jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khalid Zobaid Adnan | Neupane, Mahesh R. | Feng, Tianli
Source: arXiv (Cornell University) (preprint, 2024-04-23)
Field: Materials Science — Materials Chemistry

Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials Thermal boundary conductance (TBC) across metal diamond interfaces plays a critical role in the thermal management of future diamond based ultrawide bandgap semiconductor devices. Molecular dynamics is a sophisticated method to predict TBC but is limited by the lack of reliable potential describing metal diamond interfaces. In this work, we report the development of machine learning interatomic potentials and the prediction of TBCs of several technologically promising metal diamond interfaces using nonequilibrium molecular dynamics. The predicted TBCs of Al, Zr, Mo, and Au-diamond interfaces are approximately 316, 88, 52, and 55 MW/m2K, respectively, after quantum corrections. The corresponding thermal boundary resistances are equivalent to 0.8 μm thick of Al, 1.4 μm Mo, 0.3 μm Zr, and 5.3 μm Au, respectively. We also find that the conventional simple models, such as the acoustic mismatch model and diffuse mismatch model, even including the full-band phonon dispersion from first principles, largely misestimate the TBC values because of their inability to include inelastic transmission as well as interfacial structural and bonding details. The quantum-corrected TBC values for the metal diamond interfaces correlate well with the quantum corrected phonon specific heat of metals, instead of diamond. Additionally, our comparative analysis of Debye temperature and elastic modulus in these systems reveals that the former parameter correlates more strongly with the TBC than the latter. These low TBC values need to be considered in future diamond based semiconductor devices. Diamond ; Boundary (topology) ; Materials science ; Metal ; Conductance ; Thermal ; Molecular dynamics ; Thermal conductivity ; Dynamics (music) ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Composite material ; Metallurgy ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Mathematical analysis Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides
Authors: Yusuke Nomura | Masatoshi Imada
Source: arXiv (Cornell University) (preprint, 2024-10-03)
Field: Materials Science — Materials Chemistry

Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation. Solver ; Artificial neural network ; Quantum ; Electron ; Computer science ; Statistical physics ; Neural system ; Physics ; Artificial intelligence ; Quantum mechanics ; Neuroscience ; Psychology Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Sâmela Beutinger Cavalheiro | Dthenifer Cordeiro Santana | Marcelo Carvalho Minhoto Teixeira Filho | Izabela Cristina de Oliveira | Rita de Cássia Félix Alvarez | João Lucas Della-Silva | Fábio Henrique Rojo Baio | Ricardo Gava | Larissa Pereira Ribeiro Teodoro | Carlos Antônio da Silva | Paulo Eduardo Teodoro
Source: AgriEngineering (article, 2024-11-28)
Field: Chemistry — Analytical Chemistry

Multispectral Information in the Classification of Soybean Genotypes Using Algorithms Regarding Micronutrient Nutritional Contents Identifying machine learning models that are capable of classifying soybean genotypes according to micronutrient content using only spectral data as input is relevant and useful for plant breeding programs and agricultural producers. Therefore, our objective was to classify soybean genotypes according to leaf micronutrient levels using multispectral images. In the 2019/20 crop year, a field experiment was carried out with 103 F2 soybean populations in the experimental area of the Federal University of Mato Grosso do Sul, in Chapadão do Sul, Brazil. The data were subjected to machine learning analysis using algorithms to classify genotypes according to leaf micronutrient content. The spectral data were divided into three distinct input groups to be tested in the machine learning models: spectral bands (SBs), vegetation indices (VIs), and combining VIs and SBs. The algorithms tested were: J48 Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), Perceptron Multilayer Neural Network (ANN), Logistic Regression (LR), and REPTree (DT). All model parameters were set as the default settings in Weka 3.8.5 software. The Random Forest (RF) algorithm outperformed (&gt;90 for CC and &gt;0.9 for Kappa and Fscore) regardless of the input used, demonstrating that it is a robust model with good data generalization capacity. The DT and J48 algorithms performed well when using VIs or VIs+SBs inputs. The SVM algorithm performed well with VIs+SBs as input. Overall, inputs containing information about VIs provided better results for the classification of soybean genotypes. Finally, when deciding which data should serve as input in scenarios of spectral bands, vegetation indices or the combination (VIs+SBs), we suggest that the ease and speed of obtaining information are decisive, and, therefore, a better condition is achieved with band-only inputs. This allows for the identification of genetic materials that use micronutrients more efficiently and the adaptation of management practices. In addition, the decision to be made can be made quickly, without the need for chemical evaluation in the laboratory. Micronutrient ; Multispectral image ; Artificial intelligence ; Computer science ; Algorithm ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Haifeng Ling | Xiaogang Cheng | Xiaolong Liu | Zhaocheng Wang | Yichuan Xu | Mengya Guo | Xun Fu | Yongxia Wang | Xin Chen | Haiping Hao | Ying Wei
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

面向AI化学家的基于视觉的薄层色谱解析算法 Computer science Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Machine Learning in Materials Science
Authors: Rikesh Patel | Wenqi Li | Richard J. Smith | Matt Clark
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Developing Neural Networks to Rapidly Map Crystallographic Orientation Using Laser Ultrasound Measurements Orientation (vector space) ; Artificial neural network ; Materials science ; Laser ; Ultrasound ; Crystallography ; Artificial intelligence ; Computer science ; Optics ; Geometry ; Chemistry ; Acoustics ; Physics ; Mathematics Crystallization and Solubility Studies ; Mineral Processing and Grinding ; Material Properties and Failure Mechanisms
Authors: H.-Q. Jin | Kenneth M. Merz
Source: arXiv (Cornell University) (preprint, 2024-11-26)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated structure. In the gas phase, the site of protonation is determined using proton affinity (PA) measurements. Currently, mass spectrometry and ab initio computation methods are widely used to evaluate PA; however, both methods are resource-intensive and time-consuming. Therefore, there is a critical need for efficient methods to estimate PA, enabling the rapid identification of the most favorable protonation site in complex organic molecules with multiple proton binding sites. In this work, we developed a fast and accurate method for PA prediction by using multiple descriptors in combination with machine learning (ML) models. Using a comprehensive set of 186 descriptors, our model demonstrated strong predictive performance, with an R2 of 0.96 and a MAE of 2.47kcal/mol, comparable to experimental uncertainty. Furthermore, we designed quantum circuits as feature encoders for a classical neural network. To evaluate the effectiveness of this hybrid quantum-classical model, we compared its performance with traditional ML models using a reduced feature set derived from the full set. The result showed that this hybrid model achieved consistent performance comparable to traditional ML models with the same reduced feature set on both a noiseless simulator and real quantum hardware, highlighting the potential of quantum machine learning for accurate and efficient PA predictions. Electronic circuit ; Proton ; Quantum ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Electrical engineering ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Electron and X-Ray Spectroscopy Techniques
Authors: Anmol Jindal | Tarak Karmakar
Source: ChemRxiv (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Unveiling the Role of Solvent in Solution Phase Chemical Reactions Using Deep Potentials-based Enhanced Sampling Simulations We have used the deep learning-based active learning strategy to develop ab initio accurate machine-learned (ML) potential for a solution phase reactive system. This approach enables us to study the effect of solvents on chemical reactions. As a paradigmatic example, we have investigated the Menshutkin reaction, a classic bimolecular nucleophilic substitution SN2 reaction in aqueous medium. Enhanced sampling simulations using the ML potential enabled efficient sampling of multiple transitions between the reactant and the product states, allowing us to calculate the converged free energy surface. Our analysis revealed that water stabilizes the product state, facilitating the reaction and making it more spontaneous. Our approach expands the scope of studying chemical reactions in explicit solvents at finite temperatures, closely mimicking experiments. Solvent ; Phase (matter) ; Sampling (signal processing) ; Chemistry ; Computational chemistry ; Chemical physics ; Computer science ; Organic chemistry ; Computer vision Machine Learning in Materials Science
Authors: Ayirizia, Blaise Awola
Source: scholarworks - UTEP (The University of Texas at El Paso) (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Mechanical Stability Of Body-Centered Cubic Iron At Earth's Core Pressure And Temperature Conditions Using A Molecular Graph Kernel Regression And Gaussian Process. We used machine learning to study nonmagnetic BCC iron in conditions similar to thosefound in the Earthâ??s core. Our model predicts the stability of BCC iron at high temperatures and pressures by analyzing a dataset from ab initio molecular dynamics simulations. The framework uses Gaussian process regression to make predictions by comparing the similarity between molecules. This comparison is made using the marginalized graph kernel. To use the marginalized graph kernel, the framework applies a spatial adjacency rule to convert molecules into graphs, where the vertices and edges are labeled by elements and interatomic distances, respectively. We can use this model to forecast energy landscapes, identify the forces acting on iron atoms and recognize any instability pathways. Our findings suggest that BCC iron is stable in such extreme environments. This machine learning approach helps us better understand material behavior in challenging experimental settings, which is valuable for geophysical and materials science applications. Stability (learning theory) ; Kriging ; Graph ; Gaussian ; Molecular dynamics ; Gaussian process ; Ab initio ; Molecular graph ; Interatomic potential Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Jian Ping Gong | Hongli Liao | Sheng Hu | Yang Hu | Lei Wang | Shinya Tanaka | Ichigaku Takigawa | Wei Li | Hailong Fan
Source: Research Square (preprint, 2024-11-27)
Field: Materials Science — Polymers and Plastics

Data-Driven De Novo Design of Super-Adhesive Hydrogels Self-healing hydrogels ; Adhesive ; Computer science ; Materials science ; Nanotechnology ; Polymer chemistry Synthesis and properties of polymers ; Silicone and Siloxane Chemistry
Authors: Ajay K. Singh | Abhilash Rana | Ruchi Chauhan | Amirreza Mottafegh | Dong‐Pyo Kim
Source: Research Square (preprint, 2024-07-09)
Field: Chemistry — Organic Chemistry

Programmable Light-Induced Carbene Generation for On Demand Chemical Synthesis: Introducing DigiChemTree Carbene ; On demand ; First generation ; Chemistry ; Business ; Organic chemistry ; Catalysis ; Commerce ; Sociology Cyclopropane Reaction Mechanisms ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Md Rahatul Islam Udoy | J Lee Hutchins | Catherine D. Schuman | Ahmedullah Aziz
Source:  (preprint, 2024-08-26)
Field: Materials Science — Materials Chemistry

Integrating Atomistic Insights with Circuit Simulations via Transformer-Driven Symbolic Regression This paper introduces a framework that establishes a cohesive link between first principlesbased simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, are processed using machine learning algorithms to identify complex patterns and relationships. As these machine-learning models develop, they become tools for predicting behaviors beyond the reach of conventional modeling and simulation methods. Applied to circuit simulation, the framework improves understanding of electrical interactions, enhancing accuracy and speeding up design automation. As a proof of concept, we perform first principles-based simulations of the Graphene Nanoribbon Field Effect Transistor (GNRFET), an exploratory device, and create a symbolic-regression-based machine learning model that can readily be integrated into advanced circuit simulation. This framework presents a template offering a unified approach that synergizes the strengths of first principles-based simulations and circuit-level design tools. Transformer ; Regression ; Computer science ; Symbolic regression ; Regression analysis ; Electrical engineering ; Engineering ; Machine learning ; Statistics ; Mathematics ; Voltage Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Md Rahatul Islam Udoy | J Lee Hutchins | Catherine D. Schuman | Ahmedullah Aziz
Source:  (preprint, 2024-09-20)
Field: Materials Science — Materials Chemistry

Integrating Atomistic Insights with Circuit Simulations via Transformer-Driven Symbolic Regression This paper introduces a framework that establishes a cohesive link between first principlesbased simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, are processed using machine learning algorithms to identify complex patterns and relationships. As these machine-learning models develop, they become tools for predicting behaviors beyond the reach of conventional modeling and simulation methods. Applied to circuit simulation, the framework improves understanding of electrical interactions, enhancing accuracy and speeding up design automation. As a proof of concept, we perform first principles-based simulations of the Graphene Nanoribbon Field Effect Transistor (GNRFET), an exploratory device, and create a symbolic-regression-based machine learning model that can readily be integrated into advanced circuit simulation. This framework presents a template offering a unified approach that synergizes the strengths of first principles-based simulations and circuit-level design tools. Transformer ; Regression ; Computer science ; Regression analysis ; Statistical physics ; Machine learning ; Mathematics ; Engineering ; Physics ; Electrical engineering ; Statistics ; Voltage Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Oleg Usoltsev | Andrei A. Tereshchenko | Alina Skorynina | Elizaveta Kozyr | А. В. Солдатов | Оlga V. Safonova | Adam H. Clark | Davide Ferri | Maarten Nachtegaal | Aram L. Bugaev
Source: Small Methods (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride (Small Methods 7/2024) Machine Learning In article number 2301397, Bugaev and co-workers used machine learning algorithms to extract metal-metal interatomic distances in supported palladium hydride nanoparticles from in situ infrared spectra using CO as a probe molecule. This approach can be used as an efficient, cheap, and non-destructive method to characterize the atomic structure of metal catalysts under in situ and operando conditions. Palladium ; Hydride ; Infrared ; Palladium hydride ; Infrared spectroscopy ; Materials science ; Computer science ; Chemistry ; Physics ; Optics ; Metallurgy ; Organic chemistry Machine Learning in Materials Science
Authors: Arthur Y. Lin | Kevin K. Huguenin-Dumittan | Yong-Cheol Cho | Jigyasa Nigam | Rose K. Cersonsky
Source: arXiv (Cornell University) (preprint, 2024-03-27)
Field: Materials Science — Materials Chemistry

Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem \AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay-Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights into how molecular shape influences mesoscale behavior and explain how to reincorporate important atom-atom interactions typically not captured by coarse-grained models. Moving forward, we propose \AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation. Correlation ; Anisotropy ; Computer science ; Statistical physics ; Materials science ; Physics ; Mathematics ; Optics ; Geometry Machine Learning in Materials Science
Authors: Dan Hu | Ting‐Fung Chung | Yong P. Chen | Yaping Qi
Source: arXiv (Cornell University) (preprint, 2024-01-07)
Field: Chemistry — Electrochemistry

Advancing Noise-Resilient Twist Angle Characterization in Bilayer Graphene through Raman Spectroscopy via GAN-CNN Modeling In this study, we introduce an innovative methodology for robust twist angle identification in bilayer graphene using Raman spectroscopy, featuring the integration of generative adversarial network and convolutional neural network (GAN-CNN). Our proposed approach showcases remarkable resistance to noise interference, particularly in ultra-low Signal-to-Noise Ratio (SNR) conditions. We demonstrate the GAN-CNN model's robust learning capability, even when SNR reaches minimal levels. The model's exceptional noise resilience negates the necessity for preprocessing steps, facilitating accurate classification, and substantially reducing computational expenses. Empirical results reveal the model's prowess, achieving heightened accuracy in twist angle identification. Specifically, our GAN-CNN model achieves a test accuracy exceeding 99.9% and a recall accuracy of 99.9%, relying on an augmented dataset containing 4209 spectra. This work not only contributes to the evolution of noise-resistant spectral analysis methodologies but also provides crucial insights into the application of advanced deep learning techniques for bilayer graphene characterization through Raman spectroscopy. The findings presented herein have broader implications for enhancing the precision and efficiency of material characterization methodologies, laying the foundation for future advancements in the field. Raman spectroscopy ; Characterization (materials science) ; Noise (video) ; Computer science ; Convolutional neural network ; Artificial intelligence ; Bilayer graphene ; Materials science ; Deep learning ; Graphene ; Machine learning ; Nanotechnology ; Optics ; Physics Electrochemical Analysis and Applications ; Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science
Authors: Cristian Rojas | Doménica Muñoz | Iván A. Cordero | Belén Tenesaca | Davide Ballabio
Source:  (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids Alkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Because they can be labelled as active or inactive compounds against the inflammatory biological response, the aim of this work was to calibrate quantitative structure-activity relationships (QSARs) using machine learning classifiers to predict anti-inflammatory activity based on the molecular structures of alkaloids. A dataset of 100 alkaloids (58 active and 42 inactive) was retrieved from two systematic reviews. Molecules were properly curated, and the molecular geometries of the compounds were optimized using the semi-empirical method (PM3) to calculate molecular descriptors, binary fingerprints (extended-connectivity fingerprints and path fingerprints) and MACCS (Molecular ACCess System) structural keys. Then, we calibrated the QSAR models using well-known linear and non-linear machine learning classifiers, i.e., partial least squares discriminant analysis (PLSDA), random forests (RF), adaptive boosting (AdaBoost), k-nearest neighbors (kNN), N-nearest neighbors (N3) and binned nearest neighbors (BNN). For validation purposes, the dataset was randomly split into a training set and a test set in a 70:30 ratio. When using molecular descriptors, genetic algorithms-variable subset selection (GAs-VSS) was used for supervised feature selection. During the calibration of the models, a five-fold Venetian blinds cross-validation was used to optimize the classifier parameters and to control the presence of overfitting. The performance of the models was quantified by means of the non-error rate (NER) statistical parameter. Computer science ; Chemistry ; Computational biology ; Biology Chromatography in Natural Products ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods