jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aya Fujiwara | Sunao Nakanowatari | Yohei Cho | Toshiaki Taniike
Source: ChemRxiv (preprint, 2024-09-26)
Field: Materials Science — Materials Chemistry

Acquiring and Transferring Comprehensive Catalyst Knowledge through Integrated High-Throughput Experimentation and Automatic Feature Engineering Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La<sub>2</sub>O<sub>3</sub>-, TiO<sub>2</sub>-, and ZrO<sub>2</sub>-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning. Integrating high-throughput experimentation and automatic feature engineering in a loop enables the development of robust machine learning models, leading to comprehensive and transferable catalyst knowledge. Feature (linguistics) ; Throughput ; Computer science ; Feature engineering ; Artificial intelligence ; Knowledge management ; Engineering ; Engineering management ; Telecommunications ; Deep learning Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Pavel Korotaev | Aleksey Yanilkin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural Networks for Prediction of Swelling in Austenitic Steels Irradiated by Fast Neurons Austenite ; Swelling ; Irradiation ; Artificial neural network ; Materials science ; Metallurgy ; Artificial intelligence ; Computer science ; Composite material ; Nuclear physics ; Physics ; Microstructure Metal Alloys Wear and Properties ; Welding Techniques and Residual Stresses ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ying Fang | Hezhu Shao
Source: Preprints.org (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

Wenzhou TE: A First-Principles Calculated Thermoelectric Materials Database Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Database ; Engineering physics ; Computer science ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices
Authors: Halil Arslan | Khalid Hani Abushahla
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Ceramics and Composites

Prediction of Mass Attenuation Coefficients in Mixed Alkali and Borosilicate Glasses Using Machine Learning Approaches Borosilicate glass ; Alkali metal ; Attenuation ; Materials science ; Mineralogy ; Chemistry ; Composite material ; Physics ; Optics Glass properties and applications
Authors: Arindam Debnath
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys [version 2; peer review: 2 approved, 2 approved with reservations] Finite element method ; Yield (engineering) ; Element (criminal law) Machine Learning in Materials Science ; Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels
Authors: Naoki Yamane | Kan Hatakeyama‐Sato | Yuma Iwasaki | Yasuhiko Igarashi
Source: Science and Technology of Advanced Materials Methods (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Extraction of physicochemical laws by symbolic regression using a Bayesian information criterion In the search for new high-performance materials in materials science, especially in polynomial science, it is important to use physicochemical laws linking materials structure and physical properties, and predict the physical properties required for the design. Recently, machine learning (ML) has enabled us to extract patterns from large datasets and construct the data-driven model to predict physical properties. However, ML approach faces challenges such as interpretability and systematic errors of the data-driven model with limited data. Here, we propose a method for extracting an interpretable law from limited data, by combining a symbolic regression method and Bayesian information criterion. We focus on extracting a physicochemical law for the refractive index of polymer materials. The goal is to correct systematic errors and capture physicochemical laws more accurately. Combining explanatory variables from experiments, property calculations, and neural potential approximations, our method involves arithmetic operations on explanatory variables and selection through Bayesian information criterion. The results show that the proposed method is able to correct the results of the neural potential approximation and obtain interpretable and concise expressions for the physicochemical laws linking material structure and physical properties. Interpretability ; Computer science ; Symbolic regression ; Bayesian probability ; Artificial intelligence ; Machine learning ; Physical law ; Regression ; Data mining ; Algorithm ; Mathematics ; Statistics ; Genetic programming Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhengyu Zhang
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys [version 2; peer review: 2 approved, 1 approved with reservations] Finite element method ; Yield (engineering) ; Element (criminal law) Machine Learning in Materials Science ; Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels
Authors: Andreas Abildtrup Hansen | Anna Calissano | Aasa Feragen
Source: arXiv (Cornell University) (preprint, 2024-01-23)
Field: Materials Science — Materials Chemistry

Interpreting Equivariant Representations Latent representations are used extensively for downstream tasks, such as visualization, interpolation or feature extraction of deep learning models. Invariant and equivariant neural networks are powerful and well-established models for enforcing inductive biases. In this paper, we demonstrate that the inductive bias imposed on the by an equivariant model must also be taken into account when using latent representations. We show how not accounting for the inductive biases leads to decreased performance on downstream tasks, and vice versa, how accounting for inductive biases can be done effectively by using an invariant projection of the latent representations. We propose principles for how to choose such a projection, and show the impact of using these principles in two common examples: First, we study a permutation equivariant variational auto-encoder trained for molecule graph generation; here we show that invariant projections can be designed that incur no loss of information in the resulting invariant representation. Next, we study a rotation-equivariant representation used for image classification. Here, we illustrate how random invariant projections can be used to obtain an invariant representation with a high degree of retained information. In both cases, the analysis of invariant latent representations proves superior to their equivariant counterparts. Finally, we illustrate that the phenomena documented here for equivariant neural networks have counterparts in standard neural networks where invariance is encouraged via augmentation. Thus, while these ambiguities may be known by experienced developers of equivariant models, we make both the knowledge as well as effective tools to handle the ambiguities available to the broader community. Equivariant map ; Pure mathematics ; Mathematics ; Linguistics ; Computer science ; Geography ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis
Authors: Junfeng Zhao | Lixin Tang | Y. Y. Shuai
Source: Proceedings of the Genetic and Evolutionary Computation Conference Companion (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Exploring a Small Molecule Property Prediction Model with Optimal Comprehensive Performance through Multi-Objective Optimization Algorithms The evolution of artificial intelligence has given rise to numerous machine learning (ML) models for predicting the structural properties of materials, expediting the process of new material development. However, many of these models have parameters set based on empirical knowledge, lacking validation for optimality or multi-objective fitness. In this study, we employ a fusion modeling approach that integrates physical mechanisms with ML to construct a neural network model for predicting multiple properties of small molecules. We utilize the non-dominated sorting differential evolution (NSDE) algorithm to explore the dimensions of the network input data and the structure of the network. The goal is to identify feasible solutions that simultaneously optimize the predictive performance of the model and the training time cost. Experimental results show that the trained model determined by multi-objective optimization (MOP) not only exhibits outstanding predictive capabilities for multiple properties of small molecules but also achieves a balance between predictive performance and training time cost, showcasing excellent comprehensive performance. Property (philosophy) ; Computer science ; Algorithm ; Optimization algorithm ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ao Zhang
Source: Transactions on Computer Science and Intelligent Systems Research (article, 2024-11-25)
Field: Materials Science — Polymers and Plastics

Design and Performance Optimization of Small Molecule Organic Photovoltaic Materials In this article, researchers have investigated the design and capability optimisation of SMPS with a particular emphasis on increasing the efficiency of power transfer and developing new manufacturing techniques. Nowadays, the PV industry mostly depends on silicon base technique, but it has many advantages such as high cost, low flexibility and great environment impact. By comparison, the structure of small molecular materials is easily duplicated and has a promising future in the field of PV devices. In this paper, this paper present a discussion on how to increase energy conversion efficiency by using SMPS, as well as the high efficiency of all SMPS systems. In addition, this paper also proposes methods and theoretical frameworks for optimizing material structures using experimental design and machine learning. Not only do they contribute to the improvement of material properties, but they also provide significant theory and practice support for developing highly efficient PV devices. Flexibility (engineering) ; Photovoltaic system ; Computer science ; Efficient energy use ; Field (mathematics) ; Energy conversion efficiency ; Systems engineering ; Process engineering ; Materials science ; Engineering ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: James Durrant
Source: Cronfa (Swansea University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem Over the last two decades the organic solar cell community has synthesized tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material.These materials are often crudely evaluated simply by measuring the current-voltage (JV) curves in the light to obtain power conversion efficiencies (PCEs).Materials with low PCEs are quickly disregarded in the search for higher efficiencies.More complex measurements such as frequency/time domain characterization that could explain why the material performed as it is often not performed as they are too time consuming/complex.This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress.Herein, a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone.This technique reduces the time to fully analyze a working cell from weeks to seconds and opens up the possibility of not only fully characterizing new devices as they are fabricated, but also data mining historical data sets for promising materials the community has overlooked. Haystack ; Throughput ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Patrick J. Strohbeen | Akram Abbaspour | Amara Keita | Tarek Nabih | Aliona Lejuste | Alisa Danilenko | Ido Levy | Jacob Issokson | T. E. Cowan | William M. Strickland | Mehdi Hatefipour | Ashley Argueta | Lukas Baker | Melissa Mikalsen | Javad Shabani
Source: arXiv (Cornell University) (preprint, 2024-09-25)
Field: Materials Science — Materials Chemistry

Machine learning analysis of structural data to predict electronic properties in near-surface InAs quantum wells Semiconductor crosshatch patterns in thin film heterostructures form as a result of strain relaxation processes and dislocation pile-ups during growth of lattice mismatched materials. Due to their connection with the internal misfit dislocation network, these crosshatch patterns are a complex fingerprint of internal strain relaxation and growth anisotropy. Therefore, this mesoscopic fingerprint not only describes the residual strain state of a near-surface quantum well, but also could provide an indicator of the quality of electron transport through the material. Here, we present a method utilizing computer vision and machine learning to analyze AFM crosshatch patterns that exhibits this correlation. Our analysis reveals optimized electron transport for moderate values of $λ$ (crosshatch wavelength) and $ε$ (crosshatch height), roughly 1 $μ$m and 4 nm, respectively, that define the average waveform of the pattern. Simulated 2D AFM crosshatch patterns are used to train a machine learning model to correlate the crosshatch patterns to dislocation density. Furthermore, this model is used to evaluate the experimental AFM images and predict a dislocation density based on the crosshatch waveform. Predicted dislocation density, experimental AFM crosshatch data, and experimental transport characterization are used to train a final model to predict 2D electron gas mean free path. This model shows electron scattering is strongly correlated with elastic effects (e.g. dislocation scattering) below 200 nm $λ_{MFP}$. Surface (topology) ; Quantum well ; Quantum ; Materials science ; Physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: Victoria Castro Riglos | Beatriz Amaya Dolores | Ashwin Ramasubramaniam | Lorena González‐Souto | Rafáel Sánchez | F.J. Botana | J. Almagro | José J. Calvino | Luc Lajaunie
Source: Materials Characterization (article, 2024-04-23)
Field: Materials Science — Metals and Alloys

Combining low-loss EELS experiments with machine learning-based algorithms to automate the phases separation imaging in industrial duplex stainless steels At present, transmission electron microscopy is regarded as the main option when dealing with phase characterization for materials at a nanometric scale. The development and improvement of complementary techniques such as energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), imaging detectors and associated computational methods provide a huge variety of choices to determine the composition and crystal structure at any region of a specimen. Despite all these advancements, phase identification for some specific materials having phases with similar structure or similar composition might still be a difficult procedure to perform. To overcome this difficulty, a new method is proposed which combines the acquisition of low-loss EELS spectra with a subsequent analysis driven by machine learning based algorithms (clustering). In the present research, this new approach is applied to a set of industrial duplex stainless steels having different compositions and it is contrasted to other characterization alternatives. As opposed to the other options, this method proved to be effective, reliable and time-saving. • Innovative fusion of EELS and machine learning automates duplex stainless steel phase imaging efficiently and accurately. • Our method outperforms conventional approaches, offering comprehensive phase identification in duplex stainless steel. • DFT validation confirms accuracy, ensuring reliable characterization of duplex stainless steel phases. Characterization (materials science) ; Materials science ; Electron energy loss spectroscopy ; Algorithm ; Cluster analysis ; Transmission electron microscopy ; Machine learning ; Artificial intelligence ; Spectroscopy ; Duplex (building) ; Computer science ; Nanotechnology Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Sumner B. Harris | Arpan Biswas | Seok Joon Yun | Kevin M. Roccapriore | Christopher M. Rouleau | Alexander A. Puretzky | Rama K. Vasudevan | David B. Geohegan | Kai Xiao
Source: Small Methods (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation (Small Methods 9/2024) Front Cover In article number 2301763, Harris, Xiao, and co-workers demonstrate autonomous synthesis of thin film materials using pulsed laser deposition. Through a combination of in situ diagnostics, a high-throughput methodology, automation, and machine learning, they efficiently explore a four-dimensional parameter space 10x faster than traditional workflows to discover two distinct growth windows for WSe2, enabling the accelerated discovery and automated optimization of novel materials. In situ ; Automation ; Materials science ; Thin film ; Pulsed laser deposition ; Deposition (geology) ; Spectroscopy ; Nanotechnology ; Laser ; Chemical engineering ; Engineering ; Optics ; Chemistry ; Physics ; Mechanical engineering ; Geology ; Organic chemistry Catalytic Processes in Materials Science
Authors: Tianbai Huang | Robert Geitner | Alexander Croy | Stefanie Gräfe
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Tailoring phosphine ligands for improved C–H activation: insights from Δ-machine learning Δ-Machine Learning approach for predicting the reaction energy at the DFT level of theory, based on the results obtained at the xTB-GFN2 level of theory. Features derived from atomic positions and electronic density were utilized to train the models. Phosphine ; Chemistry ; Computer science ; Biochemistry ; Catalysis Catalytic C–H Functionalization Methods ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Marc Lehner | Paul Katzberger | Niels Maeder | Gregory A. Landrum | Sereina Riniker
Source: ChemRxiv (preprint, 2024-06-11)
Field: Materials Science — Materials Chemistry

DASH Properties: Estimating Atomic and Molecular Properties from a Dynamic Attention-Based Substructure Hierarchy Recently, we presented a method to assign atomic partial charges based on the DASH tree (dynamic attention-based substructure hierarchy) with high efficiency and quantum mechanical (QM) like accuracy. Additionally, the approach can be considered “rule based” – where the rules are derived from the attention values of a graph neural network – and thus, each assignment is fully explainable by visualizing the underlying molecular substructures. In this work, we demonstrate that these hierarchically sorted substructures capture the key features of the local environment of an atom and allow us to predict different atomic properties with high accuracy without building a new DASH tree for each property. The fast prediction of atomic properties in molecules with the DASH tree can for example be used as an efficient way to generate feature vectors for machine learning without the need for expensive QM calculations. Substructure ; Dash ; Computer science ; Hierarchy ; Key (lock) ; Tree (set theory) ; Graph ; Property (philosophy) ; Theoretical computer science ; Computation ; Quantum ; Artificial intelligence ; Data mining ; Topology (electrical circuits) ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Carbon dioxide utilization in catalysis
Authors: Mingyue Xiang | Jiaxin Shi
Source:  (article, 2024-09-20)
Field: Chemistry — Analytical Chemistry

Lightweight Model for Rice Crop Disease Detection Rice is a staple crop for more than half of the world's population, making its health and yield crucial for global food security. Early detection of rice crop diseases can significantly mitigate yield losses and reduce the use of chemical treatments. In this paper, we propose a lightweight deep learning model designed for the efficient detection of rice crop diseases. The model leverages convolutional neural networks (CNNs) optimized for mobile and edge devices, ensuring that farmers in remote or resource-constrained areas can benefit from real-time disease detection. We demonstrate that our model achieves competitive accuracy with significantly reduced computational complexity compared to traditional models, making it an ideal solution for on- field deployment. Crop ; Computer science ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI