jablonkagroup/rise_ai_scientists huggingface dataset:
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: ChemRxiv (preprint, 2024-03-26)
Field: Materials Science — Materials Chemistry

Regio-MPNN: Predicting Regioselectivity for General Metal-Catalyzed Cross-Coupling Reactions using Chemical Knowledge Informed Message Passing Neural Network As a fundamental problem in organic chemistry, synthesis planning aims at designing energy and cost-efficient reaction pathways for target compounds. In synthesis planning, it is crucial to understand regioselectivity, or the preference of a reaction over competing reaction sites. Precisely predicting regioselectivity enables early exclusion of unproductive reactions and paves the way to designing high-yielding synthetic routes with minimal separation and material costs. However, it is still at emerging state to combine chemical knowledge and data-driven methods to make practical predictions for regioselectivity. At the same time, metal-catalyzed cross-coupling reactions have profoundly transformed medicinal chemistry, and thus become one of the most frequently encountered types of reactions in synthesis planning. In this work, we for the first time introduce a chemical knowledge informed message passing neural network(MPNN) framework that directly identifies the intrinsic major products for metal-catalyzed cross-coupling reactions with regioselective ambiguity. Integrating both first principle methods and data-driven methods, our model achieves an overall accuracy of 95.24\% on the test set of eight typical metal-catalyzed cross-coupling reaction types, including Suzuki-Miyaura, Stille, Sonogashira, Buchwald-Hartwig, Hiyama, Kumada, Negishi, and Heck reactions, outperforming other commonly used model types. To integrate electronic effects with steric effects in regioselectivity prediction, we propose a quantitative method to measure the steric hindrance effect. Our steric hindrance checker can successfully identify regioselectivity induced solely by steric hindrance. Notably under practical scenarios, our model outperforms 6 experimental organic chemists with an average working experience of over 10 years in the organic synthesis industry in terms of predicting major products in regioselective cases. We have also exemplified the practical usage of our model by fixing routes designed by open-access synthesis planning software and improving reactions by identifying low-cost starting materials. To assist general chemists in making prompt decisions about regioselectivity, we have developed a free web-based AI-empowered tool. Our code and web tool have been made available at https://github.com/Chemlex-AI/regioselectivity and https://ai.tools.chemlex.com/region-choose, respectively. Regioselectivity ; Catalysis ; Artificial neural network ; Computer science ; Coupling (piping) ; Coupling reaction ; Combinatorial chemistry ; Chemistry ; Metal ; Artificial intelligence ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Shyue Ping Ong | A. P. Mishra | Ji Qi | Zihan Yu
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

(Invited) Design Insights into Solid Electrolytes with Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLPs) have had a transformative impact in the computational modeling of materials by enabling accurate simulations at length and time scales beyond that accessible by ab initio methods. In this talk, I will share new insights into the design of solid electrolytes (SEs) for all-solid-state rechargeable alkali-ion batteries from MLP-based MD simulations. I will demonstrate how the cation chemistry can influence grain boundary lithium concentration in perovskite SEs, providing a means to enhance overall conductivity. I will also discuss the effect of anion chemistry on alkali conductivity in crystalline and amorphous SEs. These new insights can guide the rational design of high conductivity SEs through compositional tuning as well as processing conditions. Interatomic potential ; Electrolyte ; Materials science ; Computer science ; Chemistry ; Physical chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science
Authors: Betim Bahtiri | Behrouz Arash | S. Scheffler | Maximilian Jux | Raimund Rolfes
Source: arXiv (Cornell University) (preprint, 2024-03-27)
Field: Materials Science — Materials Chemistry

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables required for characterizing the internal dissipation of the nanocomposite materials. In addition, another feed-forward neural network is used to indicate the free-energy function, which enables defining the thermodynamic state of the entire system. The PIDL model is initially developed for the three-dimensional case by generating synthetic data from a classical constitutive model. The model is then trained by extracting the data directly from cyclic loading-unloading experimental tests. Numerical examples show that the PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions. Nanocomposite ; Polymer ; Materials science ; Fiber ; Polymer nanocomposite ; Polymer physics ; Polymer science ; Composite material ; Nanotechnology Machine Learning in Materials Science
Authors: Hiroto Yokoyama | Hajime Shimakawa | Akiko Kumada | Masahiro Sato
Source:  (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

A Study Towards Machine Learning Prediction of Thermal Conductivity of Polymers Based on Molecular Dynamics In recent years, there has been a growing demand for further enhancement of thermal conductivity in dielectric polymers used in electrical equipment and electronic devices. While machine learning-based property predictions have been explored for polymer molecular design, existing models often suffer from low interpretability and require extensive computations or experimental efforts. In this study, we predicted the thermal conductivity of both crystalline and amorphous polymers by incorporating information from the unit cell structure of crystals and molecular dynamics force field parameters as descriptors. We achieved improved prediction accuracy for thermal conductivity of amorphous polymers. Thermal conductivity ; Polymer ; Molecular dynamics ; Computer science ; Dynamics (music) ; Artificial intelligence ; Thermal ; Machine learning ; Materials science ; Biological system ; Composite material ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Thermal properties of materials
Authors: Hao Ting-yue | Tianle Yue | Ying Li
Source: ChemRxiv (preprint, 2024-07-12)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Machine Learning for Glass Transition Temperature Prediction of Polymers Machine learning (ML) has become an important technique in materials science, markedly accelerating the discovery and design of novel materials, and concurrently lowering the burden of experimental costs. Uncertainty quantification (UQ) plays a pivotal role in the accurate prediction and innovative design of novel materials through ML techniques. In this study, we perform a comprehensive evaluation of six UQ methods in ML, including ensemble, Gaussian process regression (GPR), Monte Carlo dropout (MCD), Mean-variance estimation (MVE), Bayesian neural network (BNN) and Evidential deep learning (EDL), for predictions on the glass transition temperature (T_g) of polymers. We assess the accuracy and performance of these UQ methods using three metrics, including Spearman’s rank correlation coefficient, calibration and sparsification, offering a substantial reference for data-driven polymer design. Our analysis encompasses test data, out-of-distribution data from experiments and molecular dynamics simulations, and high-T_g polymer data for UQ analysis of ML predictions. The results indicate that ML models are robust and effective in predicting polymer’s T_g values for testing and experimental data. However, correlating actual errors with uncertainties (standard deviations) poses a significant challenge, with ML models frequently exhibiting overconfidence with low uncertainties. Moreover, the accuracy of ML predictions improves when the data with large uncertainties are excluded, suggesting a potential strategy for refining ML model’s performance. Uncertainty quantification ; Monte Carlo method ; Computer science ; Calibration ; Kriging ; Machine learning ; Gaussian process ; Artificial intelligence ; Bayesian probability ; Weighting ; Glass transition ; Polymer ; Gaussian ; Materials science ; Mathematics ; Chemistry ; Statistics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Chaudhary, Sunny | Vryonis, Orestis | Lewin, Paul
Source: ePrints Soton (University of Southampton) (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced metadata analysis for identifying polymer compositions In materials science, accurately predicting and identifying polymer compositions from limited experimental data could potentially help in advancing material design, sustainability, and faster development. Traditional methods for analysing aged or complex polymers are often time-consuming and costly, requiring extensive measurements. Machine learning along with meta-data analysis and curation of an un-biased dataset offers a promising alternative, enabling quicker and notably accurate identification of materials or material properties.<br/><br/>The aim is to integrate machine learning with metadata analysis to predict polymer-filler combinations from independently measured property values. As an introductory study, a machine learning pipeline using ensemble methods, including KNN, Random Forest and Neural Networks (MLP) with XGBoost as the final estimator combined via stacking, was developed. The model was fine-tuned using Bayesian optimization for efficient hyper-parameter tuning. All individual and combinations of models were evaluated for accuracy and computational efficiency.<br/><br/>Evaluation was done on a dataset of various polymer-filler combinations obtained from NanoMine, the performance was assessed using top-k accuracy metrics and cross-validation score, based on the ability to correctly identify likely compositions based on user input. The model predicts the top three polymer-filler combinations with associated confidence levels.<br/> Pipeline (software) ; Metadata ; Random forest ; Identification (biology) ; Artificial neural network ; Bayesian optimization ; Ensemble learning ; Bayesian probability Machine Learning in Materials Science ; Polymer crystallization and properties ; Computational Drug Discovery Methods
Authors: Daniel Julian | Rian Koots | Jesús Pérez‐Ríos
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — Biomaterials

Machine learning models for atom-diatom reactions across isotopologues This work shows that feed-forward neural networks can predict the final ro-vibrational state distributions of inelastic and reactive processes of the reaction of Ca $+$ H2 $\rightarrow$ CaH $+$ H in the hyperthermal regime, relevant for buffer gas chemistry. Furthermore, these models can be extended to the isotopologues of the reaction involving deuterium and tritium. In addition, we develop a neural network model that can learn across the chemical space based on the isotopologues of hydrogen. The model can predict the outcome of a reaction whose reactants have never been seen. This is done by training on the Ca $+$ H2 and Ca $+$ T2 reactions and subsequently predicting the Ca $+$ D2 reaction. Isotopologue ; Diatom ; Atom (system on chip) ; Computer science ; Chemistry ; Geology ; Oceanography ; Parallel computing ; Molecule Diatoms and Algae Research ; Hydrocarbon exploration and reservoir analysis ; Genomics and Phylogenetic Studies
Authors: Viswanadhapalli Balaji | E. Reddy | Makam Adithya | Mallu Venkata Ganga Prasanna Kumar Reddy | Nagaraju Krishna Chythanya
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Biomaterials

Experimental Study and Neural Network Model to Predict Formability of Magnesium Alloy AZ31B Magnesium alloy is an emerging smart metal used in various industries like automotive and aerospace industry, due to their lightweight and excellent strength-to-weight ratio. Formability, a critical factor in manufacturing processes, determines the alloy’s ability to undergo deformation without fracture or defects. Fuel economy and environmental conservatives are the key desirable factors in selection of magnesium alloy sheets. Magnesium alloy sheets have low formability at room temperature due to their hexagonal closed-packed microstructures. As the magnesium’s formability at room temperature is considerably low, stretch forming tests are conducted at moderate temperatures. For this purpose, commercially available AZ31B magnesium alloy sheet of 1.1mm thickness has been used and tested at room temperature, 25 degree to within medium temperatures range and at a higher strain rate of 0.01/s. The main objective of an experimental study to predict the formability of magnesium alloy sheets is to gather data through controlled tests and measurements. This data and Forming Limit Diagram (FLD) can be used to analyse the formability of material, it defines failure criteria. On the other hand, using a neural network to predict formability involves training the network on the collected experimental data. Once trained, the neural network can predict the formability of new magnesium alloy sheets based on their characteristics, offering a faster and potentially more accurate prediction method compared to traditional models. This work explores into the realm of regression modelling utilizing neural networks, a powerful subset of machine learning techniques. It begins with a discussion on the setup of machine learning models, emphasizing the crucial steps involved in data preprocessing, model selection, and evaluation. Formability ; Materials science ; Magnesium alloy ; Metallurgy ; Alloy ; Deep drawing ; Magnesium ; Microstructure ; Deformation (meteorology) ; Artificial neural network ; Composite material ; Computer science ; Machine learning Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Xinghai Li | Zhisen Wu | Lijing Zhang | Shengyang Tao
Source: Acta Physico-Chimica Sinica (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Machine learning enables the prediction of amide bond synthesis based on small datasets Machine learning (ML) is progressively revealing notable advantages in chemical synthesis. However, the limited output of experimental data from traditional methods poses a bottleneck, impeding the widespread adoption of machine learning. Data from literature often leads to overly optimistic predictions, and obtaining thousands of experimental data points through experiments remains a substantial challenge. Using a small dataset of experimental data, we illustrated that machine learning algorithms can reliably predict the conversion rate of amide bond synthesis. We gathered hundreds of experimental data points for 9 aromatic amines and 12 organic acids using various coupling reagents and solvents in a 96-well plate high-throughput experimental setup. Subsequently, we derived 76 feature molecular descriptors from quantum chemical calculations and utilized them as inputs for training the machine learning model. Despite the inherent limitation of low data volume, the random forest algorithm demonstrated outstanding predictive performance ( R 2 ​> ​0.95). Through comprehensive analysis of the reaction process employing importance analysis, shapley additive explanations (SHAP), and accumulated local effects (ALE) methods, we delved into the important factors influencing the reaction conversion rate. In predicting the conversion rate of unknown aromatic amine molecules, we discovered that incorporating a small amount of unknown molecule-related reaction data into the training set effectively enhances the model's predictive performance, even with a small dataset. By comparing models trained on different molecular descriptors such as density functional theory (DFT) and one-hot encoding, we validated the efficacy of adjusting the training set to improve prediction results. This study utilized a multitude of chemically meaningful feature descriptors and achieved more effective prediction results through multidimensional data analysis, offering valuable insights for machine learning-assisted chemical synthesis research in small datasets. In the near future, machine learning is poised to drive the intelligent development of organic chemistry . Computer science ; Machine learning ; Artificial intelligence ; Bond ; Business ; Finance Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Francesco Ferri | Marco Cannariato | Lorenzo Pallante | Eric A. Zizzi | Marco A. Deriu
Source: arXiv (Cornell University) (preprint, 2024-06-21)
Field: Materials Science — Materials Chemistry

Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic taste modalities that can be perceived by humans and other mammals. It is mediated by a family of G protein-coupled receptors (GPCRs), namely taste receptor type 2 (TAS2R) or bitter taste receptors. Furthermore, TAS2Rs participate in numerous functions beyond the gustatory system and have implications for various diseases due to their expression in various extra-oral tissues. For this reason, predicting the specific ligand-TAS2Rs interactions can be useful not only in the field of taste perception but also in the broader context of drug design. Considering that in-vitro screening of potential TAS2R ligands is expensive and time-consuming, machine learning (ML) and deep learning (DL) emerged as powerful tools to assist in the selection of ligands and targets for experimental studies and enhance our understanding of bitter receptor roles. In this context, ML and DL models developed in this work are both characterized by high performance and easy applicability. Furthermore, they can be synergistically integrated to enhance model explainability and facilitate the interpretation of results. Hence, the presented models promote a comprehensive understanding of the molecular characteristics of bitter compounds and the design of novel bitterants tailored to target specific TAS2Rs of interest. Deep learning ; Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: A. Gnanapraksam | M. Thirumarimurugan | D. Gomathi
Source: SHILAP Revista de lepidopterología (article, 2024-04-01)
Field: Chemistry — Inorganic Chemistry

Data-Driven Prediction of Transesterification Reactions: Analyzing Zirconium-Based Metal-Organic Framework Catalysts with Machine Learning Models This research study focuses on the utilization of machine learning models to predict transesterification reactions using zirconium-based Metal-Organic Framework (MOF) as a catalyst. Various machine learning algorithms, including Multiple Linear Regression (MLR), Polynomial regression (PLR), Decision Tree (DT), Random Forest (RF), Adaboost (AB), XGBoost (XGB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), were employed to analyze the collected data. The performance of each model was evaluated using mean accuracy and median accuracy metrics. Among the tested models, XGBoost exhibited the highest predictive accuracy, with a mean accuracy of 91.07% and a median accuracy of 96.72%. These results demonstrate that XGBoost effectively captures the intricate relationships between the input features and the outcomes of transesterification reactions. Furthermore, a feature importance analysis conducted using XGBoost revealed the relative significance of various factors in the reaction process. The analysis revealed that the preparation method held the highest importance. The factors ranked by importance were: preparation method, catalyst loading, alcohol type, reaction temperature, MOF, reaction time, acid functionalized, base functionalized, metal precursor, and linker. These findings enhance our understanding of transesterification reactions catalyzed by zirconium-based MOFs and highlight the effectiveness of machine learning models in predicting their outcomes. Transesterification ; Decision tree ; Support vector machine ; Random forest ; Artificial neural network ; AdaBoost ; Feature (linguistics) ; Linear regression Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Catalysis and Hydrodesulfurization Studies