jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Walter Malone | Johnathan von der Heyde | Abdelkader Kara
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accessing the usefulness of atomic adsorption configurations in predicting the adsorption properties of molecules with machine learning We present a systematic study into the effect of adding atomic adsorption configurations into the training and validation dataset for a neural network's predictions of the adsorption energies of small molecules on single metal and bimetallic, single crystal surfaces. Specifically, we examine the efficacy of models trained with and without H and X atomic adsorption configurations, where X is C, N, or O, to predict XH<sub><i>n</i></sub> adsorption energies. In addition, we compare our machine learning models to traditional simple scaling relationships. We find that models trained with the atomic adsorption configurations outperform models trained with only molecular adsorption configurations, with as much as a 0.37 eV decrease in the MAE. We find that models trained with the atomic adsorption configurations slightly outperform traditional scaling relationships. In general, these results suggest it may be possible to vastly reduce the number of adsorption configurations one needs for training and validation datasets by supplementing said data with the adsorption configurations of composite atoms or smaller molecular fragments. Adsorption ; Molecule ; Chemical physics ; Materials science ; Computational chemistry ; Chemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Hui Wang | Ji Fan | Tao Liu | Luyao Yan | Hongbin Zhang | Grace Li Zhang | Rolf Findeisen
Source: Measurement (article, 2024-11-16)
Field: Materials Science — Materials Chemistry

Deep learning-based image analysis with RTFormer network for measuring 2D crystal size distribution during cooling crystallization of β form L-glutamic acid Crystallization ; Image (mathematics) ; Crystal (programming language) ; Materials science ; Distribution (mathematics) ; Artificial intelligence ; Crystallography ; Computer science ; Mathematics ; Engineering ; Chemistry ; Chemical engineering ; Mathematical analysis Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Image Processing Techniques and Applications
Authors: Qianhui Zhao | Jianxun Wu | Tianhang Zhou | Suoqi Zhao | Quan Shi
Source: Petroleum Science (article, 2024-03-30)
Field: Chemistry — Analytical Chemistry

Impact of molecular composition on viscosity of heavy oil: Machine learning based on semi-quantitative analysis results from high-resolution mass spectrometry The primary impediment to the recovery of heavy oil lies in its high viscosity, which necessitates a deeper understanding of the molecular mechanisms governing its dynamic behavior for enhanced oil recovery. However, there remains a dearth of understanding regarding the complex molecular composition inherent to heavy oil. In this study, we employed high-resolution mass spectrometry in conjunction with various chemical derivatization and ionization methods to obtain semi-quantitative results of molecular group compositions of 35 heavy oils. The gradient boosting (GB) model has been further used to acquire the feature importance rank (FIR). A feature is an independently observable property of the observed object. Feature importance can measure the contribution of each input feature to the model prediction result, indicate the degree of correlation between the feature and the target, unveil which features are indicative of certain predictions (Zien et al., 2009). We have developed a framework for utilizing physical insights into the impact of molecular group compositions on viscosity. The results of machine learning (ML) conducted by GB show that the viscosity of heavy oils is primarily influenced by light components, specifically small molecular hydrocarbons with low condensation degrees, as well as petroleum acids composed of acidic oxygen groups and neutral nitrogen groups. Additionally, large molecular aromatic hydrocarbons and sulfoxides also play significant roles in determine the viscosity. Chemistry ; Viscosity ; Mass spectrometry ; Feature (linguistics) ; Resolution (logic) ; Biological system ; Analytical Chemistry (journal) ; Chromatography ; Artificial intelligence ; Computer science ; Thermodynamics ; Physics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Lung-Yi Chen | Yi‐Pei Li
Source: ChemRxiv (preprint, 2024-03-15)
Field: Materials Science — Materials Chemistry

AutoTemplate: Enhancing Chemical Reaction Datasets forMachine Learning Applications in Organic Chemistry This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a twostage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction verification, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. Scientific contribution: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database. Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: R. Radha
Source: Canadian Metallurgical Quarterly (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Predicting corrosion current density in magnesium alloy battery anodes: machine learning approach using rapid miner Battery (electricity) ; Anode ; Corrosion ; Magnesium ; Current (fluid) ; Alloy ; Magnesium alloy ; Materials science ; Metallurgy ; Computer science ; Engineering ; Chemistry ; Thermodynamics ; Physics ; Electrical engineering ; Electrode ; Physical chemistry Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Magnesium Alloys: Properties and Applications
Authors: Gerardo M. Casañola‐Martín | Jing Wang | Jian‐Ge Zhou | Bakhtiyor Rasulev | Jerzy Leszczynski
Source: Journal of Molecular Modeling (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling BODIPY ; Property (philosophy) ; Feature (linguistics) ; Density functional theory ; Quantitative structure–activity relationship ; Artificial intelligence ; Computer science ; Biological system ; Machine learning ; Chemistry ; Computational chemistry ; Fluorescence ; Physics ; Biology Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications
Authors: Hiroko Satoh | Vincenz-Maria Steiner | Jürg Hutter
Source: ChemRxiv (preprint, 2024-03-07)
Field: Chemistry — Physical and Theoretical Chemistry

“Quantum-Chemoinformatics” for Design and Discovery of New Molecules and Reactions We give an overview of the role of “quantum-chemoinformatics” in drug development. Quantum- chemoinformatics is a data-driven chemistry using descriptors on the basis of theoretical chemistry, especially quantum chemistry (QC) and ab initio molecular dynamics (MD) simulations. We focus especially on quantum-chemoinformatics for chemical reaction design and prediction, which is one of the important processes in basic research of drug development. We start with a brief historical overview and then introduces two projects of quantum-cheminformatics. The RMap project uses QC-based chemical reaction route networks for discovery and design of new molecules and reactions. The other project is related to environmental pollution by drug molecules, a property which should be taken into account in drug design and evaluation. The last section describes our recent attempt to accelerate QC-data acquisition by utilizing a limited amount of experimental data and machine learning (ML) technology. Cheminformatics ; Quantum chemical ; Drug discovery ; Computer science ; Data science ; Chemistry ; Combinatorial chemistry ; Molecule ; Computational chemistry ; Organic chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Hossein Shirani | Seyed Majid Hashemianzadeh
Source: ACS Medicinal Chemistry Letters (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation The ANI-1x neural network potential, trained on the density functional theory data set, as a quantum-level machine learning calculation has been investigated to forecast the potential energy surfaces of the Resveratrol (3,5,4'-trihydroxy-<i>trans</i>-stilbene) antiparkinsonian drug in a very short computing time. A comprehensive validation of the ANI-1x deep learning technique was provided on the Resveratrol molecule using density functional theory at the wB97X/6-31G(d) level of theory. The results showcased in this study will offer significant insights into pharmaceutical computational research, medicinal chemistry, drug discovery and design, thereby making a valuable contribution. Resveratrol ; Drug ; Computer science ; Quantum ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Pharmacology ; Medicine ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qian Li | Yuling Zhou | Wei Zhou | Hao Deng | Huaijuan Zang | Zhao Niu | Shu Zhan | Yongsheng Ren | Jiajia Xu | Wenhui Ma
Source: Molecular Physics (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Dynamic graph attention network-based crystal space-enriched representation for improving material property prediction The machine learning approach applied to materials discovery is a popular research direction. Knowledge of quantum chemistry explains that the structure of a material determines its properties. Graph neural networks (GNNs) provide a unique way of predicting the macroscopic properties of molecules and crystals rather than by solving the computationally expensive Schrödinger equation. Graph neural networks can abundantly transform the structural information of materials into corresponding features, and many models based on graph neural networks have been applied to predict material properties. We developed a new model (DYCGNN) containing a node update module for our designed edge-graph attention network composition. Through the application of the edge-gatv2 module, this module can effectively learn the complex relationship between nodes and neighbouring nodes in the crystal. Based on the calculated weight coefficients of each neighbouring node, the representation of the node is updated more effectively. In addition, we fuse the position information of the nodes into the node eigenvectors to complement the spatial information of the crystal and enrich the complete representation of the crystal. As we investigate the DYCGNN model, we find that our approach can outperform the predictions of previous models and provide insights into material crystallization. Computer science ; Graph ; Theoretical computer science ; Representation (politics) ; Node (physics) ; Eigenvalues and eigenvectors ; Artificial neural network ; Graph property ; Crystal (programming language) ; Topology (electrical circuits) ; Algorithm ; Artificial intelligence ; Line graph ; Mathematics ; Voltage graph ; Physics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Karthik Balasubramanian | Sukriti Manna | Subramanian KRS Sankaranarayanan
Source: Computational Materials Science (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Data driven insights into the characteristics of wide bandgap semiconductors in 2D materials Semiconductor ; Band gap ; Materials science ; Semiconductor materials ; Wide-bandgap semiconductor ; Engineering physics ; Optoelectronics ; Nanotechnology ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Abhishek Dhar | Anil Kumar Behera | Kaushik Gupta | Dipta Chatterjee | Nilavo Boral | Rohit L. Vekariya | Priyatosh Sarkar | Anik Sen
Source: Journal of Molecular Liquids (article, 2024-09-19)
Field: Chemistry — Analytical Chemistry

A study combining experimental, computationaland machine learning approaches on the removal of tartrazine dye using carbon nanotubes Tartrazine ; Carbon nanotube ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence ; Chemical engineering ; Chemistry ; Chromatography ; Engineering Dye analysis and toxicity ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Baochen Li | Yuru Liu | Haibin Sun | Rentao Zhang | Yongli Xie | Klement Foo | Frankie S. Mak | Ruimao Zhang | Tianshu Yu | Sen Lin | Peng Wang | Xiaoxue Wang
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Regio-MPNN: predicting regioselectivity for general metal-catalyzed cross-coupling reactions using a chemical knowledge informed message passing neural network As a fundamental problem in organic chemistry, regioselectivity is crucial in designing energy and cost-efficient reaction pathways for target compounds. Regioselectivity ; Catalysis ; Artificial neural network ; Coupling (piping) ; Coupling reaction ; Computer science ; Chemistry ; Combinatorial chemistry ; Artificial intelligence ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Qichao Liang | Qiang Ma | Hao Wu | Rongshun Lai | Yangyang Zhang | Ping Liu | Tao Qi
Source: Scientific Reports (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Performance prediction of sintered NdFeB magnet using multi-head attention regression models The preparation of sintered NdFeB magnets is complex, time-consuming, and costly. Data-driven machine learning methods can enhance the efficiency of material synthesis and performance optimization. Traditional machine learning models based on mathematical and statistical principles are effective for structured data and offer high interpretability. However, as the scale and dimensionality of the data increase, the computational complexity of models rises dramatically, making hyperparameter tuning more challenging. By contrast, neural network models possess strong nonlinear modeling capabilities for handling large-scale data, but their decision-making and inferential processes remain opaque. To enhance interpretability of neural network, we collected 1,200 high-quality experimental data points and developed a multi-head attention regression model by integrating an attention mechanism into the neural network. The model enables parallel data processing, accelerates both training and inference speed, and reduces reliance on feature engineering and hyperparameter tuning. The coefficients of determination for remanence and coercivity are 0.97 and 0.84, respectively. This study offers new insights into machine learning-based modeling of structure-property relationships in materials and has potential to advance the research of multimodal NdFeB magnet models. Interpretability ; Neodymium magnet ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Hyperparameter ; Curse of dimensionality ; Feature (linguistics) ; Inference ; Magnet ; Engineering Machine Learning in Materials Science ; Magnetic Properties and Applications ; Non-Destructive Testing Techniques
Authors: Qingfu Huang | Guixiang Li | Junjie Wang | Bo Niu | Fang Fang | Dongliang Quan | Donghui Long | Yayun Zhang
Source: The Journal of Physical Chemistry A (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Adaptive Force Field Parameter Optimization for Expanding Reaction Simulations within Wide-Ranged Temperature Large-scale and long-term simulation of chemical reactions are key research topics in computational chemistry. However, there are still difficulties in simulating high-temperature reactions, such as polymer thermal decomposition. Herein, we introduce an adaptive potential parameter optimization framework designed to automatically fine-tune parameters, and the application of it to optimize ReaxFF parameters enhances the accuracy of chemical reaction simulations conducted at experimental temperatures. To achieve this, we leverage the power of Random Forests and interpretable machine learning techniques that enable the identification and selection of parameters that exert a substantial influence on the target attribute. By training deep neural network (NN) models, we established optimized parameter associations with reference properties. We train deep neural network (NN) models to establish the relationship between the optimized parameters and reference properties. We employ a Genetic Algorithm (GA) to utilize the surrogate NN model and the quantum mechanical targets to speed up the search for the optimal parameters. Our simulation results of resin pyrolysis show that the adaptive optimized ReaxFF can predict the peak temperature more accurately and obtain reasonable product composition under conditions that more closely resemble experimental scenarios. This work facilitates advances in force field parameter optimization for more accurate and universal reaction simulations. ReaxFF ; Computer science ; Leverage (statistics) ; Artificial neural network ; Force field (fiction) ; Surrogate model ; Biological system ; Artificial intelligence ; Molecular dynamics ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Heat transfer and supercritical fluids
Authors: Yinjuan Chen | Xiaohui Zhu | Qinglong Zhou | Chen Zhu | Hui Xing | Hai Xiao | Hongying Zhuo | Yafei Li
Source: Nano Research (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

XAFS-based identification of the microscopic chemical environment in single-atom catalysts: Progress, challenges and prospects Single-atom catalysts (SACs) have attracted widespread attention in the field of catalysis due to their unique atomic dispersion characteristics, extremely high atomic utilization efficiency, and excellent catalytic performance. Accurately identifying their microscopic chemical environment is of crucial importance for revealing the catalytic mechanism and promoting the innovative design of high-performance catalysts, especially the developing of machine learning (ML) driven precision chemistry. X-ray absorption fine structure (XAFS) technology has become the core means in SACs research because of its high sensitivity to specific elements, the ability to simultaneously detect electronic and geometric structures, and good sample compatibility. This review begins with the fundamental principles of XAFS technology, systematically elucidating its key applications and significant advantages in the characterization of metal sites in SACs and <em>in situ</em> detecting of catalytic reactions. Furthermore, from the perspective of integrating theoretical calculations with experimental studies, it delves into the importance of using XAFS in conjunction with first-principles calculations and other methods to accurately determine the fine structure of active sites. Finally, this review delivers an in-depth discussion of the key challenges inherent in XAFS characterization and presents a forward-looking perspective on its future development. This work aims to furnish a robust theoretical framework and practical guidelines to foster a profound understanding of the intrinsic relationship between the structural characteristics and performance of SACs, thereby facilitating the targeted design of SACs. X-ray absorption fine structure ; Catalysis ; Atom (system on chip) ; Identification (biology) ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Computer science ; Spectroscopy ; Organic chemistry ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Dongdong Li | Haibin Liao | Qianqian Zhang | Qi Fan | Jiang-Bo She
Source: Microchemical Journal (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Enhanced upconversion photoluminescence of LiYF4:Yb3+/Tb3+ crystals by introducing Mn2+ ions for efficient anti-counterfeiting recognition based on convolution neural network Photoluminescence ; Photon upconversion ; Doping ; Materials science ; Convolutional neural network ; Convolution (computer science) ; Ion ; Characterization (materials science) ; Excitation ; Luminescence ; Artificial neural network ; Computer science ; Optoelectronics ; Nanotechnology ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry Luminescence Properties of Advanced Materials ; Forensic Fingerprint Detection Methods ; Advanced Semiconductor Detectors and Materials