jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai‐Hung Huang | Nicolás M. Morato | Yunfei Feng | Alexis Toney | R. Graham Cooks
Source: Journal of the American Chemical Society (article, 2024-11-19)
Field: Chemistry — Spectroscopy

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the <i>m</i>/<i>z</i> value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery. Chemistry ; Desorption electrospray ionization ; Chemical space ; Mass spectrometry ; Reagent ; Electrospray ionization ; Combinatorial chemistry ; Tandem mass spectrometry ; Desorption ; Chemical ionization ; Chemical reaction ; Redox ; Drug discovery ; Ionization ; Chromatography ; Organic chemistry ; Adsorption Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sakda Jampasa | Wisarut Khamcharoen | Supacha Wirojsaengthong | Akkapol Suea‐Ngam | Surinya Traipop | Tuğba Özer | Fuangfa Unob | Pumidech Puthongkham | Orawon Chailapakul
Source: TrAC Trends in Analytical Chemistry (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Recent advances and trends in the applications of nanomaterials in optical sensing platforms Nanomaterials ; Nanotechnology ; Optical sensing ; Materials science ; Computer science ; Environmental science ; Optoelectronics Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Kuntesh Rawal | Patel Dixita Devendrabhai | Pratik M. Pataniya | Prince Jain | Anand Joshi | G. K. Solanki | Mohit Tannarana
Source: Optical Materials (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Versatile photo-sensing ability of paper based flexible 2D-Sb0.3Sn0.7Se2 photodetector and performance prediction with machine learning algorithm Photodetector ; Algorithm ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Optoelectronics 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Chalcogenide Semiconductor Thin Films
Authors: Munish Kumar Gupta | Mehmet Erdi Korkmaz | Aleksander Karolczuk | Nimel Sworna Ross | Govind Vashishtha | Jolanta Królczyk | Recep Demirsöz | Grzegorz Królczyk
Source: Tribology International (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

A study on friction induced tribological characteristics of steel 316 L against 100 cr6 alloy under different lubricating conditions with machine learning model Tribology ; Alloy ; Materials science ; Metallurgy ; Composite material Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced machining processes and optimization
Authors: Yuhui Hong | Christopher J. Welch | Patrick Piras | Haixu Tang
Source: Analytical Chemistry (article, 2024-02-03)
Field: Chemistry — Spectroscopy

Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations The accurate prediction of suitable chiral stationary phases (CSPs) for resolving the enantiomers of a given compound poses a significant challenge in chiral chromatography. Previous attempts at developing machine learning models for structure-based CSP prediction have primarily relied on 1D SMILES strings [the simplified molecular-input line-entry system (SMILES) is a specification in the form of a line notation for describing the structure of chemical species using short ASCII strings] or 2D graphical representations of molecular structures and have met with only limited success. In this study, we apply the recently developed 3D molecular conformation representation learning algorithm, which uses rapid conformational analysis and point clouds of atom positions in the 3D space, enabling efficient chemical structure-based machine learning. By harnessing the power of the rapid 3D molecular representation learning and a data set comprising over 300,000 chromatographic enantioseparation records sourced from the literature, our models afford notable improvements for the chemical structure-based choice of appropriate CSP for enantioseparation, paving the way for more efficient and informed decision-making in the field of chiral chromatography. Chemistry ; Enantiomer ; Representation (politics) ; Chemical space ; Notation ; Drug discovery ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Bin Cao | Tianhao Su | Shuting Yu | Tianyuan Li | Taolue Zhang | Jincang Zhang | Ziqiang Dong | Tong‐Yi Zhang
Source: Materials & Design (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys Developing new materials through traditional trial-and-error is time-consuming and costly due to the vast potential compositions to explore. Moreover, some desirable properties are mutually exclusive, presenting challenges for materials design. Herein, we employed an active learning strategy to address the strength-ductility trade-off in SAC105 lead-free solders. We developed two Gaussian regression models, one for strength and one for elongation representing ductility. The Gaussian Upper Confidence Boundary algorithm, a Bayesian optimization method, was used to balance exploitation and exploration by considering prediction uncertainty. The variance weight was designed to decay with adaptive iterations. The two models were linearly combined and the maximal combined model recommended alloys on the Pareto front for experiments. After just three iterations, a new 91.4Sn-1.0Ag-0.5Cu-1.5Bi-4.4In-0.2Ti low-silver SAC solder was discovered, exhibiting 73.94±5.05 MPa strength and 24.37±5.92% elongation, representing the best comprehensive mechanical properties. Weldability experiments also showed excellent performance. Based on Ab initio molecular dynamics, a precise machine learning potential revealed the alloying mechanism in Sn-In solid solutions with quantum accuracy in systems of over 103 atoms, closely matching experimental X-ray diffraction pattern. To facilitate materials informatics development, all active learning algorithms was made open-source in our designed framework, Bgolearn. Weldability ; Materials science ; Ductility (Earth science) ; Bayesian optimization ; Soldering ; Materials informatics ; Mechanical engineering ; Computer science ; Metallurgy ; Machine learning ; Alloy Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Electronic Packaging and Soldering Technologies
Authors: Kaiwei Wan | Hui Wang | Xinghua Shi
Source: ACS Nano (article, 2024-05-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results. Bimetallic strip ; Nanoparticle ; Nanotechnology ; Materials science ; Catalysis ; Computer science ; Biosensor ; Workflow ; High-throughput screening ; Chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Karthik K. Srinivasan | Anjana Puliyanda | Devavrat Thosar | Abhijit Bhakte | Kuldeep Singh | Prince Addo | Rajagopalan Srinivasan | Vinay Prasad
Source: The Canadian Journal of Chemical Engineering (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning at various stages and scales of process systems engineering Abstract We review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains; furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human‐AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics‐informed regularization. We highlight the importance of studying human‐AI interactions, especially in the context of automation, and distinguish the features of human‐complements‐AI systems from those of AI‐complements‐human systems. Of particular importance in the AI‐complements‐human framework are model explanations, including rule‐based explanation, explanation‐by‐example, explanation‐by‐simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to ‘big data’, primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non‐traditional process data (images, audio, and text). Artificial intelligence ; Generative grammar ; Computer science ; Machine learning ; Process (computing) ; Relevance (law) ; Applications of artificial intelligence ; Human intelligence Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Promise O. Longe | Shadfar Davoodi | Mohammad Mehrad | David A. Wood
Source: Journal of Alloys and Compounds (article, 2024-11-30)
Field: Chemistry — Inorganic Chemistry

Robust machine-learning model for prediction of carbon dioxide adsorption on metal-organic frameworks Metal-organic framework ; Adsorption ; Carbon dioxide ; Materials science ; Metal ; Chemical engineering ; Computer science ; Metallurgy ; Chemistry ; Engineering ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Bo Xu | Zhanpeng Gong | Jingran Liu | Yunfei Hong | Yang Yang | Lou Li | Yilun Liu | Junkai Deng | Jefferson Zhe Liu
Source: Advanced Functional Materials (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Tunable Ferroelectric Topological Defects on 2D Topological Surfaces: Complex Strain Engineering Skyrmion‐Like Polar Structures in 2D Materials Abstract Polar topological structures in ferroelectric materials have attracted significant interest due to their fascinating physical properties and promising applications in high‐density, nonvolatile memories. Currently, most polar topological patterns are only observed in the bulky perovskite superlattices. In this work, a discovery of tunable ferroelectric polar topological structures is reported, designed, and achieved using topological strain engineering in two‐dimensional (2D) PbX (X = S, Se, and Te) materials via integrating first‐principles calculations, machine learning molecular dynamics simulations, and continuum modeling. First‐principles calculations discover the strain‐induced reversible ferroelectric phase transition with diverse polarization directions strongly correlated to the straining conditions. Taking advantage of the mechanical flexibility of 2D PbX, using molecular dynamics (MD) simulations, it is successfully demonstrated that the complex strain fields of 2D topological surfaces under mechanical indentation can generate unique skyrmion‐like polar topological vortex patterns. Further continuum simulations for experimentally accessible larger‐scale 2D topological surfaces uncover multiple skyrmion‐like structures (i.e., vortex, anti‐vortex, and flux‐closure) and transition between them by adopting/designing different types of mechanical loadings (such as out‐of‐plane indention and air blowing). Topological surfaces with various designable reversible polar topological structures can be tailored by complex straining flexible 2D materials, which provides excellent opportunities for next‐generation nanoelectronics and sensor devices. Materials science ; Skyrmion ; Ferroelectricity ; Polar ; Strain engineering ; Topological defect ; Topology (electrical circuits) ; Condensed matter physics ; Nanotechnology ; Optoelectronics ; Dielectric ; Physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Graphene research and applications
Authors: Juan Santiago Grassano | Ignacio Pickering | Adrián E. Roitberg | Mariano C. González Lebrero | Darío A. Estrı́n | Jonathan A. Semelak
Source: Journal of Chemical Information and Modeling (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction. Embedding ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Kaiqi Li | Bin Liu | Jian Zhou | Zhimei Sun
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations Using an efficient and accurate machine learning potential, large-scale crystallization dynamics of Sb–Te phase change materials are achieved. Crystallization ; Materials science ; Scale (ratio) ; Phase change ; Dynamics (music) ; Phase (matter) ; Chemical physics ; Molecular dynamics ; Nanotechnology ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Yuelian Qin | Xinyu Zhong | Caihong Liang | Zhenwu Liang | Yunyuan Nong | Lijun Deng | Yue Guo | Jinfeng Li | Meiling Zhang | Siqi Tang | Liuyan Wei | Yingguo Yang | Yonghong Liang | Jinxia Wu | Yeng Ming Lam | Zhiheng Su
Source: Biosensors and Bioelectronics (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and “segmentation-extraction-regression” deep learning assisted quantification of flavonoids Hesperidin ; Chemistry ; Substrate (aquarium) ; Flavonoid ; Partial least squares regression ; Computer science ; Machine learning ; Biochemistry ; Antioxidant Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Julia Razlivina | Andrei Dmitrenko | Vladimir V. Vinogradov
Source: The Journal of Physical Chemistry Letters (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform, DiZyme, featuring a state-of-the-art database of nanozymes containing 1210 experimental samples, catalytic activity prediction, and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving <i>R</i><sup>2</sup> = 0.75 for the Michaelis-Menten constant and <i>R</i><sup>2</sup> = 0.77 for the maximum velocity on unseen test data. We envision an accurate prediction of multiple catalytic activities (peroxidase, oxidase, and catalase) promoting novel applications for a wide range of surface-modified inorganic nanozymes. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide. Knowledge base ; Base (topology) ; Computer science ; Catalysis ; Artificial intelligence ; Chemistry ; Mathematics ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Machine Learning in Materials Science
Authors: Qi Wang | Chunyu Yang | Shulin Chen | Jinghua Li
Source: Angewandte Chemie International Edition (review, 2024-06-03)
Field: Materials Science — Polymers and Plastics

Miniaturized Electrochemical Sensing Platforms for Quantitative Monitoring of Glutamate Dynamics in the Central Nervous System Glutamate is one of the most important excitatory neurotransmitters within the mammalian central nervous system. The role of glutamate in regulating neural network signaling transmission through both synaptic and extra-synaptic paths highlights the importance of the real-time and continuous monitoring of its concentration and dynamics in living organisms. Progresses in multidisciplinary research have promoted the development of electrochemical glutamate sensors through the co-design of materials, interfaces, electronic devices, and integrated systems. This review summarizes recent works reporting various electrochemical sensor designs and their applicability as miniaturized neural probes to in vivo sensing within biological environments. We start with an overview of the role and physiological significance of glutamate, the metabolic routes, and its presence in various bodily fluids. Next, we discuss the design principles, commonly employed validation models/protocols, and successful demonstrations of multifunctional, compact, and bio-integrated devices in animal models. The final section provides an outlook on the development of the next generation glutamate sensors for neuroscience and neuroengineering, with the aim of offering practical guidance for future research. Glutamate receptor ; Neuroscience ; Computer science ; Neural engineering ; Neurotransmission ; Nanotechnology ; Biochemical engineering ; Biology ; Materials science ; Engineering ; Biochemistry Conducting polymers and applications ; Advanced Memory and Neural Computing ; Neuroscience and Neuropharmacology Research