jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chayanit Wechwithayakhlung | Geoffrey R. Weal | Yu Kaneko | Paul Hume | Justin M. Hodgkiss | Daniel M. Packwood
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Polymers and Plastics

Exciton diffusion in amorphous organic semiconductors: reducing simulation overheads with machine learning Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, therefore shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials. Exciton ; Pentacene ; Computer science ; Diffusion ; Intermolecular force ; Coupling (piping) ; Amorphous solid ; Materials science ; Charge (physics) ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Chemistry ; Condensed matter physics ; Nanotechnology ; Quantum mechanics ; Molecule Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Kiho Matsubara | Kei Takahashi | Takeshi Matsuda | Yuji Ueki | Noriaki Seko | Ryohei Kakuchi
Source: ChemRxiv (preprint, 2023-03-30)
Field: Materials Science — Materials Chemistry

Building Machine Learning Models for Reactivity Prediction in Radiation-Induced Graft Polymerization Using Interpretable Parameters Machine learning prediction models for radiation-induced graft polymerization reactivity of methacrylate monomers were feasibly built with chemically interpretable parameters. The reactivity can be predicted based on the decision-tree-based machine learning algorithms. Among these algorithms, the XGBoost algorithm exhibited a good performance using five interpretable parameters: the solvation free energy of the methacrylate monomer in water, solvation free energy of the methacrylate monomer in hexane, methacrylate monomer radius, conformational entropy of the methacrylate monomers, and the dipole moments of the methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion. Methacrylate ; Monomer ; Polymerization ; Solvation ; Reactivity (psychology) ; Photopolymer ; Materials science ; Polymer chemistry ; Computer science ; Chemistry ; Organic chemistry ; Polymer ; Molecule Machine Learning in Materials Science ; Advanced Radiotherapy Techniques ; Photopolymerization techniques and applications
Authors: Yong‐Chae Chung | Chihun Kim | Seunghwan Jung
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Pv-Pec Promising Materials Based on Chemical Composition: An Accelerated Machine Learning Study Chemical composition ; Composition (language) ; Materials science ; Artificial intelligence ; Machine learning ; Computer science ; Process engineering ; Engineering ; Physics ; Thermodynamics ; Art Machine Learning in Materials Science
Authors: Hieu‐Chi Dam | Tien-Sinh Vu | Minh-Quyet Ha | Duong‐Nguyen Nguyen | Viet-Cuong Nguyen | Yukihiro Abe | Truyen Tran | Tran Doan Huan | Hiori Kino | Takashi Miyake | Koji Tsuda
Source: Research Square (preprint, 2023-07-13)
Field: Materials Science — Materials Chemistry

Deep learning reveals key aspects to help interpret the structure-property relationships of materials Key (lock) ; Property (philosophy) ; Data science ; Deep learning ; Computer science ; Artificial intelligence ; Psychology ; Epistemology ; Computer security ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Bing Bai | Xu Han | Shi Wu | Jin Gao | Xinfu He | Wen Yang
Source: The Proceedings of the International Conference on Nuclear Engineering (ICONE) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

INFLUENCE ANALYSIS OF ALLOY ELEMENTS ON RADIATION SWELLING OF AUSTENITIC STAINLESS STEEL BASED ON DEEP NEURAL NETWORK The main reason that restricts the increase of fast reactor burn up is the radiation swelling of the cladding. The reported research shows that the main elements and trace alloy/impurity elements have a great influence on the radiation swelling. However, the behavior of the above elements in the process of coupling with radiation defects is complex, and it is difficult to directly measure the relationship between these elements, radiation 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 radiation swelling on austenitic stainless steel, so as to find a new type of austenitic stainless steel cladding material resistant to radiation swelling. Therefore, in this work, about 1000 groups of data such as composition, irradiation conditions and radiation 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 radiation 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 radiation swelling of austenitic stainless steel, but the addition of Ni will aggravate the swelling effect. The addition of Cr, Ni, Ti and Cr 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 radiation swelling of austenitic stainless steel will help to promote the material optimization design of austenitic stainless steel cladding material. Austenite ; Swelling ; Materials science ; Austenitic stainless steel ; Irradiation ; Cladding (metalworking) ; Alloy ; Metallurgy ; Inflection point ; Radiation ; Microstructure ; Composite material ; Corrosion ; Optics Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Metallurgy and Material Forming
Authors: Maria Angelica Moya | Eduardo Nadal Valles
Source:  (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Deep learning as an educational goal in Industrial Chemistry The methodology applied in the course Industrial Chemistry, in the 4th year of the major Industrial Engineering, based on active teaching methods, individual follow up, adjustment of the curriculum to include cases directly related to the local reality, use of various resources and activities to approach the knowledge from diverse angles, and implementation of thinking routines and metacognition, yielded highly satisfactory results for professors and students alike. Authentic assessment strategies aim to reinforce the understanding of the current challenges in engineering, and seek to awaken an intrinsic personal motivation as the source of fulfillment for the achievements and usefulness that learning provides for the professional life. These are all distinctive attributes of deep learning. Computer science ; Data science ; Chemistry Various Chemistry Research Topics
Authors: Jinming Fan | Chao Qian | Shaodong Zhou
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Driven, Interactive Design for Nonlinear Optical Molecules Based on Group Contribution Method A Lewis-mode group contribution method (LGC) -- multi-stage Bayesian neural network (msBNN) -- evolutionary algorithm (EA) framework is reported for rational design of D-Pi-A type organic small-molecule nonlinear optical materials is presented. Upon combination of msBNN and corrected Lewis-mode group contribution method (cLGC), different optical properties of molecules are afforded accurately and efficiently - by using only a small data set for training. Moreover, by employing the EA model designed specifically for LGC, structural search is well achievable. The logical origins of the well performance of the framework are discussed in detail. Considering that such a theory guided, machine learning framework combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular design related problems in wider fields. Artificial neural network ; Nonlinear system ; Set (abstract data type) ; Computer science ; Group (periodic table) ; Nonlinear optical ; Artificial intelligence ; Mode (computer interface) ; Bayesian probability ; Bayesian network ; Machine learning ; Chemistry ; Physics Nonlinear Optical Materials Research ; Photonic and Optical Devices ; Computational Drug Discovery Methods
Authors: Minjie Chen | Haoran Li | Diego Serrano | Shukai Wang
Source:  (preprint, 2023-05-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation &lt;p&gt;This paper presents the MagNet-AI platform as an online platform to demonstrate the “Neural Network as Datasheet” concept for B–H loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the B–H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc-bias. Both LSTM and Transformer based neural network models are developed, verified, and compared. The training and inference process of the neural network are fully automated to minimize the impact of human error. The neural network can be used to rapidly predict B–H loops under different operating conditions, compare materials, and recommend materials for design. Neural networks are also effective in compressing the information contained in the raw database, and enable rapid material evaluation and comparison.&lt;/p&gt; Datasheet ; Artificial neural network ; Transformer ; Computer science ; Inference ; Waveform ; Magnet ; Process (computing) ; Toolchain ; Artificial intelligence ; Machine learning ; Voltage ; Engineering ; Electrical engineering Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques
Source: JPS Hot Topics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Powered by Machine Learning: Obtaining Spectral Conductivity and Chemical Potential of Thermoelectric Materials from Experimental Data We propose a machine-learning method to obtain the fundamental physical quantity, namely, the spectral conductivity, from experimental data of thermoelectric coefficients. Our study introduces a new data-driven approach that reveals the underlying low-energy electronic states of high-performance thermoelectric materials. Thermoelectric effect ; Thermoelectric materials ; Conductivity ; Materials science ; Energy (signal processing) ; Experimental data ; Computer science ; Machine learning ; Artificial intelligence ; Engineering physics ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: H. Matsui | Yuta Muramoto | Takashi Kakubo | Naoya Amino | Tomoya Uruga | Minh-Quyet Ha | Duy-Tai Dinh | Hieu‐Chi Dam | Mizuki Tada
Source: Research Square (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

Machine-learning-revealed reaction statistics via 3D spectroimaging for copper sulfidation of adhesive layers in rubber/brass composite Abstract The sulfidation of Cu derived from Cu-Zn alloy (brass) in S-containing rubber, which is used for plating steel-cord-reinforced rubber tires, is suggested to be the key reaction for adhesive behavior between brass and rubber in tires. However, the heterogeneous structures of rubber/brass interfaces have prevented us from understanding the sulfidation of metallic Cu in brass and the formation of Cu sulfides at the brass surface and buried rubber interface. We report visualizing the three-dimensional spatial location and chemical states of Cu species in a rubber/brass composite by three-dimensional (3D) X-ray spectroimaging with X-ray absorption fine structure-computed tomography during the aging process of the rubber/brass composite for the first time. Machine-learning derived reaction statistics for the 3D spectroimaging data revealed the reaction mechanism of the Cu sulfidation in the heterogeneous rubber/brass composite. Brass ; Sulfidation ; Adhesive ; Composite number ; Copper ; Natural rubber ; Materials science ; Composite material ; Metallurgy ; Layer (electronics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Memory and Neural Computing
Authors: Ruoyu Juan
Source: Materials research proceedings (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

Optimizing crystal plasticity model parameters via machine learning-based optimization algorithms Abstract. The field of materials science and engineering is constantly evolving, and new methods are being developed to improve our understanding of the relationship between microstructure and properties. One such method is crystal plasticity (CP) modeling, which is widely used for predicting the mechanical properties of crystals and phases. However, determining the constitutive parameters for CP models has been a significant challenge, with current methods relying on either direct chemical composition or inverse fitting, both of which can be time-consuming and lack accuracy. In this study, we propose an automated, advanced, and more efficient method for determining constitutive parameters by using a genetic algorithm (GA) optimization method coupled with machine learning. Our proposed method is applied to two widely used CP models, and the reference data for the calibration is the stress-strain curve from tensile tests. The results of the automated calibration process are then compared to numerical simulation results of CP models with known parameters, demonstrating the efficiency and accuracy of our proposed method. Calibration ; Algorithm ; Computer science ; Inverse ; Machine learning ; Artificial intelligence ; Genetic algorithm ; Optimization algorithm ; Field (mathematics) ; Process (computing) ; Experimental data ; Plasticity ; Materials science ; Mathematical optimization ; Mathematics ; Statistics Machine Learning in Materials Science ; Metallurgy and Material Forming ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jared Averitt | Sajedeh Pourianejad | Olubunmi O. Ayodele | Kirby Schmidt | Anthony Trofe | Joseph M. Starobin | Tetyana Ignatova
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Optimized nanodevice fabrication using clean transfer of graphene by polymer mixture: Experiments and Neural Network based simulations In this study, we investigate both experimentally and computationally the molecular interactions of two distinct polymers with graphene. Our experimental findings indicate that the use of a polymer mixture reduces the transfer induced doping and strain in fabricated graphene devices as compared to conventional single polymer wet transfer. We found that such reduction is related to the decreased affinity of mixture of polymethyl methacrylate and angelica lactone polymer for graphene. We investigated changes in binding energy (BE) of polymer mixture and graphene by considering energy decomposition analysis using a pre-trained potential neural network. It was found that numerical simulations accurately predicted two-fold reduction of BE and order of magnitude reduction of electrostatic interaction between polymers. Graphene ; Materials science ; Polymer ; Nanodevice ; Fabrication ; Nanotechnology ; Molecular dynamics ; Chemical engineering ; Composite material ; Computational chemistry ; Chemistry Graphene research and applications ; Low-power high-performance VLSI design ; Advancements in Semiconductor Devices and Circuit Design
Authors: Joo, Chonghyo | Jeong‐Dong Kim | Hyungtae Cho | Jaewon Lee | Sungho Suh | Junghwan Kim
Source: arXiv (Cornell University) (preprint, 2023-06-02)
Field: Chemistry — Analytical Chemistry

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties. Naphtha ; Distillation ; Cracking ; Artificial neural network ; Process engineering ; Composition (language) ; Process (computing) ; Computer science ; Chemistry ; Artificial intelligence ; Engineering ; Chromatography ; Organic chemistry Petroleum Processing and Analysis ; Fault Detection and Control Systems ; Crystallization and Solubility Studies
Authors: Hengrui Zhang | Jie Chen | James M. Rondinelli | Wei Chen
Source: arXiv (Cornell University) (preprint, 2023-12-27)
Field: Materials Science — Materials Chemistry

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here we present MolSets, a specialized ML model for molecular mixtures. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecule level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in virtual screening of the combinatorial chemical space. Expansive ; Molecular graph ; Chemical space ; Flexibility (engineering) ; Computer science ; Virtual screening ; Graph ; Molecule ; Battery (electricity) ; Artificial intelligence ; Theoretical computer science ; Topology (electrical circuits) ; Materials science ; Molecular dynamics ; Drug discovery ; Chemistry ; Mathematics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Filip Dinic | Zhibo Wang | Ihor Neporozhnii | Usama Bin Salim | Rochan Bajpai | Navneeth Rajiv | Vedant Chavda | Vishal Radhakrishnan | Oleksandr Voznyy
Source: Patterns (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Strain data augmentation enables machine learning of inorganic crystal geometry optimization Machine-learning (ML) models offer the potential to rapidly evaluate the vast inorganic crystalline materials space to efficiently find materials with properties that meet the challenges of our time. Current ML models require optimized equilibrium structures to attain accurate predictions of formation energies. However, equilibrium structures are generally not known for new materials and must be obtained through computationally expensive optimization, bottlenecking ML-based material screening. A computationally efficient structure optimizer is therefore highly desirable. In this work, we present an ML model capable of predicting the crystal energy response to global strain by using available elasticity data to augment the dataset. The addition of global strains improves our model's understanding of local strains too, significantly improving the accuracy of energy predictions on distorted structures. This allows us to construct an ML-based geometry optimizer, which we used for improving the predictions of formation energy for structures with perturbed atomic positions. Energy minimization ; Computer science ; Construct (python library) ; Work (physics) ; Strain energy ; Algorithm ; Global optimization ; Mathematical optimization ; Machine learning ; Biological system ; Artificial intelligence ; Mechanical engineering ; Mathematics ; Chemistry ; Finite element method ; Structural engineering ; Engineering ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions