jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Quan Zou | Akiyoshi Kuzume | Masataka Yoshida | Takane Imaoka | Kimihisa Yamamoto
Source: Chemistry Letters (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution Abstract Metal and alloy subnanoparticles (SNPs) have been anticipated to be a class of promising catalysts because of their fundamental difference from nanoparticles (NPs). In general, the interaction among the surface and bulk atoms of SNPs is significant due to the higher degree of alloying in SNPs than that in NPs counterparts. This study compared the SNPs and NPs concerning their electrocatalytic activities of hydrogen evolution reaction (HER) to understand the essential difference between alloy SNPs and NPs by using machine learning. Chemistry ; Hydrogen ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Lanfeng Wang | Hengzhe Wang | Shuoshi Liu | Zixin Li | Yaping Yu | Yun Chai | Xiaohui Yang
Source: match Communications in Mathematical and in Computer Chemistry (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

ChemCNet: An Explainable Integrated Model for Intelligent Analyzing Chemistry Synthesis Reactions Palladium (Pd)-catalyzed cross coupling reactions are of great significance in organic synthesis. However, the reaction route is more complex, time-consuming and costly. For addressing the above problems, a model-related feature selection strategy is introduced, focusing on iterative optimization of feature description and prediction to guide and strengthen each other. Then, we combine the lightweight convolution neural network (CNN) driven by attention mechanism with CatBoost to build an intelligent chemical synthesis reaction analysis model-ChemCNet. Moreover, we conduct the interpretability analysis based on ChemCNet model. The results show that ChemCNet model has achieved relatively high prediction accuracy and generalization, and it is helpful to provide reliable decision-making information for the experimenter or institution. Interpretability ; Generalization ; Computer science ; Feature (linguistics) ; Convolution (computer science) ; Artificial intelligence ; Feature selection ; Artificial neural network ; Convolutional neural network ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Radiomics and Machine Learning in Medical Imaging
Authors: Vaidehi Menon | Sambit Das | Vikram Gavini | Liang Qi
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Atomistic Simulations and Machine Learning of Solute Grain Boundary Segregation in Mg Alloys at Finite Temperatures Grain boundary ; Materials science ; Boundary (topology) ; Metallurgy ; Microstructure ; Mathematics ; Mathematical analysis Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Magnesium Alloys: Properties and Applications
Authors: Sean Nations | Tarak Nandi | Ali Ramazani | Shengnian Wang | Yuhua Duan
Source:  (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Metal Hydride Composition-Derived Parameters as Machine Learning Features for Material Design and H2 Storage Poster for Techconnect Conference Hydride ; Composition (language) ; Hydrogen storage ; Metal ; Computer science ; Process engineering ; Materials science ; Metallurgy ; Engineering ; Alloy Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yusei Hisata | Takashi Washio | Shinobu Takizawa | Sensuke Ogoshi | Yoichi Hoshimoto
Source: ChemRxiv (preprint, 2023-11-03)
Field: Chemistry — Organic Chemistry

In-Silico-Assisted Derivatization of Triarylboranes for the Catalytic Reductive Functionalization of Amino Acids with H2 Cheminformatics-based machine learning (ML) has assisted in determining optimal reaction conditions, including catalyst struc-tures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of amino acids with aldehydes and H2 is reported. The obtained results suggest that the deformation energy serves as a useful parameter for Gauss-ian process regression to construct adequate models for predicting the turnover frequencies of triarylboranes under the applied model reaction conditions, while Gaussian progress regression based on the energy levels of the lowest unoccupied molecular orbitals (LUMOs) including the empty p-orbitals of boron tend to underestimate the turnover frequency. The optimal borane, i.e., B(2,3,5,6-Cl4-C6H)(2,6-F2-3,5-(CF3)2-C6H)2, effectively catalyzes the reductive functionalization of aniline-derived amino acids and C-terminal-protected peptides in the presence of 4-methyltetrahydropyrane and H2, generating H2O as the sole by-product. Chemistry ; Catalysis ; Context (archaeology) ; Amino acid ; Combinatorial chemistry ; Derivatization ; Computational chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Yaorui Shi | An Zhang | Enzhi Zhang | Zhiyuan Liu | Xiang Wang
Source: arXiv (Cornell University) (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably those employing Graph Neural Networks (GNNs), are often limited by insufficient training data and their inability to utilize textual information, undermining their applicability in real-world applications. In this work, we propose ReLM, a novel framework that leverages the chemical knowledge encoded in language models (LMs) to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. To further enhance the model's robustness and interpretability, we incorporate the confidence score strategy, enabling the LMs to self-assess the reliability of their predictions. Our experimental results demonstrate that ReLM improves the performance of state-of-the-art GNN-based methods across various chemical reaction datasets, especially in out-of-distribution settings. Codes are available at https://github.com/syr-cn/ReLM. Interpretability ; Computer science ; Robustness (evolution) ; Machine learning ; Artificial intelligence ; Reliability (semiconductor) ; Process (computing) ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Neel Redkar
Source: arXiv (Cornell University) (preprint, 2023-11-12)
Field: Materials Science — Materials Chemistry

CLAMP: A Contrastive Language And Molecule Pre-training Network This paper highlights a shift in how to approach material generation. Instead of material-to-material, we propose a language-to-material generation architecture that utilizes millions of untapped data points. Using a web scraper to collect crystal text pairs from open-source research papers, a contrastive model can be trained using a convolutional graph neural network encoder and a language encoder. This would allow unsupervised zero-shot classification which can be trained by taking advantage of linguistic structure. Without any specific training data, an ~82\% accuracy was achieved and ~75\% accuracy for photocatalyst prediction with an extremely small dataset. This novel network could ideally be cross-applied to any reaction that can be described via text, opening completely new methods to think about 3D chemical framework generation. In the full experiment diffusion models would likely be incorporated to fully exploit the latent space. Computer science ; Exploit ; Encoder ; Artificial intelligence ; Natural language processing ; Convolutional neural network ; Artificial neural network ; Training set Machine Learning in Materials Science
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2023-09-05)
Field: Materials Science — Materials Chemistry

Virtual Variables-Enabled Generation of Datasets for Prediction in Organic Synthesis: Digitalization of Small Molecules and Its Application to Functional Molecule Syntheses In machine learning, large datasets are typically required to improve estimation accuracy. However, the generation of such datasets in traditional organic synthesis experiments is extremely challenging. Hence, there is a strong demand for methods that enable the accurate prediction of outcomes using limited amounts of data. In this study, we report a molecular technology based on generative artificial intelligence that generates data from unexplored conditions and establishes the most suitable relationships between different small molecules using virtual variables. Specifically, our approach reveals relationship of structurally different three small molecules and represents them as virtual variables, which are then utilized to propose the reaction conditions for synthesizing target molecules in high yields. We demonstrated the utility of this approach in small molecule syntheses, using the iodination reaction of polyfluoronaphthalenes serving as a case study. The obtained iodinated perfluoronapthalenes have recently garnered significant attention as promising candidates for functional molecules. By computationally generating data that would be inaccessible through realistic reaction experiments, we successfully optimized reaction conditions in an additional 8 experiments. Moreover, we succeeded to depict virtual variables in electrostatic potentials using density functional theory calculations and represented them as physicochemical indices. This study introduces a novel application of machine learning as molecular technology and contributes small molecule syntheses in molecular science, utilizing fewer than 100 data points in the realm of organic synthesis experiments. Computer science ; Small molecule ; Molecule ; Virtual screening ; Molecular descriptor ; Organic molecules ; Artificial intelligence ; Machine learning ; Biological system ; Biochemical engineering ; Computational chemistry ; Chemistry ; Quantitative structure–activity relationship ; Organic chemistry ; Molecular dynamics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Elise Lunde Gjelsvik | Kristin Tøndel
Source: PLoS ONE (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Increased interpretation of deep learning models using hierarchical cluster-based modelling Linear prediction models based on data with large inhomogeneity or abrupt non-linearities often perform poorly because relationships between groups in the data dominate the model. Given that the data is locally linear, this can be overcome by splitting the data into smaller clusters and creating a local model within each cluster. In this study, the previously published Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) procedure was extended to deep learning, in order to increase the interpretability of the deep learning models through local modelling. Hierarchical Cluster-based Convolutional Neural Networks (HC-CNNs), Hierarchical Cluster-based Recurrent Neural Networks (HC-RNNs) and Hierarchical Cluster-based Support Vector Regression models (HC-SVRs) were implemented and tested on spectroscopic data consisting of Fourier Transform Infrared (FT-IR) measurements of raw material dry films, for prediction of average molecular weight during hydrolysis and a simulated data set constructed to contain three clusters of observations with different non-linear relationships between the independent variables and the response. HC-CNN, HC-RNN and HC-SVR outperformed HC-PLSR for the simulated data set, showing the disadvantage of PLSR for highly non-linear data, but for the FT-IR data set there was little to gain in prediction ability from using more complex models than HC-PLSR. Local modelling can ease the interpretation of deep learning models through highlighting differences in feature importance between different regions of the input or output space. Our results showed clear differences between the feature importance for the various local models, which demonstrate the advantages of a local modelling approach with regards to interpretation of deep learning models. Interpretability ; Partial least squares regression ; Artificial intelligence ; Data set ; Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Feature (linguistics) ; Linear model ; Support vector machine ; Set (abstract data type) ; Artificial neural network ; Linear regression ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Michael J. Statt | Brian A. Rohr | Kris S. Brown | Dan Guevarra | Jens S. Hummelshøj | Linda Hung | abraham anapolsky | John M. Gregoire | Santosh K. Suram
Source: ChemRxiv (preprint, 2023-05-09)
Field: Materials Science — Materials Chemistry

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment. Workflow ; Computer science ; Metadata ; Event (particle physics) ; Architecture ; Data science ; Synthetic data ; Distributed computing ; Data mining ; Database ; Artificial intelligence ; World Wide Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Alejandro Santana‐Bonilla | Raquel López-Ríos de Castro | Peike Sun | Robert M. Ziolek | Christian D. Lorenz
Source: ChemRxiv (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Modular Software for Generating and Modelling Diverse Polymer Databases Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale however building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modelling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a workflow by which to create and curate molecular databases without requiring significant levels of user input. This ensures well-founded concerns regarding data provenance, reproducibility and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London), that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with a minimal user intervention. PySoftK is available as an efficient, fully-tested, and easily installed Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modelling and curation of large polymer libraries to support functional materials discovery in nano- and bio-technology. Workflow ; Python (programming language) ; Modular design ; Computer science ; Software ; Software engineering ; Extensibility ; Data science ; World Wide Web ; Database ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Phase Equilibria and Thermodynamics
Authors: Jingbai Li | Steven A. Lopez
Source: ChemRxiv (preprint, 2023-05-23)
Field: Materials Science — Materials Chemistry

Machine learning accelerated photodynamics simulations Machine learning (ML) continues to revolutionize computational chemistry by accelerating predictions and simulations by training on experimental or accurate but expensive quantum chemical (QC) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QC calculations of energies, forces, and non-adiabatic couplings that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QC calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QC calculations and machine learning techniques. We then discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis-trans isomerization, [2+2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism. Isomerization ; Solvation ; Adiabatic process ; Chemistry ; Cycloaddition ; Molecular dynamics ; Computational chemistry ; Quantum ; Molecule ; Computer science ; Chemical physics ; Statistical physics ; Physics ; Quantum mechanics ; Catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sanskriti Tripathy | Usha Manasi Mohapatra
Source:  (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

A Study on Machine Learning Based Classification of Crystal Structures in AB<sub>O3</sub> Perovskites This study focuses on the classification of ABO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> materials using the XGBoost model. Out of 5329 perovskite compounds, 547 compounds are selected after the preprocessing phase of data mining. Some features are dropped due to a lack of relevance to the classification task. The correlated features are identified and redundant features are removed from the dataset. The proposed model successfully classified the structures into their appropriate classes with 90% accuracy. The augmentation technique is applied to deal with the imbalance in the dataset. The model is inexpensive and fast. The crystal structure classification finds application in material engineering. Computer science ; Materials science ; Artificial intelligence ; Crystal (programming language) ; Crystal structure ; Machine learning ; Crystallography ; Chemistry Machine Learning in Materials Science ; Machine Learning and ELM ; Advancements in Solid Oxide Fuel Cells
Authors: Eugène Sanscartier | Félix Saint-Denis | Karl-Étienne Bolduc | Normand Mousseau
Source: arXiv (Cornell University) (preprint, 2023-01-20)
Field: Materials Science — Materials Chemistry

Evaluating approaches for on-the-fly machine learning interatomic potential for activated mechanisms sampling with the activation-relaxation technique nouveau In the last few years, much efforts have gone into developing universal machine-learning potentials able to describe interactions for a wide range of structures and phases. Yet, as attention turns to more complex materials including alloys, disordered and heterogeneous systems, the challenge of providing reliable description for all possible environment become ever more costly. In this work, we evaluate the benefits of using specific versus general potentials for the study of activated mechanisms in solid-state materials. More specifically, we tests three machine-learning fitting approaches using the moment-tensor potential to reproduce a reference potential when exploring the energy landscape around a vacancy in Stillinger-Weber silicon crystal and silicon-germanium zincblende structure using the activation-relaxation technique nouveau (ARTn). We find that a a targeted on-the-fly approach specific and integrated to ARTn generates the highest precision on the energetic and geometry of activated barriers, while remaining cost-effective. This approach expands the type of problems that can be addressed with high-accuracy ML potentials. Relaxation (psychology) ; Computer science ; On the fly ; Range (aeronautics) ; Tensor (intrinsic definition) ; Silicon ; Statistical physics ; Artificial intelligence ; Physics ; Materials science ; Machine learning ; Nanotechnology ; Mathematics ; Neuroscience ; Psychology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Electron and X-Ray Spectroscopy Techniques
Authors: Hengzhe Wang | Lichao Peng | Li Chang | Zixin Li | Yanhui Guo | Qian Li | Xiaohui Yang
Source: match Communications in Mathematical and in Computer Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

CatBoost-Based Framework for Intelligent Prediction and Reaction Condition Analysis of Coupling Reaction Machine learning is increasingly popular in predicting chemical reaction performance. This study aims to apply the CatBoost algorithm to build an intelligent prediction system for organic chemical reaction yields. The parameter analysis, convergence analysis, prediction accuracy analysis and generalization analysis are carried out. Then, the internal relationship between reaction conditions and yield is excavated through feature importance and SHAP. The results show that the proposed method has the potential as a high-precision tool to assist the optimization of chemical reaction system. Convergence (economics) ; Generalization ; Feature (linguistics) ; Computer science ; Yield (engineering) ; Artificial intelligence ; Coupling (piping) ; Machine learning ; Mathematics ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification