jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuqing Gao | Shengguang Qi | Mianrui Li | Tongmei Ma | Huiyu Song | Zhiming Cui | Zhenxing Liang | Li Du
Source: Angewandte Chemie (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium‐Ion Batteries Abstract Polymer electrolytes are of interest in high‐energy‐density batteries. However, how the intrinsic electron‐donating capability of polymer segments involved in coordination affects lithium‐ion dissociation/transmission and rationally guides the design and fabrication of electrolytes is a highly exploratory topic. This study proposes a workable method that integrates machine learning with density functional theory to predict donor numbers (DN) for polymer building units. Using this approach, polymer chains with optimized DN are designed, effectively modulating the solvation structure of lithium‐ion. Molecular dynamics simulations confirm the positive impact of polymer chains on rapid transport of lithium ions. Experimental validation of the proposed zwitterionic polymer electrolyte (ZPE) showcases satisfactory parameters: ion conductivity (0.59 mS cm −1 ), ion migration numbers (0.82), and activation energy (0.016 eV). Electrochemical analysis on Li|ZPE|Li symmetric batteries demonstrate sustained plating/stripping performance exceeding 3000 hours at a current density of 0.2 mA cm −2 . Assembled NCM|ZPE|Li batteries exhibit stable cycling over 1400 cycles at 4.3 V, with a capacity retention ratio of 92.3 %. Moreover, even under ultra‐high voltages of 4.5 V and 4.7 V, NCM|ZPE|Li batteries display stable cycling performances. This approach offers a paradigmatic strategy for polymer molecule design, advancing sustainable battery technologies. Polymer electrolytes ; Lithium (medication) ; Electrolyte ; Fabrication ; Ion ; Polymer ; Rational design ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Organic chemistry ; Ionic conductivity ; Engineering ; Composite material ; Electrode ; Physical chemistry ; Psychology Machine Learning in Materials Science
Authors: Takuya Seki | Yudai Shinozaki | Ryosuke Ito | Shunsuke Sato | Jun Takeya | Toshihiro Okamoto | Go Watanabe
Source: ChemRxiv (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Determining Molecular Packing of Organic Semiconductors: Toward Accurate Crystal Structure Prediction Organic semiconductors (OSCs) with π-electron skeletons (π-cores) have attracted much attention. The creation of more innovative new molecules with high carrier mobility requires the strategic molecular design. One of the most important properties affecting the carrier mobility of π-conjugated OSCs is the molecular arrangement, especially the two-dimensional (2D) molecular packing of the π-cores which exhibit 1D or 2D carrier conductivity as follows: π-stacking, herringbone (HB) packing, and brickwork (BW). Since two molecular packing structures, HB and BW, have not been predicted theoretically, chemists have typically designed new OSC molecules based on their previous knowledge so far. Therefore, the use of computational science and informatics science is eagerly needed to strategically design an OSC molecule with unprecedented properties and functions. In this study, we introduce machine learning method to determine whether an OSC forms 2D HB or BW molecular packing with a high accuracy. We further present the computational method to predict the crystal structure of an OSC only from its chemical structure using molecular mechanics calculation and molecular dynamics simulation coupled with our proposed machine learning model to classify the type of 2D molecular packing. The combination of molecular simulations with the machine learning model has the potential to predict the crystal structure of organic molecules. Organic semiconductor ; Semiconductor ; Materials science ; Crystal (programming language) ; Crystal structure prediction ; Artificial intelligence ; Crystal structure ; Computer science ; Biological system ; Crystallography ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Dana O’Connor | Paola A. Buitrago
Source:  (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Property Prediction of Functional Organic Molecular Crystals with Graph Neural Networks Predicting the properties of molecular crystals is imperative to the field of materials design. In lieu of alternative methods, advances in machine learning have made it possible to predict the properties of materials before synthesis. This is especially important for organic semiconductors (OSCs) that are prone to exhibit polymorphism, as this phenomenom can impact the properties of a system, including the bandgap in OSCs. While graph neural networks (GNNs) have shown promise in predicting the bandgap in OSCs, few studies have considered the impact of polymorphism on their performance. Using the MatDeepLearn framework, we examine five different graph convolution layers of ALIGNN, GATGNN, CGCNN, MEGNet, and SchNet, which all have graph convolutions implemented in torch geometric. A dataset of functional organic molecular crystals is extracted from the OCELOT database, which has calculated density functional theory (DFT) values for the bandgap as well as several sets of polymorphs. The trained models are then evaluated on several test cases including the polymorphs of ROY. In future work we plan to examine the impact of graph representations on the performance of these models in the case of predicting properties of polymorphic OSCs. Property (philosophy) ; Computer science ; Artificial neural network ; Graph ; Graph theory ; Artificial intelligence ; Theoretical computer science ; Materials science ; Biological system ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Virginio Torlao | Estefania Fajardo
Source: arXiv (Cornell University) (preprint, 2024-12-01)
Field: Materials Science — Materials Chemistry

Formation Energy Prediction of Material Crystal Structures using Deep Learning Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability property. Our model leverages elemental fractions derived from material composition and incorporates the symmetry classification as an additional input feature. The materials' symmetry classifications represent the crystal polymorphs and are crucial for understanding phase transitions in materials. Our findings demonstrate that the integration of crystal system, point group, or space group symmetry information significantly enhances the predictive performance of the developed deep learning architecture, where the highest accuracy was achieved when space group classification was incorporated. In addition, we use the same model architecture to predict the energy above hull, an indicator to material stability, with formation energy as an additional input feature. Energy (signal processing) ; Crystal (programming language) ; Materials science ; Artificial intelligence ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Saksam Checker | Saksham Gupta | Deshraj Meena
Source:  (article, 2024-05-09)
Field: Materials Science — Materials Chemistry

Exploring the Classification of Crystal Structure of Perovskite Oxide Using Machine Learning and Deep Learning Classification of crystal structures of perovskite oxides (ABO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>) has been done using various machine learning algorithms. This paper identifies various features, like valance, ionic radius, and electronegativity from the dataset, and the respective features crucial for classification of the perovskites have been used to classify structures of perovskite oxides. We classified the oxides into cubic, tetragonal, orthorhombic, and rhombohedral crystal structures using machine learning and deep learning models. Amongst these, the Kernel SVM model proved to be the best model showing an accuracy of 94%. The GridSerachCV method was implemented for hyperparameter tuning. KernelSVM uses kernel trick to handle nonlinear data and thus allows an SVM to find a better hyperplane to classify. Therefore, KernelSVM proves to be a robust and reliable model for the classification of perovskite materials. Further, data augmentation improved the accuracy of all models except Logistic Regression. We also proposed a novel Deep Learning approach, Encoder-Decoder Kernel SVM (EDKSVM) which expands the feature matrix to an expanded dimension, outperforming the existing Machine Learning approaches. Perovskite (structure) ; Artificial intelligence ; Computer science ; Deep learning ; Crystal structure ; Materials science ; Oxide ; Crystal (programming language) ; Machine learning ; Chemistry ; Crystallography ; Metallurgy Machine Learning in Materials Science
Authors: Patrick Geitner
Source: arXiv (Cornell University) (preprint, 2024-04-05)
Field: Materials Science — Materials Chemistry

Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery New technology for energy storage is necessary for the large-scale adoption of renewable energy sources like wind and solar. The ability to discover suitable catalysts is crucial for making energy storage more cost-effective and scalable. The Open Catalyst Project aims to apply advances in graph neural networks (GNNs) to accelerate progress in catalyst discovery, replacing Density Functional Theory-based (DFT) approaches that are computationally burdensome. Current approaches involve scaling GNNs to over 1 billion parameters, pushing the problem out of reach for a vast majority of machine learning practitioner around the world. This study aims to evaluate the performance and insights gained from using more lightweight approaches for this task that are more approachable for smaller teams to encourage participation from individuals from diverse backgrounds. By implementing robust design patterns like geometric and symmetric message passing, we were able to train a GNN model that reached a MAE of 0.0748 in predicting the per-atom forces of adsorbate-surface interactions, rivaling established model architectures like SchNet and DimeNet++ while using only a fraction of trainable parameters. Deep learning ; Catalysis ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Brian H. Lee | James P. Larentzos | John K. Brennan | Alejandro Strachan
Source: arXiv (Cornell University) (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

Graph neural network coarse-grain force field for the molecular crystal RDX Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities. Crystal (programming language) ; Graph ; Materials science ; Artificial neural network ; Force field (fiction) ; Computer science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Christian Venturella | Jiachen Li | Christopher Hillenbrand | Ximena Leyva Peralta | Jessica Liu | Tianyu Zhu
Source: arXiv (Cornell University) (preprint, 2024-07-29)
Field: Materials Science — Materials Chemistry

Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions Quantum many-body methods provide a systematic route to computing electronic properties of molecules and materials, but high computational costs restrict their use in large-scale applications. Due to the complexity in many-electron wavefunctions, machine learning models capable of capturing fundamental many-body physics remain limited. Here, we present a deep learning framework targeting the many-body Green's function, which unifies predictions of electronic properties in ground and excited states, while offering physical insights into many-electron correlation effects. By learning the $GW$ or coupled-cluster self-energy from mean-field features, our graph neural network achieves competitive performance in predicting one- and two-particle excitations and quantities derivable from one-particle density matrix. We demonstrate its high data efficiency and good transferability across chemical species, system sizes, molecular conformations, and correlation strengths in bond breaking, through multiple molecular and nanomaterial benchmarks. This work opens up opportunities for utilizing machine learning to solve many-electron problems. Deep learning ; Quantum ; Computer science ; Cognitive science ; Artificial intelligence ; Nanotechnology ; Psychology ; Physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science
Authors: Ke-Lin Zhao | Qingxu Li
Source: Advances in Applied Sciences (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed. Computer science ; Graph ; Convolution (computer science) ; Artificial neural network ; Data science ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xiaojian Wang | Xi Xue | Kai Chen | Hanyu Sun | Xiangying Liu | Xue Liu | Shize Li | Jingjie Yan | Mohammad S. Mubarak | Ahmed Al‐Harrasi | Hai‐Yu Hu | Yafeng Deng | Xiandao Pan
Source: Research Square (preprint, 2024-07-25)
Field: Materials Science — Materials Chemistry

Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reactions Retrosynthetic analysis ; Computer science ; Benchmark (surveying) ; Artificial intelligence ; Chemical database ; Machine learning ; Deep learning ; Training set ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Tomoya Shiota | Kenji Ishihara | Wataru Mizukami
Source: arXiv (Cornell University) (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the number of constituent elements, making even machine learning-based screening calculations time-intensive. To address this challenge, we propose a rapid method for predicting HEA properties using data from monometallic systems (or few-component alloys). Central to our approach is the newly introduced local surface energy (LSE) descriptor, which captures local surface reactivity at atomic resolution. We established a correlation between LSE and adsorption energies using monometallic systems. Using this correlation in a linear regression model, we successfully estimated molecular adsorption energies on HEAs with significantly higher accuracy than a conventional descriptor (i.e., generalized coordination numbers). Furthermore, we developed high-precision models by employing both classical and quantum machine learning. Our method enabled CO adsorption-energy calculations for 1000 quinary nanoparticles, comprising 201 atoms each, within a few days, considerably faster than density functional theory, which would require hundreds of years or neural network potentials, which would have taken hundreds of days. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts. Quinary ; Artificial neural network ; Adsorption ; Materials science ; Density functional theory ; Entropy (arrow of time) ; Cluster expansion ; Chemical space ; Molecule ; High entropy alloys ; Statistical physics ; Catalysis ; Computer science ; Alloy ; Machine learning ; Chemical physics ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions
Authors: Yi-Bin Fang | Cheng Shang | Zhi‐Pan Liu | Xin-Gao Gong
Source: The Journal of Chemical Physics (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential Liquid–liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid’s atomic arrangements. When examining CdSxTe1−x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x &amp;gt; 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x &amp;lt; 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system’s behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes. Alloy ; Chemical physics ; Molecular dynamics ; Phase diagram ; Phase transition ; Materials science ; Crystallography ; Phase (matter) ; Chemistry ; Condensed matter physics ; Physics ; Computational chemistry ; Metallurgy Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Phase-change materials and chalcogenides
Authors: K E Vipin | Prahallad Padhan
Source: arXiv (Cornell University) (preprint, 2024-12-17)
Field: Materials Science — Materials Chemistry

Data-Driven Catalyst Design: A Machine Learning Approach to Predicting Electrocatalytic Performance in Hydrogen Evolution and Oxygen Evolution Reactions The transition to sustainable green hydrogen production demands innovative electrocatalyst design strategies that can overcome current technological limitations. This study introduces a comprehensive data-driven approach to predicting and understanding catalytic performance for Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER) using advanced machine learning methodologies. By usimg a dataset of 16,226 data points from the Catalysis-hub database, we developed a novel stacking ensemble model that integrates Random Forest, XGBoost, and Support Vector Regression to predict Gibbs free energy of adsorption across diverse bimetallic alloy surfaces. Our innovative feature engineering strategy combined Matminer-based compositional analysis, Principal Component Analysis for adsorption site related features, and correlation screening to generate robust predictive descriptors. The machine learning model demonstrated exceptional predictive capabilities, achieving R^2 values of 0.98 for HER and 0.94 for OER, with Mean Absolute Error values of 0.251 and 0.121, respectively. Shapley Additive Explanations (SHAP) analysis revealed critical insights into the complex interplay of compositional, structural, and electronic features governing catalytic performance. The research provides a powerful computational framework for accelerating electrocatalyst design, offering unprecedented insights into the fundamental properties that drive hydrogen evolution and oxygen evolution reactions. By bridging advanced machine learning techniques with fundamental electrochemical principles, this study presents a transformative approach to developing cost-effective, high-performance catalysts for sustainable hydrogen production. Oxygen evolution ; Catalysis ; Oxygen ; Computer science ; Electrocatalyst ; Chemistry ; Electrochemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials