jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Yang | Ziyi Chen | Tianyu Feng | Fei Xiong | Jue Wang | Yangang Wang | Zongguo Wang
Source: Scientific Reports (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Tripartite interaction representation algorithm for crystal graph neural networks Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In the field of materials science, there is a current research emphasis on exploring techniques for characterizing material structures to achieve precise descriptions. This paper proposes a crystal graph convolution neural network model that incorporates a tripartite interaction approach. The model not only incorporates atomic information, bond lengths, and bond angles but also offers a method for updating atoms and bond lengths, facilitating accurate descriptions of crystal structures by capturing implicit structural information. Focusing on predicting the formation energy of crystalline compounds, our results demonstrate improved predictive accuracy compared to existing representation algorithms. The average error of the formation energy in the random dataset, demonstrating robust generalization, is merely 0.048eV/atom, with an impressive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> </mml:math> value of 0.994. Additionally, this paper establishes a crystal graph neural network framework for predicting algorithm performance. By integrating automatic parallel algorithms and an automated process, we achieve a synthesis of these techniques, enhancing computational efficiency and streamlining algorithm usage. Computer science ; Representation (politics) ; Graph ; Artificial intelligence ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Jing Zhou | Syed Shoaib Ahmad Shah | Sumaira Naeem | Bilal Siddique | Numan Khan | Abrar U. Hassan | Mohamed A. El‐Sheikh | Hosam O. Elansary
Source: Chemical Physics (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Revolutionizing polymer engineering for Photodetectors: A Machine Learning-Assisted paradigm for rapid materials discovery Chemistry ; Photodetector ; Nanotechnology ; Data science ; Optoelectronics ; Computer science ; Materials science Machine Learning in Materials Science ; Analytical Chemistry and Sensors ; Ionic liquids properties and applications
Authors: Christopher M. Collins | Hasan M. Sayeed | George R. Darling | John B. Claridge | Taylor D. Sparks | Matthew J. Rosseinsky
Source: Faraday Discussions (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Integration of generative machine learning with the heuristic crystal structure prediction code FUSE The prediction of new compounds <i>via</i> crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction: performing crystal structure prediction <i>via</i> heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge. Generative grammar ; Fuse (electrical) ; Heuristic ; Code (set theory) ; Computer science ; Artificial intelligence ; Machine learning ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Saleh Abdul Al | A. Allouche
Source: Chemical Physics Letters (preprint, 2024-09-03)
Field: Materials Science — Materials Chemistry

Neural network approach for predicting infrared spectra from 3D molecular structure Anharmonicity ; Spectral line ; Ab initio ; Metric (unit) ; Artificial neural network ; Statistical physics ; Density functional theory ; Dipole ; Computer science ; Infrared ; Scaling ; Molecule ; Infrared spectroscopy ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Archit Anand | Priyanka Kumari | Ajay Kumar Kalyani
Source: Computational Materials Science (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

High throughput screening of new piezoelectric materials using graph machine learning and knowledge graph approach Computer science ; Graph ; Throughput ; Piezoelectricity ; Artificial intelligence ; Machine learning ; Materials science ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Advanced Computing and Algorithms ; Fuel Cells and Related Materials
Authors: Viktoriia Zinkovich | Vadim Sotskov | Alexander V. Shapeev | Evgeny V. Podryabinkin
Source: npj Computational Materials (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Exhaustive search for novel multicomponent alloys with brute force and machine learning Abstract We present an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components. It is particularly important for high entropy alloys (HEAs), where multiple principal elements can form numerous potential intermetallic compounds during the condensation process, making it challenging to predict the dominant phase. Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice (FCC or BCC) accelerated by machine-learning interatomic potentials. The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures. The candidate structures are evaluated using the low-rank potential (LRP), trained to reproduce energies of structures equilibrated with density functional theory (DFT). Thanks to extreme computational effectiveness of the LRP, it is feasible to evaluate hundreds of thousands of structures per second, per CPU core. Thus, our algorithm screens a complete set of candidate structures for a given system without missing any configurations. We validated our method on systems with BCC (Nb-W, Nb-Mo-W, V-Nb-Mo-Ta-W) and FCC (Cu-Pt, Cu-Pd-Pt, Cu-Pd-Ag-Pt-Au) lattices and discovered 268 new alloys not reported in the AFLOW database 1 , which we used as a benchmark. Intermetallic ; Convex hull ; Density functional theory ; Materials science ; Crystal structure prediction ; Algorithm ; Crystal structure ; Interatomic potential ; Lattice (music) ; Computer science ; Computational science ; Computational chemistry ; Chemistry ; Crystallography ; Mathematics ; Physics ; Molecular dynamics ; Regular polygon ; Geometry ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies
Authors: Abdus Sattar | Md. Asif Mahmud Ridoy | Aloke Kumar Saha | Hafiz Md. Hasan Babu | Mohammad Nurul Huda
Source: Intelligent Systems with Applications (article, 2024-06-20)
Field: Chemistry — Analytical Chemistry

A comprehensive approach to detecting chemical adulteration in fruits using computer vision, deep learning, and chemical sensors Contamination of harmful additives in fruits has become a concerning norm these days. Owing to the great popularity of fruits, dishonest vendors frequently use harmful chemicals to contaminate fruits to extend their shelf life, which is extremely dangerous for the general public's health. To mitigate this issue, machine-learning algorithms like Decision Tree Classifier, Naïve Bayes and a deep learning model named “DurbeenNet” are evaluated separately. Alongside, a computer vision-based detection method coupled with a hybrid model is proposed that combines deep learning and chemical sensor. Formaldehyde Detection Sensor is used in this experiment to take reading of the sensor data. Mango, Apple, Banana, and Malta are taken as sample fruits in this study. Sensor data for both fresh and chemical-mixed fruit is newly collected using Formaldehyde Detection Sensor. The above mentioned sensor data along with the previously captures images of both fresh and chemical-mixed state are being integrated to a hybrid model. Among two machine learning algorithms naïve bayes come up with 82 % accuracy. Using both sensor data and captured image data, the proposed model “SensorNet” provides highest accuracy of 97.03 % which is substantial than “DurbeenNet” model's accuracy. Through the utilization of these fruit samples, formaldehyde detection sensor provides instantaneous detection, identifying the specific toxic substances present in the contaminated fruits. Artificial intelligence ; Computer science ; Computer vision ; Chemical sensor ; Chemistry Spectroscopy and Chemometric Analyses ; Dye analysis and toxicity ; Identification and Quantification in Food
Authors: Jiaqi An | Yanlong Ran | Jiaping Lin | Liangshun Zhang
Source: Computational Materials Science (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Prediction of microstructural evolution of multicomponent polymers by Physics-Informed neural networks Artificial neural network ; Polymer ; Statistical physics ; Materials science ; Nanotechnology ; Physics ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Rheology and Fluid Dynamics Studies ; Model Reduction and Neural Networks
Authors: Quanliang Liu
Source: Applied and Computational Engineering (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Applications of machine learning in materials science: From a methodological point of view Discovering new functional materials with certain properties using high-throughout methods is of vital importance for materials science. The advancement of the machine learning method has provided a new, more efficient pathway for this procedure. Traditional trial-and-error methods have been supplanted by in-silico simulations, which facilitate rapid material discovery. Machine learning (ML) has further accelerated this process by uncovering patterns and relationships within complex computational data sets, thus enhancing predictive abilities for material properties and performance. This integration of computational methods and machine learning holds great potential, promising a future where material limitations are overcome, catalyzing technological breakthroughs. Furthermore, ML also inspired advancements in fields like crystallography and metallurgy, and enhancing energy storage materials. Despite challenges in model interpretability, overfitting, and data quality, ML presents an exciting evolution toward a data-driven discipline. Ultimately, ML promises to reduce the time from materials discovery to deployment, turning material limitations into catalysts for innovation. Overfitting ; Interpretability ; Computer science ; Machine learning ; Artificial intelligence ; Process (computing) ; Software deployment ; Materials informatics ; Data science ; Software engineering ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Lijie Ding | Chi-Huan Tung | Bobby G. Sumpter | Wei-Ren Chen | Changwoo Do
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Machine Learning Inversion from Scattering for Mechanically Driven Polymers We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching, and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration, and the off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the Machine Learning inversion. Finally, we train a Gaussian Process Regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters. Inversion (geology) ; Polymer ; Scattering ; Materials science ; Computer science ; Nanotechnology ; Artificial intelligence ; Geology ; Physics ; Composite material ; Optics ; Seismology Machine Learning in Materials Science
Authors: Delchere Don-tsa | Messanh Agbeko Mohou | Kossi Amouzouvi | M. Mâaza | Katawoura Beltako
Source: Machine Learning Science and Technology (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Predictive models for inorganic materials thermoelectric properties with machine learning Abstract The high computational demand of the Density Functional Theory (DFT) based method for screening new materials properties remains a strong limitation to the development of clean and renewable energy technologies essential to transition to a carbon-neutral environment in the coming decades. Machine Learning comes into play with its innate capacity to handle huge amounts of data and high-dimensional statistical analysis. In this paper, supervised Machine Learning models together with data analysis on existing datasets obtained from a high-throughput calculation using Density Functional Theory are used to predict the Seebeck coefficient, electrical conductivity, and power factor of inorganic compounds. The analysis revealed a strong dependence of the thermoelectric properties on the effective masses, we also proposed a machine learning model for the prediction of highly performing thermoelectric materials which reached an efficiency of 95 percent. The analyzed data and developed model can significantly contribute to innovation by providing a faster and more accurate prediction of thermoelectric properties, thereby, facilitating the discovery of highly efficient thermoelectric materials. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Computer science ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Zaheer Abbas | Md Mostakim Meraz | Wenhong Yang | Weishen Yang | Wen‐Hua Sun
Source: Catalysts (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning The present study explored machine learning methods to predict the catalytic activities of a dataset of 165 α-diimino nickel complexes in ethylene polymerization. Using 25 descriptors as the inputs, the XGBoost model presented the optimal performance among six different algorithms (R2 = 0.999, Rt2 = 0.921, Q2 = 0.561). The results of the analysis indicate that high activity is related to the presence of polarizable atoms and less bulky substituents within the N-aryl group. This approach offers valuable insights on the variation principle of catalytic activity as a function of complex structure, helping to effectively design and optimize α-diimino Ni catalysts with desirable performance. Catalysis ; Polymerization ; Aryl ; Ethylene ; Nickel ; Polarizability ; Function (biology) ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Polymer ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Licheng Yu | Wenwen Zhang | Zhihao Nie | Jingjing Duan | Sheng Chen
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction Traditional design/optimization of metal-organic frameworks (MOFs) is time-consuming and labor-intensive. In this study, we utilize machine learning (ML) to accelerate the synthesis of MOFs. We have built a library of over 900 MOFs with different metal salts, solvent ratios, reaction durations and temperatures, and utilize zeta potentials as target variables for ML training. A total of four ML models have been used to train the collected dataset and assess their convergence performances, where Random Forest Regression (RFR) and Gradient Boosting Regression (GBR) models show strong correlation and accurate predictions. We then predicted two kinds of MOFs from RFR and GBR models. Remarkably, the experimentally data of the synthesized MOFs closely matched the predicted results, and these MOFs exhibited excellent electrocatalytic performances for oxygen evolution. This study would have general implications in the utilization of machine learning for accelerating the synthesis of MOFs for diverse applications. Zeta potential ; Solvent ; Oxygen ; Composition (language) ; Metal ; Oxygen evolution ; Charge (physics) ; Chemistry ; Chemical engineering ; Computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Physical chemistry ; Engineering ; Physics ; Electrochemistry ; Electrode Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Lu Li | Jianing Shen | Qinkun Xiao | Chaozheng He | Jinzhou Zheng | Chaoqin Chu | Chen Chen
Source: Chinese Chemical Letters (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

Stable crystal structure prediction using machine learning-based formation energy and empirical potential function Function (biology) ; Energy (signal processing) ; Crystal structure prediction ; Artificial intelligence ; Crystal structure ; Computer science ; Machine learning ; Chemistry ; Materials science ; Physics ; Crystallography ; Quantum mechanics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Yuexuan Shu | Jiwei Chen | Beibei Xu | Zhengchang Liu | Hao Zheng | Fan Zhang | Weiqi Fu
Source: Advanced Intelligent Systems (article, 2024-04-10)
Field: Materials Science — Biomaterials

Biomimetic Synthesis of Nanosilica by Deep Learning‐Designed Peptides and Its Anti‐UV Application The silicifying peptide R5 (H‐SSKKSGSYSGSKGSKRRIL‐OH) derived from diatoms is extensively investigated, but the mechanism underlying silica synthesis by R5 or R5‐alike peptides is still poorly understood, limiting the design of silicifying peptides Herein, machine learning techniques are used to design peptides with silicifying functionality. Utilizing a comprehensive dataset of peptides and their corresponding silicification outcomes, a deep learning model based on antimicrobial peptide migration learning is created. This model exhibits the remarkable capability to accurately predict peptide sequences with a high potential for facilitating silica formation. A selection of artificially designed peptides can catalyze the biomimetic synthesis of nanosilica, some of which demonstrate better catalytic activity with a wider pH range and faster reaction rate compared with the peptide R5. Additionally, the designed peptide is used to wrap the model diatom Phaeodactylum tricornutum with nanosilica coatings, resulting in a significant enhancement in the UV resistance of cells. The new silicified peptides are highly significant for advancing the understanding of the silica synthesis mechanism in diatoms, and the encapsulation of P. tricornutum has potential benefits in the development of new biosensors. Nanotechnology ; Combinatorial chemistry ; Materials science ; Chemistry Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Protist diversity and phylogeny
Authors: Huyang Li | Xiangli Meng | Yuhang Jing | Lingzhi Cong | Xintong Zhang | Junqing Zhao | Yi Sun | Weiqi Li | Jihong Yan | Jianqun Yang | Xingji Li
Source: Computational Materials Science (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

Primary radiation damage in Si1−xGex alloys: Molecular dynamics study with machine-learning interatomic potential Interatomic potential ; Molecular dynamics ; Radiation damage ; Primary (astronomy) ; Materials science ; Dynamics (music) ; Radiation ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Ion-surface interactions and analysis
Authors: Tianrun Ma | Jianwei Wang | Liqing Ban | Huijun He | Ziliang Lu | Jie Zhu | Xiaoling Ma
Source: Applied Catalysis A General (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

A machine-learning-based composition design of ternary Cu-based Rochow-Müller catalyst with high M2 selectivity Ternary operation ; Catalysis ; Selectivity ; Chemistry ; Stacking ; Heterojunction ; Composition (language) ; Chemical engineering ; Materials science ; Optoelectronics ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science