jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruijiao Tian | Ping Jiang | Jing Xu
Source:  (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Machine Learning Algorithms is used for the Design and Improvement of Low-Temperature SCR Catalysts In order to solve the challenges of designing and improving low-temperature SCR catalysts for chemicals, in view of the shortcomings of the Learning sting AHP algorithms, this study proposes an innovative design and improvement method of low-temperature SCR catalysts based on machine Learning algorithms. This new scheme uses the principles of statistical information theory to accurately identify and locate key influencing factors, and accordingly performs a sensible classification of indicators to reduce possible interference. At the same time, using the unique mechanism of machine Learning algorithm, the design strategy of low-temperature SCR catalyst is cleverly constructed. The empirical results show that this scheme shows significant improvement compared with the traditional AHP algorithm in terms of key performance indicators such as the accuracy of the design and improvement of the low-temperature SCR catalyst, and the processing efficiency of key factors, showing its obvious advantages. In chemical products, the design and improvement of low-temperature SCR catalysts play a crucial role, which can accurately predict and optimize the growth trend and output results of the design and improvement of low-temperature SCR catalysts for chemical products. However, when faced with complex simulation tasks, traditional AHP algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of designing and improving the low-temperature SCR catalyst optimized by machine science Learning algorithm, and accurately controls the influencing parameters through statistical information theory, and uses this as a road map for index allocation, and then uses machine Learning algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the design and improvement of low-temperature SCR catalysts for chemicals, the simulation scheme based on machine Learning algorithm successfully overcomes the shortcomings of the traditional AHP algorithm and significantly improves the accuracy and operation efficiency of the simulation. Computer science ; Catalysis ; Algorithm ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Jie Qian | Junfan Xia | Bin Jiang
Source: JUSTC (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics simulations of liquid methanol As the simplest hydrogen-bonded alcohol, liquid methanol has attracted intensive experimental and theoretical interest. However, theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals. Inspired by recent studies on bulk water using increasingly accurate machine learning force fields, we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction. Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations, yielding the radial distribution functions, self-diffusion coefficients, and hydrogen bond network properties with very small statistical errors. The resulting structural and dynamical properties are compared well with the experimental data, demonstrating the superior accuracy of this machine learning force field. This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems. Molecular dynamics ; Methanol ; Dynamics (music) ; Computer science ; Statistical physics ; Chemical physics ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Fatemeh Shahbazi | Mohammad Nasr Esfahani | Amir Keshmiri | Masoud Jabbari
Source: Research Square (preprint, 2024-01-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Molecular Dynamics Simulation for Prediction of Binding Kinetics Kinetics ; Dynamics (music) ; Molecular dynamics ; Computer science ; Receptor–ligand kinetics ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Classical mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Protein Structure and Dynamics
Authors: Sanghoon Lee | Hyunwoo Kim
Source: ChemRxiv (preprint, 2024-11-29)
Field: Materials Science — Materials Chemistry

Revealing the Impact of Aggregations in the Graph-based Molecular Machine Learning: Electrostatic Interaction versus Pooling Methods Molecular structures that can be readily represented by graphs comprising constituent atoms (nodes) and their chemical bonds (edges) can also be used as input data for well-known machine learning (ML) models that process this data, such as graph neural networks (GNNs). GNNs showed a reasonable performance in the predicting properties of chemical systems. In typical applications of GNNs to chemistry-related fields, the main objective is to create an optimal molecular representation by aggregating atomic features and pooling features in the graph. In this study, we investigated two different approaches that can possibly generate better molecular representations. First, we created intermolecular edges to predict the photochemical properties of chromophore molecules in the solution. These intermolecular edges were constructed using atomic partial charges, inspired from the fact that electrostatic interaction is the main component of solute-solvent interaction. In the second approach, we investigated the effect of the aggregation and pooling functions. The results showed that intermolecular electrostatic interactions based on ground state charges prevent the GNN model from generating more effective molecular representations. On the contrary, the model demonstrated better performance when the averaging and adding operations were employed in a hybrid manner for aggregation and pooling functions. Pooling ; Artificial intelligence ; Graph ; Computer science ; Machine learning ; Data science ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Siyu Gao | Yiqun Luo | Xingyu Liu | Noa Marom
Source: ChemRxiv (preprint, 2024-12-20)
Field: Materials Science — Polymers and Plastics

Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning Excited-state properties of crystalline organic semiconductors are key to organic electronic device applications. Machine learning (ML) models capable of predicting these properties could significantly accelerate materials discovery. We use the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm to generate models to predict the first singlet excitation energy, which corresponds to the optical gap, the first triplet excitation energy, the singlet-triplet gap, and the singlet exciton binding energy of organic molecular crystals. To train the models we use the “PAH101" dataset of many-body perturbation theory calculations within the GW approximation and Bethe- Salpeter equation (GW+BSE) for 101 crystals of polycyclic aromatic hydrocarbons (PAHs). The best-performing SISSO models yield predictions within about 0.2 eV of the GW +BSE reference values. SISSO models are selected based on considerations of accuracy and computational cost to construct materials screening workflows for each property. The screening targets are chosen to demonstrate typical use cases relevant for organic electronic devices. We show that the workflows based on SISSO models can effectively screen out most of the materials that are not of interest and significantly reduce the number of candidates selected for further evaluation using computationally expensive excited-state theory. Excited state ; Organic semiconductor ; Semiconductor ; Materials science ; State (computer science) ; Optoelectronics ; Computer science ; Physics ; Atomic physics ; Algorithm Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Jiale Ma | Wenchao Zhang | Zhiqiang Han | Qingyan Xu | Haidong Zhao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

An Explainable Deep Learning Model Based on Multi-Scale Microstructure Information for Establishing Composition-Microstructure-Property Relationship of Aluminum Alloys Microstructure ; Aluminium ; Property (philosophy) ; Materials science ; Scale (ratio) ; Composition (language) ; Artificial intelligence ; Metallurgy ; Computer science ; Geography ; Philosophy ; Epistemology Machine Learning in Materials Science
Authors: Ganesh D Sathe | Jayesh Jadhav | Manik Kadam | Anagha Pathak | Sandesh Jadkar | Vishal Bhalla
Source:  (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Thermal Conductivity in Fe Alloys Thermal conductivity is an essential feature that affects boilers and heat transfer devices, among other applications to transport heat effectively in fe alloys. The conductivity of heat has generally been determined by laborious and costly experimental techniques. In consideration of material composition data, the current work studies the application of machine learning (ML) models to estimate thermal conductivity in Fe alloys. We evaluate the efficacy of many regression models, such as Support Vector Machines (Regression), Linear Regressor, Random Forest, XGBoost, etc., The findings show that amongst all other used machine learning algorithms CatBoost is capable of accurately and successfully predicting thermal conductivity. The CatBoost model’s average squared error (MSE), average absolute error (MAE), and average absolute percentage error (MAPE) were 3.44, 1.37, and 0.049 respectively on the test set with coefficient of determination $\mathbf{R 2}$ value of 0.97. This study lays the path for using ML to speed up the production of novel Fe alloys with customized thermal conductivity characteristics. Thermal conductivity ; Materials science ; Thermal ; Artificial intelligence ; Computer science ; Metallurgy ; Composite material ; Thermodynamics ; Physics Material Properties and Failure Mechanisms ; Metallurgical Processes and Thermodynamics ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Jiantao Wang | Peitao Liu | Heyu Zhu | Mingfeng Liu | Hui Ma | Yun Chen | Yan Sun | Xing‐Qiu Chen
Source: arXiv (Cornell University) (preprint, 2024-11-02)
Field: Materials Science — Materials Chemistry

Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and chemistry. This necessitates the continual development of MLIP models with improved accuracy and efficiency, which enable long-time scale molecular dynamics simulations to unveil the intricate underlying mechanisms that would otherwise remain elusive. Among various existing MLIP models, the moment tensor potential (MTP) model employs a highly descriptive rotationally-covariant moment tensor to describe the local atomic environment, enabling the use of even linear regression for model fitting. Although the current MTP model has achieved state-of-the-art efficiency for similar accuracy, there is still room for optimizing the contraction process of moment tensors. In this work, we propose an effective genetic algorithm based optimization scheme that can significantly reduce the number of independent moment tensor components and intermediate tensor components. This leads to a speedup of nearly one order of magnitude in efficiency and also improved accuracy compared to the traditional MTP model for intricate basis sets. We have applied our improved MTP model to predicting the energetic and dynamical properties of various point and planar defects in Ni-Al alloys, showing overall good performances and in general outperforming the semi-empirical potentials. This work paves the way for fast and accurate atomistic modeling of complex systems and provides a useful tool for modeling defects in Ni-Al alloys. Moment tensor ; Tensor (intrinsic definition) ; Moment (physics) ; Interatomic potential ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Computational science ; Physics ; Mathematics ; Classical mechanics ; Geometry ; Molecular dynamics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Bing Bai | Xu Han | Shi Xiu Wu | Jin Gao | Xinfu He | Wen Yang
Source: Mechanical Engineering Journal (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The influence of alloy composition on neutron irradiation swelling of austenitic stainless steel by deep neural network The main reason that restricts the increase of fast reactor burnup is the irradiation swelling of the cladding. The reported research shows that the main elements and trace alloy/impurity elements have a great influence on the irradiation swelling. However, the behavior of the above elements in the process of coupling with irradiation defects is complex, and it is difficult to directly measure the relationship between these elements, irradiation defects and microstructure evolution in experiments. The emergence of machine learning and big data mining technology will help to gain new understanding of the impact of irradiation swelling on austenitic stainless steel, so as to find a new type of austenitic stainless steel cladding material resistant to irradiation swelling. Therefore, in this work, about 1000 groups of data such as composition, irradiation conditions and irradiation swelling of austenitic stainless steel are collected, and the data are cleaned and screened for modelling by machine learning. The deep neural network with back propagation is used in this work, and the correlation between alloy composition such as Cr, Ni, Ti and C, irradiation dose and temperature and irradiation swelling of austenitic stainless steel is established. The results show that the addition of a certain amount of Ti and Si can effectively inhibit the irradiation swelling of austenitic stainless steel, but the addition of Ni will aggravate the swelling effect. The addition of Cr, Ni, Ti and Si will increase the swelling inflection point dose, while the addition of C and P will reduce the swelling inflection point dose. Besides, the influence of multi factor coupling such as composition on irradiation swelling of austenitic stainless steel will help to promote the material optimization design of austenitic stainless steel cladding material. Swelling ; Materials science ; Irradiation ; Austenite ; Austenitic stainless steel ; Alloy ; Metallurgy ; Cladding (metalworking) ; Microstructure ; Composite material ; Corrosion Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Aanish Paruchuri | Yunfei Wang | Xiaodan Gu | Arthi Jayaraman
Source: arXiv (Cornell University) (preprint, 2024-09-15)
Field: Materials Science — Materials Chemistry

Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends In this paper we present a new machine learning workflow with unsupervised learning techniques to identify domains within atomic force microscopy images obtained from polymer films. The goal of the workflow is to identify the spatial location of the two types of polymer domains with little to no manual intervention and calculate the domain size distributions which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered or disordered domains. We briefly review existing approaches used in other fields, computer vision and signal processing that can be applicable for the above tasks that happen frequently in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform or discrete cosine transform with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of 144 input AFM images, we then used an existing porespy python package to calculate the domain size distribution from the output of that image from DFT based workflow. The information and open source codes we share in this paper can serve as a guide for researchers in the polymer and soft materials fields who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline or amorphous domains, sharp or rough interfaces between domains, or micro or macrophase separated domains. Atomic force microscopy ; Materials science ; Microscopy ; Polymer ; Nanotechnology ; Scanning Force Microscopy ; Composite material ; Physics ; Optics Machine Learning in Materials Science
Authors: Kexin Jia | Kaitian Zhao | Hongcan Sun | Shuhong Xu | Chunlei Wang | Ruohu Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Synthesis of Full-Color Carbon Dots Full color ; Computer science ; Artificial intelligence ; Materials science ; Optoelectronics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Rubens C. Souza | Júlio César Duarte | Ronaldo Ribeiro Goldschmidt | Itamar Borges
Source: ChemRxiv (preprint, 2024-10-18)
Field: Materials Science — Materials Chemistry

Machine learning prediction of electronic molecular excited state properties Accurate knowledge of electronic molecular properties of excited states is fundamental for understanding the behavior of functional materials for organic electronics and sensors. In this work, we focus on determining the properties of the most intense peak in the electronic absorption spectra of organic molecules. For this purpose, we employed the quantum chemistry QM-symex dataset, which has approximately 173,000 organic molecules and time-dependent DFT (TD-DFT) data of the first ten electronic absorption transitions. Each one is identified by its Cartesian coordinates. From the original QM-symex, we built a new dataset named QM-symex-modif by converting the molecular Cartesian coordinates into the Simplified Molecular Input Line Entry System (SMILES) format, selecting the main transition orbitals of the singlet most intense absorption peak, their corresponding oscillator strengths and transition energies. We employed twenty machine learning (ML) algorithms to investigate these target properties plus the highest occupied molecular orbitals (HOMOs). As inputs for the ML algorithms, we employed several chemical descriptors generated in the RDKit tool for each molecule using the corresponding SMILES format. The QM-symex-modif dataset significantly improved the accuracy of ML predictions of these key photophysical properties. Very good mean absolute errors were obtained for the test set composed of 45,056 molecules. Additionally, a Shapley additive explanations (SHAP) analysis was carried out to evaluate the importance of the input parameters for the investigated ML models. We found several interesting relationships involving the input parameters. In particular, the molecular weight has enormous importance among several different descriptors in determining HOMO values and the transition orbitals. Excited state ; State (computer science) ; Computer science ; Machine learning ; Artificial intelligence ; Physics ; Atomic physics ; Algorithm Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Various Chemistry Research Topics
Authors: Shun Muroga
Source: Kobunshi (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Multimodal Deep Learning: An Emerging Technique Integrating Multiple Generative Models for Virtual Screening of Complex Materials In this article, we propose a multimodal deep learning technique that enables virtual screening of complex materials with diverse properties. Our method encompasses physical and chemical structure information of complex materials based on an ensemble of generative models of images and spectra, and integrated prediction model for property prediction. The proposed method was demonstrated on polymer composites with 10 compositions and 8 properties. From a vast amount of predictions by multimodal deep learning, Pareto optimal solutions under conflicting multiple properties were clearly identified. Our results represents the potential of multimodal deep learning offering significant insights for the exploration and optimization of complex materials with desired properties. Generative grammar ; Computer science ; Artificial intelligence ; Deep learning ; Generative model ; Human–computer interaction Machine Learning in Materials Science ; Drilling and Well Engineering ; Non-Destructive Testing Techniques
Authors: Li Tang | Shuxin Wang
Source: Cell Signaling (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Accelerating metal nanocluster synthesis: A high-throughput approach with machine learning assistance Understanding the total structure of noble metal nanoclusters has been a major dream of colloid chemists, however, relatively low synthesis efficiency is still the main issue of cluster research. In a recent publication in Chemistry - A European Journal, Shuxin Wang and co-workers reported the development of a machine learning (ML) guided automated high-throughput synthesis platform, which has been crucial for accelerating the synthesis of clusters. The authors ingeniously implemented a cyclical feedback mechanism involving high-throughput synthesis and machine learning. A series of nanoclusters with atomically accurate structures were successfully synthesized, namely, [Au41Cu66(SC6H11)44](SbF6)3, Au40Cu34(4-S-PhF)40, Au40Cu34(4-S-PhF)40, Au18Cu32(3,5-C8H9S)36, [Au6Cu6(SPh)12]n, Cu-NC respectively, are abbreviated as Au41Cu66, Au40Cu34, Au18Cu32, Au18, Au6Cu6, Cu-NC. This approach not only significantly optimized the synthesis pathway, but also offered profound insights into the influence of various reaction variables, thereby illuminating fresh perspectives for advancing research in the realm of metal nanoclusters. Nanoclusters ; Throughput ; Nanotechnology ; Realm ; Cluster (spacecraft) ; Computer science ; Noble metal ; Chemistry ; Metal ; Materials science ; Political science ; Organic chemistry Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials