jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. G. Filippov | Ya. A. Mikhailov | Andrey Elyshev
Source: Kinetics and Catalysis (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Big Data Analysis in the Field of Catalysis (A Review) Recently, the rapid development of experimental methods in the field of catalytic research allows for large amounts of data to be obtained. The use of new statistical and computational processing methods, including the extraction of information from experimental data and their unbiased interpretation, is important for accelerating the development and implementation of catalytic technologies. Necessary information can be extracted using statistical approaches such as PCA, MCR, and ALS. At the same time, machine learning algorithms are beginning to be actively used to interpret and build descriptive models. This paper discusses the main methods of machine learning and examples of their successful application to the analysis of infrared and X-ray absorption spectroscopic data. Field (mathematics) ; Chemistry ; Big data ; Statistical analysis ; Machine learning ; Artificial intelligence ; Experimental data ; Catalysis ; Interpretation (philosophy) ; Data processing ; Computer science ; Data science ; Data mining ; Database ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Valentin Stanev | Ryota Maehashi | Yoshimi Ohta | Ichiro Takeuchi
Source: Artificial Intelligence Chemistry (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Machine learning modeling of the absorption properties of azobenzene molecules We present a machine learning framework for modeling the absorption properties of azobenzene molecules – an important class of organic compounds with many potential photochemical applications. The framework utilizes predictors based on the chemical composition and structure of each molecule and consists of separate regression models trained to predict the absorption at distinct wavelengths, covering the UV and visible light ranges. Despite the relatively small size of the dataset (330 molecule-absorption spectrum pairs), the models were able to learn to accurately predict the absorption at fixed wavelengths, as well as the position and intensity of the maximum absorption. These predictions can be used to rapidly screen thousands of candidate molecules for a variety of potential applications, reducing the need for time-consuming and expensive experiments or first-principles computations. Absorption (acoustics) ; Azobenzene ; Molecule ; Wavelength ; Biological system ; Absorption spectroscopy ; Materials science ; Computer science ; Chemistry ; Optics ; Physics ; Optoelectronics ; Organic chemistry ; Composite material Photochromic and Fluorescence Chemistry ; Conducting polymers and applications ; Machine Learning in Materials Science
Authors: Naohiro Fujinuma | S. E. Lofland
Source: Advanced Intelligent Systems (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Physics‐Based Human‐in‐the‐Loop Machine Learning Combined with Genetic Algorithm Search for Multicriteria Optimization: Electrochemical CO<sub>2</sub> Reduction Reaction Machine learning (ML) can be a powerful tool to expedite materials research, but the deployment for experimental research is often hindered by data scarcity and model uncertainty. An human‐in‐the‐loop procedure to tailor the implementation of ML for multicriteria optimization is described. The effectiveness of this procedure in the development of a nafion‐based membrane electrode assembly for electrochemical CO 2 reduction reaction (CO 2 RR) into CO for two targets is demonstrated: energy efficiency (EE) and partial current density for CO 2 RR (). Model‐agnostic nonlinear correlation analyses identify the 11 features relevant to those targets. The three studied decision tree‐based ML models yield similar cross‐validation errors so an ad hoc feature analysis of the models is done with SHapley Additive exPlanations and nonlinear correlation techniques. The predicted EE‐ space and the functional dependency of the predictions are investigated to assess model plausibility. A genetic algorithm with CO production cost as the final target with subsequent validation experiments of candidate conditions is devised. The model chosen through ad hoc analysis yields the highest accuracy and the only one that can locate the Pareto front with a single round of experiments, demonstrating how appropriate model selection through careful inspection can greatly accelerate the research cycle. Reduction (mathematics) ; Multi-objective optimization ; Computer science ; Nonlinear system ; Genetic algorithm ; Pareto principle ; Machine learning ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Fuel Cells and Related Materials
Authors: Kazi Zihan Hossain | Sharif Amit Kamran | Alireza Tavakkoli | M. Rashed Khan
Source: Journal of Physics Materials (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium or Galinstan are high-surface tension fluids coated with a ∼5 nm gallium oxide (Ga<sub>2</sub>O<sub>3</sub>) film and falls under this fluid classification, also known as liquid metals (LMs). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning (ML) technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from ML and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems. Materials science ; Surface tension ; Elastic modulus ; Oscillation (cell signaling) ; Modulus ; Oxide ; Composite material ; Metal ; Liquid metal ; Tension (geology) ; Metallurgy ; Thermodynamics ; Chemistry ; Ultimate tensile strength ; Physics Machine Learning in Materials Science ; Surface Roughness and Optical Measurements ; Ion-surface interactions and analysis
Authors: Swagata Roy | Johannes P. Dürholt | Thomas S. Asche | Federico Zipoli | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-07-04)
Field: Materials Science — Materials Chemistry

Learning a reactive potential for silica-water through uncertainty attribution The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the $ω$-B97XD def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism. Silicate ; Reactivity (psychology) ; Robustness (evolution) ; Molecular dynamics ; Nanoporous ; Aqueous solution ; Chemical physics ; Catalysis ; Phase (matter) ; Materials science ; Chemistry ; Chemical engineering ; Computational chemistry ; Nanotechnology ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Qing Zhang | K. J. Zhu | Yuhong Luo | Zhengyu Bai | Zisheng Zhang | Jingde Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Guided Prediction of Cu-Based Electrocatalysts Towards Ethylene Production in Co2 Reduction Reduction (mathematics) ; Ethylene ; Production (economics) ; Materials science ; Computer science ; Chemistry ; Catalysis ; Economics ; Mathematics ; Organic chemistry ; Microeconomics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Michail Minotakis | Hugo Rossignol | Matteo Cobelli | Stefano Sanvito
Source: Physical Review Materials (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Machine-learning surrogate model for accelerating the search of stable ternary alloys The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semilocal functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbor-analysis potentials (SNAPs) over readily available binary phases, and we establish whether this is good enough to predict the energies of novel ternaries. Such a strategy does not require any new calculations specific for the construction of the model, but just avails of data stored in binary-phase-diagram repositories. We find that a so-constructed SNAP is capable of accurate total-energy estimates for ternary phases close to the equilibrium geometry but, in general, is not able to perform atomic relaxation. This is because during a typical relaxation path, a given phase traverses regions in the parameter space poorly represented by the training set. Different metrics are then investigated to assess how well an unknown structure is described by a given SNAP model, and we find that the standard deviation of an ensemble of SNAPs provides a fast and non-specie-specific metric. Overall, we show that it is possible to train machine-learning interatomic potentials on readily available binary-compound data to effectively screen ternary compounds in a high-throughput search. Ternary operation ; Binary number ; Metric (unit) ; Relaxation (psychology) ; Set (abstract data type) ; Computer science ; Phase diagram ; Convex hull ; Statistical physics ; Algorithm ; Binary data ; Phase (matter) ; Regular polygon ; Physics ; Mathematics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Dong Hyeon Mok | Daeun Chloe Shin | Jonggeol Na | Seoin Back
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A chemically inspired convolutional neural network using electronic structure representation We developed Electronic Structure Network (ESNet) to predict formation energies using density of states extracted from initial structures. ESNet outperformed previously reported models that used other input features and architectures. Convolutional neural network ; Representation (politics) ; Electronic structure ; Computer science ; Artificial intelligence ; Artificial neural network ; Network structure ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jong Youl Choi | Massimiliano Lupo Pasini | Pei Zhang | Kshitij Mehta | Frank Liu | Jonghyun Bae | Khaled Z. Ibrahim
Source:  (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale. Scalability ; Computer science ; Artificial neural network ; Class (philosophy) ; Graph ; Node (physics) ; Distributed computing ; Scale (ratio) ; Training set ; Artificial intelligence ; Parallel computing ; Machine learning ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Jiawei Guo | Tyler Sours | Sam Holton | Chenghan Sun | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2023-10-08)
Field: Materials Science — Materials Chemistry

Screening Cu-Zeolites for Methane Activation using Curriculum-based Training Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a new philosophy called curriculum-based training (CBT) to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferrable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step towards overcoming the long- standing barriers within the computational heterogeneous catalysis community. Methane ; Catalysis ; Zeolite ; Computer science ; Curriculum ; Anaerobic oxidation of methane ; Chemistry ; Work (physics) ; Biochemical engineering ; Machine learning ; Physics ; Engineering ; Organic chemistry ; Thermodynamics ; Psychology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yeongrok Jin | Jaekwang Lee
Source: Journal of the Korean Physical Society (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Machine learning for prediction of atom-projected properties Interpretability ; Atom (system on chip) ; Artificial neural network ; Materials science ; Material properties ; Computer science ; Modulus ; Machine learning ; Property (philosophy) ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Mario Lovrić | Tomislav Đuričić | Hussain Hussain | Bono Lučić | Roman Kern
Source: ChemRxiv (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

PyChemFlow: an automated pre-processing pipeline in Python for reproducible machine learning on chemical data PyChemFlow is a Python library for automated and reproducible data pre-processing. Based on open-source code, PyChemFlow has simple requirements that rely on pandas, scikit-learn and joblib. The library's backbone is built up of transformer objects, which are fully constructed during the PyChemFlow fitting process using training data and can be conveniently stored using joblib. The user can run the library with a one-line command after splitting data into train and validation sets or while working with additional data. This is especially useful when reproducibility is critical. PyChemFlow also offers the ability to persistently store metadata, in addition to providing customizable and configurable data manipulation steps. Python (programming language) ; Computer science ; Metadata ; Data processing ; Transformer ; Pipeline (software) ; Open source ; Programming language ; Operating system ; Data mining ; Software engineering ; Database ; Software ; Engineering Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Andreas Leitherer | Byung Chul Yeo | Christian H. Liebscher | Luca M. Ghiringhelli
Source: arXiv (Cornell University) (preprint, 2023-03-22)
Field: Materials Science — Surfaces, Coatings and Films

Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy (STEM) images of polycrystalline materials. The method is based on a Bayesian convolutional neural network (BNN) that is trained only on simulated images. AI-STEM automatically and accurately identifies crystal structure, lattice orientation, and location of interface regions in synthetic and experimental images. The model is trained on cubic and hexagonal crystal structures, yielding classifications and uncertainty estimates, while no explicit information on structural patterns at the interfaces is included during training. This work combines principles from probabilistic modeling, deep learning, and information theory, enabling automatic analysis of experimental, atomic-resolution images. Artificial intelligence ; Convolutional neural network ; Computer science ; Scanning transmission electron microscopy ; Resolution (logic) ; Artificial neural network ; Crystallite ; Materials science ; Pattern recognition (psychology) ; Machine learning ; Transmission electron microscopy ; Nanotechnology ; Crystallography ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Jiansheng Pan | Huan Liu | Wendong Zhu | Shunbo Wang | Xifeng Gao | Pengyue Zhao
Source: Crystals (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Predicting Mechanical Properties of Boron Nitride Nanosheets Obtained from Molecular Dynamics Simulation: A Machine Learning Method Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young’s modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs’ chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young’s modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors. Boron nitride ; Molecular dynamics ; Ultimate tensile strength ; Materials science ; Modulus ; Work (physics) ; Nanomaterials ; Computer science ; Composite material ; Biological system ; Nanotechnology ; Mechanical engineering ; Computational chemistry ; Chemistry ; Engineering Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials