; Chemistry ; Potential energy ; Electrostatics ; Force field (fiction) ; Drug discovery ; Computational chemistry ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hyuk Jin Kim | Minsu Chong | Tae Gyu Rhee | Yeong Gwang Khim | Min‐Hyoung Jung | Young‐Min Kim | Hu Young Jeong | Byoung Ki Choi | Young Jun Chang
Source: Nano Convergence (article, 2023-02-20)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe<sub>2</sub>) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe<sub>2</sub> layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques. Reflection high-energy electron diffraction ; Thin film ; Electron diffraction ; Molecular beam epitaxy ; Materials science ; Graphene ; Epitaxy ; Substrate (aquarium) ; Diffraction ; Nanotechnology ; Optoelectronics ; Optics ; Physics ; Geology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Xinzhong Song | Jia Man | Yinghua Qiu | Jiali Wang | Jianing Liu | Ruijian Li | Yongqi Zhang | Jianyong Li | Jianfeng Li | Yuguo Chen
Source: Acta Biomaterialia (article, 2023-12-19)
Field: Materials Science — Surfaces, Coatings and Films

Design, preparation, and characterization of lubricating polymer brushes for biomedical applications Materials science ; Characterization (materials science) ; Polymer ; Nanotechnology ; Polymer science ; Composite material Polymer Surface Interaction Studies ; Hemostasis and retained surgical items ; Bone Tissue Engineering Materials
Authors: Cheuk Chi Albert Ng | Yin Zhou | Zhong‐Ping Yao
Source: Analytica Chimica Acta (review, 2023-05-08)
Field: Chemistry — Spectroscopy

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review Chemistry ; Tandem mass spectrometry ; Computational biology ; DNA sequencing ; Mass spectrometry ; Peptide ; Tandem mass tag ; Proteomics ; Chromatography ; Biochemistry ; Gene ; Quantitative proteomics ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Yucong Li | Ruofei Zhang | Xiyun Yan | Kelong Fan
Source: Journal of Materials Chemistry B (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning facilitating the rational design of nanozymes As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics. Rational design ; Obstacle ; Computer science ; Face (sociological concept) ; Nanotechnology ; Artificial intelligence ; Machine learning ; Materials science ; Political science Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Shams Kalam | Muhammad Arif | Arshad Raza | Najeebullah Lashari | Mohamed Mahmoud
Source: International Journal of Coal Geology (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Data-driven modeling to predict adsorption of hydrogen on shale kerogen: Implication for underground hydrogen storage Kerogen ; Adsorption ; Oil shale ; Hydrogen ; Gradient boosting ; Genetic programming ; Computer science ; Chemistry ; Artificial intelligence ; Geology ; Engineering ; Random forest ; Organic chemistry ; Waste management Hydrogen Storage and Materials ; Hydrocarbon exploration and reservoir analysis ; Methane Hydrates and Related Phenomena