jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongwei Du | Jian Hui | Lanting Zhang | Hong Wang
Source: Journal of Chemical Theory and Computation (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics. Computer science ; Leverage (statistics) ; Deep learning ; Artificial intelligence ; Property (philosophy) ; Benchmark (surveying) ; Machine learning ; Field (mathematics) ; Convolutional neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xuan Zhou | Cheng Xu | Xiaolong Guo | Petru Apostol | Alexandru Vlad | René A. J. Janssen | Süleyman Er
Source: Advanced Energy Materials (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Computational and Machine Learning‐Assisted Discovery and Experimental Validation of Conjugated Sulfonamide Cathodes for Lithium‐Ion Batteries Abstract Conjugated sulfonamides (CSAs) stand out for their propitious electroactivity and notable stability in ambient conditions, making them suitable candidates for high‐potential cathode materials in lithium‐ion batteries (LIBs). This study employs a combination of machine learning, semi‐empirical quantum mechanics, and density functional theory methods to evaluate a large library of 11 432 CSA molecules, focusing on material properties crucial for application in batteries, such as synthetic complexity, redox potential, gravimetric charge capacity, and energy density. After applying the thresholds for the synthetic complexity score at 2.62 and the redox potential at 3.25 V versus Li/Li + , we identify 50 CSA molecules that are easy to synthesize and suitable for the positive electrode in LIBs. By ranking on the basis of redox potential, 13 CSA molecules having potentials greater than 3.50 V versus Li/Li + are identified. Through further investigations using molecular dynamics simulations on these reactant molecules and their lithiated products, a molecule is singled out for synthesis and electrochemical evaluation. This molecule, lithium (2,5‐dicyano‐1,4‐phenylene)bis((methylsulfonyl)amide)(Li 2 ‐DCN‐PDSA), demonstrates a redox potential surpassing those previously reported within the class of CSA molecules. Moreover, the study explores the quantitative structure‐property relations of CSAs, yielding insights for the development of CSA‐based LIB cathode materials, informed by the comprehensive data assembled. Materials science ; Sulfonamide ; Conjugated system ; Lithium (medication) ; Cathode ; Ion ; Nanotechnology ; Organic chemistry ; Electrical engineering ; Engineering ; Polymer ; Composite material ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Jinming Fan | Bowei Yuan | Chao Qian | Shaodong Zhou
Source: Precision Chemistry (article, 2024-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials, a theory guided machine learning framework is constructed. Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component, which is in line with the concept of group contribution method in thermodynamics. To realize this, a Lewis-mode group contribution method (LGC) has been developed in this work, which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework (LGC-msBNN-EA). Thus, different optical properties of molecules are afforded accurately and efficiently-by using only a small data set for training. Moreover, by employing the EA model designed specifically for LGC, structural search is well achievable. The origins of the satisfying performance of the framework are discussed in detail. Considering that such a framework combines chemical principles and data-driven tools, most likely, it will be proven to be rational and efficient to complete mission regarding structure design in related fields. Artificial neural network ; Nonlinear system ; Computer science ; Set (abstract data type) ; Component (thermodynamics) ; Group (periodic table) ; Artificial intelligence ; Property (philosophy) ; Organic molecules ; Machine learning ; Molecule ; Chemistry ; Physics Nonlinear Optical Materials Research ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Junzhong Xie | Xuyuan Zhou | Bin Jin | Hong Jiang
Source: Journal of Chemical Theory and Computation (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems. Cluster (spacecraft) ; Phase diagram ; Cluster expansion ; Field (mathematics) ; Computer science ; Force field (fiction) ; Diagram ; Phase (matter) ; Data mining ; Materials science ; Nanotechnology ; Artificial intelligence ; Physics ; Thermodynamics ; Mathematics ; Database Machine Learning in Materials Science ; High Temperature Alloys and Creep ; X-ray Diffraction in Crystallography
Authors: Yuji Takiguchi | Daisuke Nakane | Takashiro Akitsu
Source: IUCrJ (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The prediction of single-molecule magnet properties via deep learning This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising. Deep learning ; Molecule ; Magnet ; Computer science ; Single-molecule magnet ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Magnetization ; Magnetic field Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Machine Learning in Materials Science
Authors: Sownyak Mondal | Raheel Hammad
Source: Advanced Theory and Simulations (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Discovery of Non‐Linear Optical Materials Abstract Nonlinear optical(NLO) materials are crucial in achieving desired frequencies in solid‐state lasers. So far, new NLO materials have been discovered using high‐throughput calculations or chemical intuition. This study demonstrates the effectiveness of utilizing a high refractive index as a proxy for a high second harmonic generation(SHG) coefficient. It also emphasizes the importance of hardness in screening balanced NLO materials. Two machine learning models are developed to predict refractive indices and Vickers hardness. By applying these models to the OQMD database, potential NLO candidates are identified based on non‐centrosymmetricity, refractive index, hardness value, and bandgap properties. These findings are validated using density functional theory(DFT) calculations. Notably, this approach successfully identifies several already established NLO materials, reinforcing the validity of the methodology. Computer science ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Oliver Morrison | Elena Uteva | Gavin S. Walker | David M. Grant | Sanliang Ling
Source: ACS Applied Energy Materials (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials Magnesium hydride (MgH<sub>2</sub>) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material's limiting factor is the high dehydrogenation temperature (over 300 °C) and sluggish (de)hydrogenation kinetics when no catalyst is present, making it impractical for onboard applications. Catalysts and physical restructuring (e.g., through ball milling) have both shown kinetic improvements, without full theoretical understanding as to why. In this work, we developed a machine learning interatomic potential (MLP) for the Mg-H system, which was used to run long time scale molecular dynamics (MD) simulations of a thick magnesium hydride surface slab for up to 1 ns. Our MLP-based MD simulations reveal previously unreported behavior of subsurface molecular H<sub>2</sub> formation and subsequent trapping in the subsurface layer of MgH<sub>2</sub>. This hindered diffusion of subsurface H<sub>2</sub> offers a partial explanation on the slow dehydrogenation kinetics of MgH<sub>2</sub>. The kinetics will be improved if a catalyst obstructs subsurface formation and trapping of H<sub>2</sub> or if the diffusion of subsurface H<sub>2</sub> is improved through defects created by physical restructuring. Dehydrogenation ; Magnesium hydride ; Molecular dynamics ; Hydrogen storage ; Magnesium ; Hydride ; Catalysis ; Hydrogen ; Diffusion ; Chemistry ; Materials science ; Chemical physics ; Kinetics ; Thermodynamics ; Computational chemistry ; Metallurgy ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: K. Yamaguchi | Yoshiya Matsukawa | Yui Numazawa | Hideyuki Aoki
Source: Chemical Engineering Journal (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Modeling of water gas shift reaction using neural network trained on detailed kinetic mechanisms Artificial neural network ; Kinetic energy ; Computational fluid dynamics ; Logarithm ; Water-gas shift reaction ; Reaction rate ; Computation ; Biological system ; Inverse ; Computer science ; Chemistry ; Algorithm ; Thermodynamics ; Mathematics ; Machine learning ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Heat transfer and supercritical fluids
Authors: Chaofan Liu | Zhengxin Chen | Chunliang Ding | Shengde Jin | Jiafan Wang | Jiawei Feng | Jiang Wu | Heping Huang | Jia Lin | Jingfei Yu | Yuyue Quan | Kaiyuan Zhang
Source: Solar Energy Materials and Solar Cells (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of direct bandgap doped-spinel photovoltaic materials: A target-driven approach using interpretable machine learning Spinel ; Band gap ; Doping ; Photovoltaic system ; Computer science ; Context (archaeology) ; Materials science ; Semiconductor ; Ab initio ; Machine learning ; Nanotechnology ; Artificial intelligence ; Algorithm ; Engineering physics ; Optoelectronics ; Physics ; Engineering ; Electrical engineering ; Geology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Miguel Gallegos | Bienfait Kabuyaya Isamura | Paul L. A. Popelier | Ángel Martín Pendás
Source: Journal of Chemical Information and Modeling (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters. In this work, we implement an unsupervised ML strategy relying on a Gaussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs effortlessly decompose the vastness of the chemical and conformational spaces into well-defined radial and angular clusters, which are then used to build tailor-made ACSFs. The unsupervised exploration of the space has demonstrated general applicability across a diverse range of systems, spanning from various unimolecular landscapes to heterogeneous databases. The impact of the sampling technique and temperature on space exploration is also addressed, highlighting the particularly advantageous role of high-temperature Molecular Dynamics (MD) simulations. The reliability of the resulting features is assessed through the estimation of the atomic charges of a prototypical capped amino acid and a heterogeneous collection of CHON molecules. The automatically constructed ACSFs serve as high-quality descriptors, consistently yielding typical prediction errors below 0.010 electrons bound for the reported atomic charges. Altering the spatial distribution of the functions with respect to the cluster highlights the critical role of symmetry rupture in achieving significantly improved features. More specifically, using two separate functions to describe the lower and upper tails of the cluster results in the best performing models with errors as low as 0.006 electrons. Finally, the effectiveness of finely tuned features was checked across different architectures, unveiling the superior performance of Gaussian Process (GP) models over Feed Forward Neural Networks (FFNNs), particularly in low-data regimes, with nearly a 2-fold increase in prediction quality. Altogether, this approach paves the way toward an easier construction of local chemical descriptors, while providing valuable insights into how radial and angular spaces should be mapped. Finally, this work opens the possibility of encoding many-body information beyond angular terms into upcoming ML features. Computer science ; Chemical space ; Gaussian ; Mixture model ; Cluster (spacecraft) ; Unsupervised learning ; Space (punctuation) ; Artificial intelligence ; Cluster analysis ; Range (aeronautics) ; Atom (system on chip) ; Machine learning ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Computational chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Abdulelah S. Alshehri | Kai A. Horstmann | Fengqi You
Source: Journal of Chemical Information and Modeling (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction Chemical information disseminated in scientific documents offers an untapped potential for deep learning-assisted insights and breakthroughs. Automated extraction efforts have shifted from resource-intensive manual extraction toward applying machine learning methods to streamline chemical data extraction. While current extraction models and pipelines have ushered in notable efficiency improvements, they often exhibit modest performance, compromising the accuracy of predictive models trained on extracted data. Further, current chemical pipelines lack both transferability─where a model trained on one task can be adapted to another relevant task with limited examples─and extensibility, which enables seamless adaptability for new extraction tasks. Addressing these gaps, we present ChemREL, a versatile chemical data extraction pipeline emphasizing performance, transferability, and extensibility. ChemREL utilizes a custom, diverse data set of chemical documents, labeled through an active learning strategy to extract two properties: normal melting point and lethal dose 50 (LD<sub>50</sub>). The normal melting point is selected for its prevalence in diverse contexts and wider literature, serving as the foundation for pipeline training. In contrast, LD<sub>50</sub> evaluates the pipeline's transferability to an unrelated property, underscoring variance in its biological nature, toxicological context, and units, among other differences. With pretraining and fine-tuning, our pipeline outperforms existing methods and GPT-4, achieving F1-scores of 96.1% for entity identification and 97.0% for relation mapping, culminating in an overall F1-score of 95.4%. More importantly, ChemREL displays high transferability, effectively transitioning from melting point extraction to LD<sub>50</sub> extraction with 10 randomly selected training documents. Released as an open-source package, ChemREL aims to broaden access to chemical data extraction, enabling the construction of expansive relational data sets that propel discovery. Pipeline (software) ; Computer science ; Extraction (chemistry) ; Data extraction ; Artificial intelligence ; Data science ; Chemistry ; MEDLINE ; Chromatography Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Web Data Mining and Analysis
Authors: Karol Frydrych | Maciej Tomczak | Stefanos Papanikolaou
Source: Materials (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach This paper describes an application of a machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long short-term memory networks allowed a reasonable agreement of stress–strain curves to be obtained for cyclic deformation in a low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility of obtaining parameters for a new material without the necessity of conducting any further optimizations. As the power and robustness of the developed method was demonstrated for very challenging problems (cyclic deformation, crystal plasticity, self-consistent model and isotropic and kinematic hardening), it is directly applicable to other experiments and models. Crystal plasticity ; Copper ; Plasticity ; Materials science ; Crystal (programming language) ; Composite material ; Mechanical engineering ; Metallurgy ; Computer science ; Engineering Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties