jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas Dufils | Lisanne Knijff | Yunqi Shao | Chao Zhang
Source: arXiv (Cornell University) (preprint, 2023-03-27)
Field: Materials Science — Materials Chemistry

PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modeling heterogeneous and complex electrode materials often found in energy storage systems. Computer science ; Electrode ; Artificial intelligence ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrocatalysts for Energy Conversion
Authors: Neel Redkar
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Chemistry — Inorganic Chemistry

CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emissions; almost 51% of total US carbon emissions are from factories. Current materials used in CO2 capture are lacking either in efficiency, sustainability, or cost. Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the components used industrially as fuel, saving transportation costs, creating financial incentives. Metal Organic Frameworks (MOFs) are crystals made of organo-metals that adsorb, filter, and electrocatalyze CO2. The current available MOFs for capture &amp; electrocatalysis are expensive to manufacture and inefficient at capture. The goal therefore is to computationally design a MOF that can adsorb CO2 and catalyze carbon monoxide &amp; oxygen with low cost. A novel active transfer learning neural network was developed, utilizing transfer learning due to limited available data on 15 MOFs. Using the Cambridge Structural Database with 10,000 MOFs, the model used incremental mutations to fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF (C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions &amp; literature, the converged MOF was shown to be more effective &amp; more synthetically accessible than existing MOFs, showing the model had an understanding of effective electrocatalytic structures in the material space. This novel network can be implemented for other gas separations and catalysis applications that have limited training accessible datasets. Metal-organic framework ; Electrocatalyst ; Computer science ; Adsorption ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Yifei Wang | Yunrui Li | Liu, Lin | Pengyu Hong | Hao Xu
Source: arXiv (Cornell University) (preprint, 2023-11-11)
Field: Materials Science — Materials Chemistry

Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, opening new frontiers in chemical understanding and drug discovery. Hence, we introduce Asymmetric Contrastive Multimodal Learning (ACML), a specifically designed approach to enhance molecular understanding and accelerate advancements in drug discovery. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pre-trained chemical unimodal encoders and a shallow-designed graph encoder with 5 layers, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. We demonstrate the effectiveness of this framework through large-scale cross-modality retrieval and isomer discrimination tasks. Additionally, ACML enhances interpretability by revealing chemical semantics in graph presentations and bolsters the expressive power of graph neural networks, as evidenced by improved performance in molecular property prediction tasks from MoleculeNet and Therapeutics Data Commons (TDC). Ultimately, ACML exemplifies its potential to revolutionize molecular representational learning, offering deeper insights into the chemical semantics of diverse modalities and paving the way for groundbreaking advancements in chemical research and drug discovery. Computer science ; Interpretability ; Modalities ; Artificial intelligence ; Deep learning ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Valentina Rein | Hao Gao | Hendrik H. Heenen | Wissal Sghaier | Anastasios C. Manikas | Mehdi Saedi | Johannes T. Margraf | Costas Galiotis | G. Renaud | Oleg Konovalov | Irene M. N. Groot | Karsten Reuter | Maciej Jankowski
Source: arXiv (Cornell University) (preprint, 2023-05-24)
Field: Materials Science — Materials Chemistry

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor deposition on a liquid copper catalyst. By monitoring the growing graphene flakes in real time using in situ radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH4-to-H2 pressure ratios and deposition temperatures. Constant growth rates of the flakes' radius indicate a growth mode limited by precursor attachment, whereas methane-flux-dependent flake shapes point to limited precursor availability. Large-scale free energy simulations enabled by an efficient machine-learning moment tensor potential trained to density-functional theory data provide quantitative barriers for key atomic-scale growth processes. The wealth of experimental and theoretical data can be consistently combined into a microkinetic model that reveals mixed growth kinetics that, in contrast to the situation at solid Cu, is partly controlled by precursor attachment alongside precursor availability. Key mechanistic aspects that directly point toward the improved graphene quality are a largely suppressed carbon dimer attachment due to the facile incorporation of this precursor species into the liquid surface and a low-barrier ring-opening process that self-heals 5-membered rings resulting from remaining dimer attachments. Graphene ; Chemical vapor deposition ; Materials science ; Nanotechnology ; Kinetics ; Catalysis ; Deposition (geology) ; Copper ; Chemical engineering ; Chemical physics ; Characterization (materials science) ; Chemistry ; Organic chemistry ; Metallurgy Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Seonghwan Kim | Jeheon Woo | Woo Youn Kim
Source: Research Square (preprint, 2023-05-23)
Field: Materials Science — Materials Chemistry

Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs Abstract The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learned the distribution of TS geometries for diverse reactions in training. Thus, TSDiff was able to find more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration. Generative grammar ; Transition (genetics) ; Diffusion ; Generative model ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Elena Gelžinytė | Mario Öeren | Matthew Segall | Gábor Cśanyi
Source: ChemRxiv (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 dataset, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific datasets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H bond dissociation energy (BDE), which allows us to compare reaction energies of hydrogen abstraction, which is the rate limiting step of the aliphatic hydroxylation reaction catalysed by CYPs. On the "CYP 3A4" dataset MACE achieves BDE RMSE of 1.37 kcal/mol and a better BDE rank predictions than currently used AM1 semi-empirical method. Chemistry ; Dissociation (chemistry) ; Molecule ; Transferability ; Bond-dissociation energy ; Hydrogen bond ; Mace ; Hydroxylation ; Computational chemistry ; Limiting ; Computer science ; Physical chemistry ; Machine learning ; Organic chemistry ; Enzyme Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Ahmed Elrashidy | James Della-Giustina | Jia-An Yan
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks In this study, we employ Graph Neural Networks (GNNs) to accelerate the discovery of novel 2D magnetic materials which have transformative potential in spintronics applications. Using data from the Materials Project database and the Computational 2D materials database (C2DB), we train three GNN architectures on a dataset of 1190 magnetic monolayers with energy above the convex hull ($E_{\text{hull}}$) less than 0.3 eV/atom. Our Crystal Diffusion Variational Auto Encoder (CDVAE) generates 11,100 candidate crystals. Subsequent training on two Atomistic Line Graph Neural Networks (ALIGNN) achieves a 93$\%$ accuracy in predicting magnetic monolayers and a mean average error of 0.039 eV/atom for $E_{\text{hull}}$ predictions. After narrowing down candidates based on magnetic likelihood and predicted energy, constraining the atom count in the monolayers to five or fewer, and performing dimensionality checks, we identify 190 candidates. These are validated using Density-Functional Theory (DFT) to confirm their magnetic and energetic favorability resulting in 167 magnetic monolayers with $E_{\text{hull}} &lt; 0.3$ eV/atom and a total magnetization of $\geq$ $0.5 μ_{B}$. Our methodology offers a way to accelerate exploring and predicting potential 2D magnetic materials, contributing to the ongoing computational and experimental efforts aimed at the discovery of new 2D magnets. Curse of dimensionality ; Convex hull ; Magnet ; Density functional theory ; Condensed matter physics ; Atom (system on chip) ; Spintronics ; Monolayer ; Computer science ; Artificial neural network ; Magnetization ; Materials science ; Graph ; Artificial intelligence ; Physics ; Algorithm ; Theoretical computer science ; Regular polygon ; Mathematics ; Nanotechnology ; Quantum mechanics ; Magnetic field ; Ferromagnetism ; Geometry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Multiferroics and related materials
Authors: Zhiyu Wang | Jirui Jin | Mingjie Liu
Source: The Journal of Physical Chemistry C (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Atomic Charge Schemes Comparison for Fe Single Atom in Graphitic Carbon: Insights from Quantum Simulations and Machine Learning Single-atom catalysts (SACs), where a transition-metal atom is embedded in graphitic carbon materials, have shown great potential as effective catalysts. The metal atom plays a major role in determining the catalytic reactivity, and substrate–metal interaction can tailor the intrinsic properties of metal, for example, the atomic partial charge. However, in quantum simulations, various charge schemes are used to assign charges for molecules and materials. It remains unclear whether the atomic partial charge of the metal in SACs is correlated across different charge schemes and if they exhibit different behaviors in response to variations in metal–substrate interactions. In this study, we investigated the behavior of Bader, Mulliken, Hirshfeld, Charge Model 5 (CM5), and Density Derived Electrostatic and Chemical 6 (DDEC6) charge schemes for Fe-centered SACs using quantum simulations and machine learning models. By tuning the structural and chemical parameters in the systems, we examined 166 Fe-centered graphene flakes and compared different charge schemes on the Fe atom. We observed that the DDEC6, Mulliken, and Hirshfeld charges exhibit more noticeable correlations than Bader and CM5 charges. Our findings also indicate that the local chemical environment plays a crucial role in determining the atomic charge of Fe in SACs and that different charge schemes present varied responses to changes in metal–substrate interactions. Additionally, we utilized machine learning (ML) models to predict all five charge schemes by gathering features from geometric, atomic, and electronic properties. Our machine learning models successfully predicted the CM5 scheme, which is primarily determined by electronic properties. However, for DDEC6, Bader, Mulliken, and Hirshfeld schemes, geometric features played a significant role, resulting in lower prediction accuracy than CM5. We expect that the insights gained from this study will contribute to a better understanding of the selection of appropriate charge schemes for SACs design. Mulliken population analysis ; Atom (system on chip) ; Charge (physics) ; Chemical physics ; Partial charge ; Substrate (aquarium) ; Electronic structure ; Chemistry ; Materials science ; Density functional theory ; Computational chemistry ; Physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Shaswat Mohanty | James Stevenson | Andrea Browning | Leif D. Jacobson | Karl Leswing | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: Research Square (preprint, 2023-01-18)
Field: Materials Science — Materials Chemistry

Development of Scalable and Generalizable Machine Learned Force Field for Polymers Scalability ; Field (mathematics) ; Computer science ; Force field (fiction) ; Artificial intelligence ; Human–computer interaction ; Machine learning ; Industrial engineering ; Engineering ; Mathematics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Injection Molding Process and Properties
Authors: Yulia F. Kovalenko | Ekaterina A. Shulaeva | Н. С. Шулаев
Source: Nanotechnologies in Construction A Scientific Internet-Journal (article, 2023-12-31)
Field: Materials Science — General Materials Science

Application of polyvinyl chloride for nanocomposites (analysis and optimization of quality indicators) Introduction. The application of nanocomposites in various industries has increased in recent years due to their unique properties and performance characteristics. Enhancing the quality performance of nanocomposites presents a formidable challenge, primarily attributable to the intricate interactions among their constituent elements. This paper presents an overview of the methods used to analyze and calculate the quality performance of nanocomposites using PVC as the base material. Methods and materials. To calculate technological parameters that are not directly measurable, we conducted a thorough analysis of literature sources related to the specific process under consideration. Through this analysis, we established appropriate relationships between empirical data in accordance with the fundamental principles of thermodynamics and mass transfer processes. In the course of the research, the method of neural networks was applied in order to describe the process of vinyl chloride polymerization carried out by suspension method. To solve this problem, a cascade network with forward propagation of the signal and backward propagation of the error was applied. Network composition: in the hidden layer -ten sigmoid neurons, in the output layer -two linear neurons. Results and Discussion. Throughout this research, it was observed that the heat flux during polymerization exhibits temporal variation, contingent upon the concentration level of the initiator. Further the dependencies obtained can be used in controlling the flow rate of refrigerant into the reactor cooling jacket, to ensure that the entire process is isothermal. It was found that by varying the stirrer speed, it is possible to change the particle size and hence the molecular weight distribution of polyvinyl chloride. The developed neural network was tested. The obtained results have minimal error and are close to the real values, from which we can conclude that the network is trained correctly and the dependence between the data is found. Conclusion. Dependencies linking physicochemical parameters of the technological process with the design features of the apparatus have been established. To maintain the quality of PVC, in particular the appropriate molecular weight distribution, a neural network (a cascade network with direct signal propagation and reverse error propagation, consisting of ten sigmoidal neurons in the hidden layer and two linear neurons in the output layer) was developed in MATLAB environment. The network was trained on a sample and tested on test values, which showed that the network predicts the outcome of the process with minimal tolerable error, with other parameters unchanged. Polyvinyl chloride ; Nanocomposite ; Quality (philosophy) ; Chloride ; Materials science ; Composite material ; Physics ; Metallurgy Material Properties and Applications
Authors: Progna Banerjee | Alexander S. Filatov | Xiaobing Zuo | Benjamin T. Diroll | Elena V. Shevchenko
Source: ChemRxiv (preprint, 2023-07-21)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into Copper(I) and Copper (II) Cation Exchange Reactions in CdSe Nanoplatelets In this study, we investigated the synthesis of copper selenide nanoplatelets (NPLs) through a cation exchange reaction (CER) in 5 monolayers thick CdSe NPLs using Cu(I) and Cu(II) precursors. We discovered that the exposure of CdSe NPLs to Cu(I) precursor led to the transformation of NPLs into Cu2−xSe while maintaining their nanoplatelet morphology. The replacement of Cd(II) with Cu(I) prevailed over the formation of doped structures. In the case of Cu(II) precursor, we observed that Cu(II) was first reduced to Cu(I) before being intercalated into the host lattice, resulting in synthesis of Cu2−xSe, similar to CER with Cu(I) precursors but without preservation of the initial morphology of NPLs. Interestingly, the presence of oxygen was found to facilitate the cation exchange processes in CdSe NPLs, whereas a nitrogen atmosphere suppressed the CER. Despite the similar ionic sizes of Cu(I) and Cu(II), the substitution of Cd(II) with Cu(II) was found to be challenging, possibly due to the involvement of redox processes resulting in the significant deterioration of initial CdSe NPLs. We demonstrate that CER can achieve near-complete substitution of cadmium atoms with monovalent copper under room temperature. Understanding the processes involved in CERs is crucial for engineering more complex structures, such as high entropy nanoparticles involving cation exchange with different oxidation states and development of material synthesis using machine learning and artificial intelligence approaches. Copper ; Selenide ; Nanoparticle ; Chemistry ; Redox ; Ion exchange ; Oxygen ; Inorganic chemistry ; Ionic bonding ; Materials science ; Selenium ; Ion ; Nanotechnology Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications