jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Teerachote Pakornchote | Annop Ektarawong | Thiparat Chotibut
Source: arXiv (Cornell University) (preprint, 2023-06-22)
Field: Materials Science — Materials Chemistry

StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic scale ab initio approaches are computationally intensive, especially for studying complex materials with a large number of atoms in a unit cell. We introduce a novel data-driven approach to efficiently predict the elastic properties of crystal structures using SE(3)-equivariant graph neural networks (GNNs). This approach yields important scalar elastic moduli with the accuracy comparable to recent data-driven studies. Importantly, our symmetry-aware GNNs model also enables the prediction of the strain energy density (SED) and the associated elastic constants, the fundamental tensorial quantities that are significantly influenced by a material's crystallographic group. The model consistently distinguishes independent elements of SED tensors, in accordance with the symmetry of the crystal structures. Finally, our deep learning model possesses meaningful latent features, offering an interpretable prediction of the elastic properties. Equivariant map ; Elastic modulus ; Crystal structure prediction ; Ab initio ; Crystal structure ; Artificial neural network ; Scalar (mathematics) ; Moduli ; Graph ; Computer science ; Statistical physics ; Symmetry (geometry) ; Artificial intelligence ; Algorithm ; Materials science ; Mathematics ; Theoretical computer science ; Physics ; Crystallography ; Chemistry ; Thermodynamics ; Geometry ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Robert H. Lavroff | Bobby Judd
Source: ChemRxiv (preprint, 2023-08-24)
Field: Materials Science — Materials Chemistry

Accurately Extending the Organic-Trained RexGen Graph Convolutional Neural Network to Inorganic Reaction Prediction Computational chemists have taken great interest in machine learning in recent years, as techniques are being developed to produce faster predictions with higher accuracy. In 2019, Coley, et al, proposed a graph convolutional neural network (GCNN) model which predicts organic chemical reactions with 85\% accuracy. By creating graphs with atoms as nodes and bonds as edges, and exploiting the standard rules of organic chemical bonding, the trained model was able to output graphs corresponding to the actual product of the given reaction. This is a promising model, and would be even more powerful if also able to predict metal-based, inorganic chemical reactions. We explore the accuracy of this model for inorganic reaction predictions in this paper. Testing on 53,000 data points not included in the original paper, we were able to demonstrate 85\% accuracy in predicting chemical reactions, both organic and inorganic, with the pre-trained, organic-based GCNN. On exclusively inorganic reactions, the pre-trained model performed with 82\% accuracy. Re-training the model with our own training and validation datasets comprised of inorganic reactions, we were able to achieve 96.0\% accuracy, and 97.2\% accuracy with dropout regularization. Convolutional neural network ; Computer science ; Regularization (linguistics) ; Artificial neural network ; Graph ; Artificial intelligence ; Deep learning ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Robert H. Lavroff | Bobby Judd
Source: ChemRxiv (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Accurately Extending the Organic-Trained RexGen Graph Convolutional Neural Network to Inorganic Reaction Prediction Computational chemists have taken great interest in machine learning in recent years, as techniques are being developed to produce faster predictions with higher accuracy. In 2019, Coley, et al, proposed a graph convolutional neural network (GCNN) model which predicts organic chemical reactions with 85\% accuracy. By creating graphs with atoms as nodes and bonds as edges, and exploiting the standard rules of organic chemical bonding, the trained model was able to output graphs corresponding to the actual product of the given reaction. This is a promising model, and would be even more powerful if also able to predict metal-based, inorganic chemical reactions. We explore the accuracy of this model for inorganic reaction predictions in this paper. Testing on 53,000 data points not included in the original paper, we were able to demonstrate 85\% accuracy in predicting chemical reactions, both organic and inorganic, with the pre-trained, organic-based GCNN. On exclusively inorganic reactions, the pre-trained model performed with 82\% accuracy. Re-training the model with our own training and validation datasets comprised of inorganic reactions, we were able to achieve 96.0\% accuracy, and 97.2\% accuracy with dropout regularization. Convolutional neural network ; Computer science ; Regularization (linguistics) ; Graph ; Artificial neural network ; Artificial intelligence ; Deep learning ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jianan Zhang | Aditya Koneru | Subramanian K. R. S. Sankaranarayanan | Carmen M. Lilley
Source: ChemRxiv (preprint, 2023-04-10)
Field: Materials Science — Materials Chemistry

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is non-trivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism), and density functional theory (DFT). Systematic downsampling of the training datasets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (< 2000) DFT generated energy labels for training. We further couple the GNN model with a multi-objective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, material agnostic, and is anticipated to accelerate the discovery of 2D GB structures. Computer science ; Artificial neural network ; Evolutionary algorithm ; Workflow ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Didac Martí | Rémi Pétuya | Emanuele Bosoni | Anne‐Claude Dublanchet | Stephan Mohr | Fabien Léonforte
Source: ChemRxiv (preprint, 2023-12-12)
Field: Materials Science — Polymers and Plastics

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning Nature has only provided us with a limited number of bio-based and biodegradable building blocks. Therefore, the fine tuning of the sustainable polymer properties is expected to be achieved through the control of the composition of bio-based copolymers for targeted applications such as cosmetics. Until now, the main approaches to alleviate the experimental efforts and accelerate the discovery of new polymers have relied on machine learning models trained on experimental data, which implies an enormous and difficult work in the compilation of data from heterogeneous sources. On the other hand, molecular dynamics simulations of polymers have shown that they can accurately capture the experimental trends for a series of properties. However, the combination of different ratios of monomers in copolymers can rapidly lead to a combinatorial explosion, preventing the investigation of all possibilities via molecular dynamics simulations. In this work, we show that the combination of machine learning approaches and high-throughput molecular dynamics simulations permits to quickly and efficiently sample and characterize the relevant chemical design space for specific applications. Reliable simulation protocols have been implemented to evaluate the glass transition temperature of a series of 58 homopolymers, which exhibit a good agreement with experiments, and 488 copolymers. Overall, 2,184 simulations (4 replicas per polymer) were performed, for a total simulation time of 143.052 µs. These results, constituting a dataset of 546 polymers, have been used to train a machine learning model for the prediction of the MD-calculated glass transition temperature with a mean absolute error of 19.34 K and a R2 score of 0.83. Overall, within its applicability domain, this machine learning model provides an impressive acceleration over molecular dynamics simulations: the glass transition temperature of thousands of polymers can be obtained within seconds, whereas it would have taken node-years to simulate them. This type of approach can be tuned to address different design spaces or different polymer properties and thus have the potential to accelerate the discovery of new polymers. Glass transition ; Molecular dynamics ; Polymer ; Computer science ; Throughput ; Copolymer ; Work (physics) ; Chemical space ; Machine learning ; Materials science ; Biological system ; Nanotechnology ; Chemistry ; Thermodynamics ; Computational chemistry ; Physics Polymer crystallization and properties ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Fahimeh Najafi | Henrik Andersen Sveinsson | Christer Dreierstad | Anders Malthe‐Sørenssen
Source:  (preprint, 2023-02-26)
Field: Materials Science — Materials Chemistry

Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks Neural networks have proved to be able to capture the relevant and informative features of a wide variety of data types and predict the desired output for different regression or classification problems. Finding a mapping between materials&amp;#8217; structure and a given physical property of those systems is an example of a problem that could be approached with machine learning methods like neural networks. Especially when we are dealing with systems with a very large design space where using classical computational methods like molecular dynamics can be very time and resource-consuming for the study of a very large number of systems, a well-trained neural network can be greatly faster and more efficient for computing the relevant properties. In this work, we study &amp;#945;-quartz crystals with one porous layer with simplex noise as the shape of porosity. Simplex noise is a gradient based procedural algorithm that can produce irregular geometries with surface morphology resembling what is observed in nature. The property that we want the neural network to learn is the yield stress of these systems under both shear and tensile deformation. Molecular dynamics simulations are used for a randomly selected sample of possible structures in order to generate the ground truth to be used as the training data. We employ deep convolutional neural networks (CNN) which are commonly used when dealing with image or image-like data since the input data for the problem in hand is a binary 2-D structure of the porous layer of the systems. The trained model is compared with a basic polynomial fit of stress versus porosity. The trained CNN is successful in predicting the yield stress of a system based on the geometry of that given system, with lower variability and higher precision compared to the base polynomial regression method. The saliency maps created with the trained model show the model to be successful in capturing the physics of the problem when compared with the stress fields calculated using molecular dynamics simulations. This method of modeling materials can be further developed for the inverse design of structures with desired properties without the need for a huge number of simulations on a wide domain of possible systems. Artificial neural network ; Convolutional neural network ; Artificial intelligence ; Computer science ; Deep learning ; Algorithm ; Pattern recognition (psychology) Machine Learning in Materials Science ; Drilling and Well Engineering
Authors: Daniel Schwalbe‐Koda | Nitish Govindarajan | Joel B. Varley
Source: ChemRxiv (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Controlling neural network extrapolation enables efficient and comprehensive sampling of coverage effects in catalysis Sampling high-coverage configurations and predicting adsorbate-adsorbate interactions on surfaces are highly relevant to understand realistic interfaces in heterogeneous catalysis. However, the combinatorial explosion in the number of adsorbate configurations among diverse site environments presents a considerable challenge in accurately estimating these interactions. Here, we propose a strategy combining high-throughput simulation pipelines and a neural network-based model with the MACE architecture to increase sampling efficiency and speed. By training the models on unrelaxed structures and energies, which can be quickly obtained from single-point DFT calculations, we achieve excellent performance for both in-domain and out-of-domain predictions, including extrapolation to different facets, coverage regimes and low-energy configurations. From this systematic understanding of model robustness, we exhaustively sample the configuration phase space of catalytic systems without false positives or active learning. In particular, by evaluating over 14 million structures within the simulated annealing method, we predict coverage-dependent adsorption energies for CO adsorption on six Cu facets (111, 100, 211, 331, 410 and 711) and the co-adsorption of CO and CHOH on Rh(111). When validated by targeted post-sampling relaxations, our results for CO on Cu correctly reproduce experimental interaction energies reported in the literature, and provide atomistic insights on the site occupancy of steps and terraces for the six facets at all coverage regimes. Additionally, the arrangement of CO on the Rh(111) surface is demonstrated to substantially impact the activation barriers for the CHOH bond scission, illustrating the importance of comprehensive sampling on reaction kinetics. Our findings demonstrate that simplified data generation routines and controlled extrapolation of neural networks can be deployed at scale to understand lateral interactions on surfaces, paving the way towards realistic modeling of heterogeneous catalytic processes. Extrapolation ; Adsorption ; Sampling (signal processing) ; Robustness (evolution) ; Computer science ; Simulated annealing ; Chemical physics ; Materials science ; Chemistry ; Biological system ; Physical chemistry ; Algorithm ; Mathematics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Valentina Di Francesco | Daniela P. Boso | Thomas L. Moore | Bernhard A. Schrefler | Paolo Decuzzi
Source: Research Square (preprint, 2023-06-15)
Field: Materials Science — Biomaterials

Machine Learning Instructed Microfluidic Synthesis of Curcumin-loaded Liposomes Abstract The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters – flow rates and mixing configurations, type and concentrations of the reagents – contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles. Dispersity ; Liposome ; Microfluidics ; Nanoparticle ; Curcumin ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence ; Biological system ; Chemistry Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Microfluidic and Bio-sensing Technologies
Authors: Sheng Gong | Shuo Wang | Taishan Zhu | Yang Shao‐Horn | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Multimodal machine learning for materials science: composition-structure bimodal learning for experimentally measured properties The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning. Machine learning ; Artificial intelligence ; Computer science ; Ensemble learning ; Materials informatics ; Health informatics Machine Learning in Materials Science
Authors: Jérôme Rey | Céline Chizallet | Dario Rocca | Tomáš Bučko | Michaël Badawi
Source: Angewandte Chemie (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Reference‐Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory Abstract For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation—RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer–Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling. Isomerization ; Ab initio ; Chemistry ; Transition state theory ; Transition state ; Cracking ; Computational chemistry ; Catalysis ; Molecular dynamics ; Free energy perturbation ; Thermodynamics ; Chemical physics ; Physical chemistry ; Reaction rate constant ; Kinetics ; Organic chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced Chemical Physics Studies
Authors: Suvo Banik | Karthik Balasubramanian | Sukriti Manna | Sybil Derrible | Subramanian Sankaranarayananan
Source: ChemRxiv (preprint, 2023-03-30)
Field: Materials Science — Materials Chemistry

Machine Learning for Elastic Properties of Materials: A predictive benchmarking study in a domain-segmented feature Space Insights into the unique characteristics across different classes of materials are crucial for Machine Learning (ML) tools and reveal the physics behind the studied process. Traditional predictive modeling of elastic properties of materials is limited to only a few classes of materials and a small set of ML tools despite the broad applications of these materials. While in recent years, Graph neural networks (GNNs) have outshined traditional ML models in terms of predictability, their intensive data requirement and lack of interpretability may limit practical applicability. In this work, we developed a domain-segmented feature space using a diverse set of material attributes and performed a predictive analysis of elastic properties of materials using state-of-the-art ML tools. By deducing the model-independent overall ranking of the features based on feature importance learned by each model, the knowledge is then transferred to GNNs. The findings indicate a saturation limit in the predictability of traditional ML models, but the transfer of task-specific feature importance knowledge to the GNNs can enhance their performance by reducing their data requirement while retaining considerable accuracy. Interpretability ; Predictability ; Benchmarking ; Computer science ; Machine learning ; Artificial intelligence ; Feature (linguistics) ; Domain knowledge ; Normalization (sociology) ; Ranking (information retrieval) ; Data mining ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Bingqian Song | Zhen Fan | Guangyong Jin | Yongli Song | Feng Pan | Chao Xin
Source: Research Square (preprint, 2023-08-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Discovery of Novel 2D Ferromagnetic Materials with Strong Magnetization Ferromagnetism ; Magnetization ; Condensed matter physics ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices