jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilia Kevlishvili | R. Michel | Aaron Garrison | Jacob Toney | Husain Adamji | Haojun Jia | Yuriy Román‐Leshkov | Heather J. Kulik
Source: Faraday Discussions (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure-property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure-property relationships with machine learning. Computer science ; Relevance (law) ; Property (philosophy) ; Chemical space ; Artificial intelligence ; Natural language processing ; Transition (genetics) ; Space (punctuation) ; Machine learning ; Database ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Maurits Vuijk | Gianmarco Ducci | Luis Sandoval | Markus Ruben Pietsch | Karsten Reuter | Thomas Lunkenbein | Christoph Scheurer
Source: Microscopy and Microanalysis (article, 2024-12-07)
Field: Materials Science — Materials Chemistry

Physics-Based Synthetic Data Model for Automated Segmentation in Catalysis Microscopy In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model. We study environmental scanning electron microscopy (ESEM) data collected from isopropanol oxidation to acetone over cobalt oxide as an example. Upon applying a temperature program during the reaction a phase transition occurs, reducing the catalyst selectivity toward acetone. This is accompanied on the micrometer ESEM scale by the formation of cracks between the pores of the catalyst surface. We aim to generate synthetic data to train a neural network capable of semantic segmentation (pixel-wise labeling) of this ESEM data. This analysis will lead to insights into this phase transition. To generate synthetic data that approximates this transition, our algorithm composes the ESEM images of the room-temperature catalyst with dynamically evolving synthetic cracks satisfying physical construction principles, gathered from qualitative knowledge accessible in the ESEM data. We mimic the surface crack growth propagation along surface paths, avoiding close vicinity to nearby pores. This physics-based approach results in a lowered rate of false positives compared to a random approach. Environmental scanning electron microscope ; Materials science ; Catalysis ; Segmentation ; Synthetic data ; Computer science ; Biological system ; Nanotechnology ; Artificial intelligence ; Chemistry ; Scanning electron microscope ; Composite material ; Organic chemistry ; Biology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Takuya Uto
Source: Polymer Journal (article, 2024-10-03)
Field: Materials Science — Biomaterials

Atomistic simulations of polysaccharide materials for insights into their crystal structure, nanostructure, and dissolution mechanism Abstract Crystalline polysaccharides are abundant in nature and can be transformed into highly functional materials. However, the molecular basis for the formation of higher-order structures remains unclear. Computer simulation is an advanced tool for modeling macromolecular structures, and the atomistic simulations provide valuable information on the crystalline polysaccharides. Fiber deformation, crystalline transition, and novel nanostructures of cellulose were characterized through molecular dynamics simulations and density functional theory calculations of models of molecular chain sheets extracted from the crystal structure of the cellulose polymorphs. Extended ensemble molecular dynamics simulations were applied to analyze the artificial crystal structure of non-natural amylose analog polysaccharides, revealing the hexagonal packing of double helices through the self-assembly of molecular chains dispersed in aqueous solution. Dissolution simulations of the cellulose and chitin crystalline fibers revealed that the anions of ionic liquids, with their solvation power, played a key role in the cleavage of intermolecular hydrogen bonds in the crystal structure, whereas the cations contributed to irreversible molecular chain dispersion. The good correlation between the actual solubility of polysaccharides and the predicted number of intermolecular hydrogen bonds prompted the development of a platform that combined simulations and machine learning for high-throughput screening of solvents for cellulose and chitin. Molecular dynamics ; Intermolecular force ; Hydrogen bond ; Dissolution ; Cellulose ; Solvation ; Materials science ; Crystal structure ; Macromolecule ; Crystal (programming language) ; Solubility ; Ionic bonding ; Chemical physics ; Aqueous solution ; Chemistry ; Crystallography ; Computational chemistry ; Molecule ; Organic chemistry Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Nanocomposite Films for Food Packaging
Authors: Lulu Li | Xiaofang Xu | Mao Ye | Zhongmin Liu
Source: The Journal of Physical Chemistry C (review, 2024-12-24)
Field: Chemistry — Inorganic Chemistry

Toward Realistic Simulations of Zeolite Catalytic Processes: A Mini Review Zeolites are crucial in industrial catalysis, renowned for their unique microporous structures and versatile catalytic properties. However, accurately simulating zeolite-catalyzed processes poses significant challenges due to their spatiotemporal complexity, which requires capturing both atomic-level interactions and macroscopic phenomena. This review examines recent advancements in realistic simulations of zeolite catalytic processes, focusing on techniques such as machine learning potentials (MLPs), enhanced sampling methods, and kinetic Monte Carlo (KMC) simulations. These computational strategies have substantially improved the accuracy and efficiency of catalytic reaction simulations, addressing the traditional limitations associated with complex systems like zeolites. MLPs offer precise potential energy surfaces at lower computational costs, enabling extended molecular dynamics simulations. Enhanced sampling techniques, including umbrella sampling and metadynamics, effectively explore rare events and complex energy landscapes, although their success depends on the careful selection of collective variables (CVs). KMC simulations further enhance our understanding by modeling long-term molecular events, such as diffusion and reaction kinetics, at larger spatial and temporal scales. Despite notable progress, challenges remain, particularly regarding CV selection and KMC’s reliance on accurate first-principles data. The integration of machine learning approaches, such as automated CV selection and transfer learning for MLP refinement, presents promising solutions to these issues. This review highlights these advancements and their potential to revolutionize the study of zeolite catalytic processes, bridging the gap between theoretical modeling and experimental observations and contributing to the design of more effective and sustainable catalysts. Zeolite ; Catalysis ; Computer science ; Environmental science ; Chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Dario Massa | Efthimios Kaxiras | Stefanos Papanikolaou
Source: Acta Materialia (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Alloy informatics through ab initio charge density profiles: Case study of hydrogen effects in face-centred cubic crystals Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose ‘alloy informatics’ as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centred cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects. Materials science ; Cubic crystal system ; Ab initio ; Alloy ; Charge (physics) ; Hydrogen ; Face (sociological concept) ; Condensed matter physics ; Crystallography ; Chemical physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Oliver Pereira | Marcel Ruth | Dennis Gerbig | Raffael C. Wende | Peter R. Schreiner
Source: Journal of the American Chemical Society (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔ<i>G</i><sup>‡</sup> of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee). This translates into a >50% improvement in relative Δ<i>G</i><sup>‡</sup> from 0.9 to 1.4 kcal mol<sup>-1</sup>. We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a key-intermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model. Our results highlight the synergy of synthetic chemistry and data-centric approaches and provide a blueprint for future catalyst optimization. Chemistry ; Catalysis ; Selectivity ; Enantiomeric excess ; Supramolecular chemistry ; Substrate (aquarium) ; Graph ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Enantioselective synthesis ; Computer science ; Theoretical computer science ; Molecule Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Shuqin Wang | Qiongxiong Ma | Zhongchao Wei | Ruihuan Wu | Wen Ding | Jianping Guo
Source: Physica Scripta (article, 2024-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning-assisted design of multi-degree-of-freedom metamaterial absorber Abstract In recent years, the development of metamaterial absorbers (MAs) based on deep learning methods has become a popular research topic. Nevertheless, conventional analytical approaches have limitations, leading to MAs characterized by low-degree-of-freedom cell structures, thereby restricting their absorption characteristics. In this study, we propose a multi-degree-of-freedom MA with multiple variable parameters in terms of structures, materials, and number of layers, which are distinguished by coding. Additionally, deep learning models, including a fully connected neural network, recurrent neural network, AlexNet, and residual neural network, are designed to predict structures and absorption spectra. We compare the prediction accuracy of these four neural network models and identify the optimal network model for spectral prediction and on-demand design. Using trained neural network models, we successfully designed broadband, dual-band, and single-band MAs in on-demand design. Compared with other design methods, this method provides a greater degree of freedom in device design, which allows the design of MAs to have more absorption characteristics Artificial neural network ; Computer science ; Broadband ; Metamaterial ; Deep learning ; Residual ; Degree (music) ; Artificial intelligence ; Algorithm ; Materials science ; Telecommunications ; Acoustics ; Physics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Rongzhi Dong | Nihang Fu | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: The Journal of Physical Chemistry A (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Generative Design of Inorganic Compounds Using Deep Diffusion Language Models Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria, such as charge neutrality, balanced electronegativity, synthesizability, and mechanical stability. In response to this formidable task, we introduce a deep-learning-based generative model for material composition and structure design by learning and exploiting explicit and implicit chemical knowledge. Our pipeline first uses deep diffusion language models as the generator of compositions and then applies a template-based crystal structure prediction algorithm to predict their corresponding structures, which is then followed by structure relaxation using a universal graph neural network-based potential. Density functional theory (DFT) calculations of the formation energies and energy-above-the-hull analysis are used to validate new structures generated through our pipeline. Based on the DFT calculation results, six new materials, including Ti<sub>2</sub>HfO<sub>5</sub>, TaNbP, YMoN<sub>2</sub>, TaReO<sub>4</sub>, HfTiO<sub>2</sub>, and HfMnO<sub>2</sub>, with formation energy less than zero have been found. Remarkably, among these, four materials, namely, Ti<sub>2</sub>HfO<sub>5</sub>, TaNbP, YMoN<sub>2</sub>, and TaReO<sub>4</sub>, exhibit an e-above-hull energy of less than 0.3 eV. These findings have proved the effectiveness of our approach. Generative grammar ; Diffusion ; Computer science ; Natural language processing ; Artificial intelligence ; Linguistics ; Physics ; Philosophy ; Thermodynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Kai Chen | Riyi Yang | Zhefeng Wang | Wuyan Zhao | Youmin Xu | Huaijun Sun | Chao Zhang | Songyou Wang | Kai-Ming Ho | Cai‐Zhuang Wang | Wan-Sheng Su
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the phase change and structure of carbon using a deep learning interatomic potential Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though <i>ab initio</i> molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C<sub>60</sub> crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy. Interatomic potential ; Chemical physics ; Phase change ; Carbon fibers ; Phase (matter) ; Materials science ; Engineering physics ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Shivani Bhawsar | Eui‐Hyeok Yang
Source: Journal of Physics D Applied Physics (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Recent advances in machine learning and deep learning-enabled studies on transition metal dichalcogenides Abstract The machine learning and deep learning (ML/DL) techniques have significantly advanced the understanding and utilization of transition metal dichalcogenides (TMDs) by enabling efficient analysis, prediction, and optimization of their properties. ML/DL methods permit rapid screening, optimization and analysis of two-dimensional (2D) material candidates, potentially accelerating the discovery and development of TMDs with desired electronic, optoelectronic, and energy storage properties. This review provides a comprehensive review of ML/DL methods to enhance 2D materials research via the optimization of synthesis conditions, interpretation of complex data sets, and the use of generative adversarial networks and variational autoencoders for innovative material design and image processing tasks. Furthermore, it highlights the potential of ML/DL techniques in predicting and tailoring the electronic, optical, and mechanical properties of 2D materials to meet specific application requirements. Nanotechnology ; Deep learning ; Transition metal ; Materials science ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Supramolecular Self-Assembly in Materials ; Organoselenium and organotellurium chemistry
Authors: Bilvin Varughese | Sukriti Manna | Troy D. Loeffler | Rohit Batra | Mathew J. Cherukara | Subramanian K. R. S. Sankaranarayanan
Source: ACS Applied Materials & Interfaces (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning <i>via</i> retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design. Workflow ; Flexibility (engineering) ; Nanotechnology ; Molecular dynamics ; Computer science ; Multiscale modeling ; Materials science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Electronic and Structural Properties of Oxides