jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Blais | Chao Xu | Richard H. West
Source: ChemRxiv (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Linear Scaling, Machine Learning, and DFT Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation (BPE) for thermodynamic parameters calculated by DFT, LSRs, and GEMNET, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane on Rhodium was chosen as a case study, in which the model’s thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GEMNET. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior. Rhodium ; Scaling ; Catalysis ; Methane ; Thermodynamics ; Partial oxidation ; Chemistry ; Physics ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Christopher Blais | Chao Xu | Richard H. West
Source: ChemRxiv (preprint, 2024-07-16)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Linear Scaling, Machine Learning, and DFT Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity. However, given the high cost of DFT calculations for adsorbates in heterogeneous catalysis, linear scaling relations (LSRs) and machine learning (ML) models were developed to give rapid estimates of the parameters in MKM. Regardless of the method, few studies have attempted to quantify the uncertainty in catalytic MKMs, as the uncertainties are often orders of magnitude larger than those for gas phase models. This study explores uncertainty quantification and Bayesian Parameter Estimation (BPE) for thermodynamic parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed under the Open Catalyst Project. A model for catalytic partial oxidation of methane (CPOX) on Rhodium was chosen as a case study, in which the model's thermodynamic parameters and their associated uncertainties were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte Carlo coupled with Ensemble Slice Sampling was used to sample the highest probability density (HPD) region of the posterior and determine the maximum of the a posteriori (MAP) for each thermodynamic parameter included. The optimized microkinetic models for each of the three estimation methods had quite similar mechanisms and agreed well with the experimental data for gas phase mole fractions. Exploration of the HPD region of the posterior further revealed that adsorbed hydroxide and oxygen likely bind on facets other than Rhodium 111. The demonstrated workflow addresses the issue of inaccuracies arising from the integration of data from multiple sources by considering both experimental and computational uncertainties, and further reveals information about the active site that would not have been discovered without considering the posterior. Rhodium ; Methane ; Scaling ; Catalysis ; Thermodynamics ; Partial oxidation ; Linear scale ; Chemistry ; Physical chemistry ; Physics ; Mathematics ; Organic chemistry ; Geology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Daniel M. Kaplan | A. M. Zhang | Joanna Bławat | Rongying Jin | R. J. Cava | V. S. Oudovenko | Gabriel Kotliar | Anirvan M. Sengupta | Weiwei Xie
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Deep Learning Based Superconductivity: Prediction and Experimental Tests The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chem-ical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound $\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$, which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions. Superconductivity ; Artificial intelligence ; Deep learning ; Condensed matter physics ; Psychology ; Computer science ; Physics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism
Authors: R. W. Lee | Yong‐Hoon Kim
Source: arXiv (Cornell University) (preprint, 2024-03-28)
Field: Materials Science — Surfaces, Coatings and Films

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution ($ρ$) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question from both practical and fundamental standpoints. Herein, a machine learning strategy DeepSCF is presented in which the map between the SCF $ρ$ and the initial guess density ($ρ_0$) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by expanding the input features to include atomic fingerprints beyond $ρ_0$ and encoding them on a 3D grid. The prediction of the residual density ($δρ$) rather than $ρ$ itself is targeted, and, since $δρ$ corresponds to chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. After enhancing the fidelity of the method by subjecting the atomic geometries in the dataset to random strains and rotations, the effectiveness of DeepSCF is finally demonstrated using a complex large carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structures can be optimally represented via the local connectivity in CNNs. Computer science ; Grid ; Self consistent ; Electron density ; Electron ; Physics ; Geography ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Eisuke F. Sato | Seiji Suga
Source: Journal of Synthetic Organic Chemistry Japan (article, 2024-09-01)
Field: Chemistry — Organic Chemistry

Synthetic Organic Electrolysis Using Catalysts In the past decades, synthetic organic electrolysis has attracted much attention as an excellent tool for organic synthesis due to its potent and irreversible electron transfer between electrodes and organic compounds. While developing new reactions using organic electrolysis, we are investigating the concept of catalyst-supported organic electrolysis. The first strategy is the generation of bromenium ions (Br+) via anodic oxidation and the oxidative transformation of indole compounds. We found that the semi-pinacol rearrangement of tetrahydrocarbolines proceeded with Br+ generated by the anodic oxidation. The anodic oxidation of tetrahydrocarbazole did not give spiro compounds under similar conditions, but instead the aromatization proceeded to afford carbazoles. This transformation required both the bromide and the lithium-ion. The second strategy is organic electrolysis promoted by the catalytic amount of electricity. The reactions that are completed by the catalytic amount of electricity can be categorized as hole- or electron-catalyzed transformations. Moreover, these reactions can be achieved using an electrochemical flow reactor, which enables efficient processes. The numerical parameters in the electrochemical flow system, such as the current values and the flow rate, were optimized using the Gaussian process regression. This machine-learning method suggested better reaction conditions with a small number of experiments. Chemistry ; Electrolysis ; Catalysis ; Combinatorial chemistry ; Organic chemistry ; Electrode ; Physical chemistry ; Electrolyte Radical Photochemical Reactions ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Christopher Broyles | W.D. Charles | Sheng Ran
Source: arXiv (Cornell University) (preprint, 2024-06-26)
Field: Chemistry — Inorganic Chemistry

Structure-Driven Prediction of Magnetic Order in Uranium Compounds The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and validation, which are not always effective for uranium-based materials due to their strong electron correlations. This study presents a computationally inexpensive machine learning approach, specifically a random forest classifier, to predict the magnetic ground states of uranium compounds using only structural inputs. Our model, trained on a curated dataset of experimentally-verified magnetic orders, achieves a mean accuracy of 60.2%, significantly outperforming random chance. By excluding computationally intensive DFT calculations, our method offers a faster and reliable alternative for discovering new materials with desirable magnetic properties, addressing the challenges posed by strong correlations in quantum materials. Uranium ; Order (exchange) ; Nuclear engineering ; Physics ; Nuclear physics ; Engineering ; Business Radioactive element chemistry and processing ; Rare-earth and actinide compounds ; Nuclear physics research studies
Authors: Debjyoti Bhattacharya | Harrison J. Cassady | Michael A. Hickner | Wesley F. Reinhart
Source: ChemRxiv (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Large Language Models as Molecular Design Engines The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although generative machine learning methods have recently shown potential for computational molecular design, their use is hindered by complex training procedures, and they often fail to generate valid and unique molecules. In this context, pre-trained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts. In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with an impressive 97% valid and unique molecules. By quantifying these modifications in a low-dimensional latent space, we systematically evaluate the model's behavior under different prompting conditions. Notably, the model is able to perform guided molecular generation when asked to manipulate the electronic structure of molecules using simple, natural-language prompts. Our findings highlight the potential of LLMs as powerful and versatile molecular design engines. Computer science ; Chemical space ; Context (archaeology) ; Generative grammar ; Simple (philosophy) ; Space (punctuation) ; Human–computer interaction ; Natural language ; Generative model ; Data science ; Artificial intelligence ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ryosuke Jinnouchi
Source: arXiv (Cornell University) (preprint, 2024-09-28)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning surrogate models for particle insertions and element substitutions Two machine learning-aided thermodynamic integration schemes to compute the chemical potentials of atoms and molecules have been developed and compared. One is the particle insertion method, and the other combines particle insertion with element substitution. In the former method, the species is gradually inserted into the liquid, and its chemical potential is computed. In the latter method, after the particle insertion, the inserted species is substituted with another species, and the chemical potential of this new species is computed. In both methods, the thermodynamic integrations are conducted using machine-learned potentials trained on first-principles datasets. The errors of the machine-learned surrogate models are further corrected by performing thermodynamic integrations from the machine-learned potentials to the first-principles potentials, accurately providing the first-principles chemical potentials. These two methods are applied to compute the real potentials of proton, alkali metal cations, and halide anions in water. The applications indicate that these two entirely different thermodynamic pathways yield identical real potentials within statistical error bars, demonstrating that both methods provide reproducible real potentials. The computed real potentials and solvation structures are also in good agreement with past experiments and simulations. These results indicate that machine learning surrogate models enabling the atomic insertion and element substitution provide a precise method for determining the chemical potentials of atoms and molecules. Element (criminal law) ; Particle (ecology) ; Computer science ; Artificial intelligence ; Biology ; Political science ; Law Electron and X-Ray Spectroscopy Techniques
Authors: Rakesh Kumar | B. Shakila | M. Prakash
Source:  (article, 2024-11-21)
Field: Chemistry — Analytical Chemistry

Battery State of Health Prognostic based on Neural Hierarchical Interpolation The precise State of Health (SOH) estimation of Lithium-ion Batteries (LIB) is critical for analyzing the dependability and endurance of battery-powered systems. SOH estimate using data driven techniques has gained popularity in recent years. The approach envisions the possibility to harness battery information and also supports in cloud-based battery management techniques. In this proposed work, novel data driven SOH estimation approach was carried out and performance metrics including RMSE, MAPE, and coefficient of determination (R-square) were calculated, to evaluate the efficacy of the proposed approach. To build and validate the model, experimental data of LIB were taken from NASA website. The performance metrics of the proposed model is compared against similar deep learning and machine learning models to analyze the improvisation of accuracy. The proposed Linear-NHI model achieves the lowest error inclusive of RMSE as 0.004, MAPE as $\mathbf{0. 4 9 \%}$ and attains highest coefficient of determination as $\mathbf{9 5 \%}$ for $\mathbf{B 0 0 0 5}$ model. For B0018 model, the performance metrics are similarly evaluated: RMSE, MAPE and $R^{2}$ are obtained as $\mathbf{0. 0 0 6 5}, \mathbf{0. 7 5 5}$ and $\mathbf{7 7. 6 \%}$ respectively thereby securing improved accuracy and reliability in SOH estimation. The findings emphasise the potential of advanced modelling techniques and is helpful to improve the operational efficiency and maintenance of Lithium-ion battery. Interpolation (computer graphics) ; Computer science ; Battery (electricity) ; State (computer science) ; Artificial neural network ; State of health ; Artificial intelligence ; Machine learning ; Algorithm Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Data Processing Techniques
Authors: Ateeb Naseer | Yawar Hayat Zarkob | Musaib Rafiq | Mohammad Sajid Nazir | Owais Ahmad | Amit Agarwal | Somnath Bhowmick | Yogesh Singh Chauhan
Source:  (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Enhancing the Capabilities of Quantum Transport Simulations Utilizing Machine Learning Strategies The ongoing pursuit of exploring novel materials for potential future device applications continues to strengthen the vital role of Technology Computer-Aided Design (TCAD) simulations within the device community. However, its computationally intensive and time-consuming nature necessitates novel methodologies to overcome the limitations. Machine learning (ML) is a potential remedy in our contemporary data-driven society. In this work, we present a unique approach based on Neural Networks (NNs) to generate an efficient potential profile guess. The predicted ML potential enhances the convergence of the coupled Schrodinger and Poisson equation and speeds up the simulation process, ~ 2.5x, by reducing the number of self-consistent iterations at each bias point. Moreover, our method demonstrates versatility by providing reasonable prediction capacity and accuracy for grid reduction, doping, and channel material variations. Computer science ; Quantum ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Quantum and electron transport phenomena
Authors: Ateeb Naseer | Yawar Hayat Zarkob | Musaib Rafiq | Mohammad Sajid Nazir | Owais Ahmad | Amit Agarwal | Somnath Bhowmick | Yogesh Singh Chauhan
Source:  (article, 2024-09-09)
Field: Materials Science — Materials Chemistry

Enhancing the Capabilities of Quantum Transport Simulations Utilizing Machine Learning Strategies The on-going pursuit of exploring novel materials for potential future device applications continues to strengthen the vital role of Technology Computer-Aided Design (TCAD) simulations within the device community. However, its computationally intensive and time-consuming nature necessitates novel methodologies to overcome the limitations. Machine learning (ML) is a potential remedy in our contemporary data-driven society. In this work, we present a unique approach based on Neural Networks (NNs) to generate an efficient potential profile guess. The predicted ML potential enhances the convergence of the coupled Schrodinger and Poisson equation and speeds up the simulation process, $\sim$ 2.5 x, by reducing the number of self-consistent iterations at each bias point. Moreover, our method demonstrates versatility by providing reasonable prediction capacity and accuracy for grid reduction, doping, and channel material variations. Computer science ; Quantum ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Luozhijie Jin | Zijian Du | Le Shu | Yongfeng Mei | Hao Zhang
Source: ArXiv.org (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties In this work, we propose a novel approach to generate universal atomic embeddings, significantly enhancing the representational and accuracy aspects of atomic embeddings, which ultimately improves the accuracy of property prediction. Moreover, we demonstrate the excellent transferability of universal atomic embeddings across different databases and various property tasks. Our approach centers on developing the CrystalTransformer model. Unlike traditional methods, this model does not possess a fundamental graph network architecture but utilizes the Transformer architecture to extract latent atomic features. This allows the CrystalTransformer to mitigate the inherent topological information bias of graph neural networks while maximally preserving the atomic chemical information, making it more accurate in encoding complex atomic features and thereby offering a deeper understanding of the atoms in materials. In our research, we highlight the advantages of CrystalTransformer in generating universal atomic embeddings through comparisons with current mainstream graph neural network models. Furthermore, we validate the effectiveness of universal atomic embeddings in enhancing the accuracy of model predictions for properties and demonstrate their transferability across different databases and property tasks through various experiments. As another key aspect of our study, we discover the strong physical interpretability implied in universal atomic embeddings through clustering and correlation analysis, indicating the immense potential of our universal atomic embeddings as atomic fingerprints. Interpretability ; Transferability ; Computer science ; Embedding ; Theoretical computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography