jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tsubasa Koyama | Masanobu Nakayama | Judith Schuett | Steffen Grieshammer
Source: The Journal of Physical Chemistry C (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Dependence of Oxygen Diffusion on the La and Sr Configuration in Melilite-Type La <sub> 1+ <i>x</i> </sub> Sr <sub> 1– <i>x</i> </sub> Ga <sub>3</sub> O <sub> 7+ <i>x</i> /2 </sub> by Neural Network Potential Molecular Dynamics Simulation Oxide ion conductors, including O2 sensors and solid electrolytes for solid oxide fuel cells, are used in various applications. O2– vacancies are often the charge carriers in oxide ion conductors, including fluorite- and perovskite-type oxides. Recently, melilite-type La1+xSr1–xGa3O7+x/2 (0 ≤ x ≤ 1) materials have attracted attention because of their high O2– ion conductivity resulting from the migration of interstitial oxide ions formed for charge compensation. In this study, we evaluate the ionic conductivities of various cation configurations by molecular dynamics calculations using a universal neural network potential and investigate the relationship between conductivity and the cation configuration. The activation energy of ionic conduction is significantly dependent on the cation (La, Sr) configuration, even for the same composition. The disordered La/Sr arrangement tends to have a higher ionic conductivity than the energetically stable La/Sr arrangement. Such an effect is prominent in the medium-temperature range (800–1100 K). Melilite ; Oxygen ; Diffusion ; Materials science ; Analytical Chemistry (journal) ; Mineralogy ; Crystallography ; Condensed matter physics ; Physics ; Chemistry ; Thermodynamics ; Metallurgy ; Spinel Advancements in Solid Oxide Fuel Cells ; Nuclear materials and radiation effects ; Advanced Condensed Matter Physics
Authors: Fan Fan | Bin Xi | Xiaofeng Meng | Han Wang | Bowen Zhang | Qingbo Xu | Wei Feng | Xiaoman Wang | Hongbo Zhang | Feng Zhou | Zhenming Liu | Wenbiao Zhou | Bo Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-11)
Field: Materials Science — Materials Chemistry

Assessing Conformation Validity and Rationality of Deep Learning-Generated 3D Molecules Abstract Recent advancements in artificial intelligence (AI) have revolutionized the field of 3D molecule generation. However, the lack of effective evaluation methods for 3D conformations limits further improvements. Current techniques, in order to achieve the necessary speed for evaluating large numbers of AI-generated molecules, often rely on empirical geometric metrics that do not adequately capture various conformational anomalies, or on molecular mechanics (MM) energy metrics that exhibit low accuracy and lack atomic or torsional details. To address this gap, we propose a two-stage approach that achieves both high speed and quantum mechanical (QM) level accuracy. The first stage, termed the validity test, employs an AI-derived force field to identify atoms with elevated energy resulting from implausible neighboring environments. The second stage, known as the rationality test, utilizes a deep learning network trained on data with density functional theory (DFT) accuracy to detect rotatable bonds with high torsional energies. To demonstrate the functionality of our evaluation system, we applied our approach to five recently reported 3D molecule generation AI models across 102 targets in Directory of Useful Decoys-Enhanced (DUD-E) dataset. To facilitate accessibility for the academic community, our method is available as an open-source package. Rationality ; Artificial intelligence ; Psychology ; Computer science ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hongkui Zheng | Xiwen Chen | Abolfazl Razi | Kai He
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for Evaluation of Sequential Beam Damage of Beam-Sensitive Solid Electrolytes Convolutional neural network ; Materials science ; Beam (structure) ; Electrolyte ; Computer science ; Optics ; Artificial intelligence ; Chemistry ; Physics Solid-state spectroscopy and crystallography ; Ultrasonics and Acoustic Wave Propagation ; Ferroelectric and Piezoelectric Materials
Authors: Haonan Shi | Chaoren Shen | Zheng Huang | Kaiwu Dong
Source: ChemPhysChem (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Prediction of Hydroformylation A holistic model for predicting yield and linear selectivity for the hydroformylation of 1-octene was developed by machine learning using the experimental data collected from literatures. Physical organic chemistry (POC) parameter-based descriptors were adopted to represent pre-catalyst molecular features. Machine learning models trained respectively by Random Forests (RF) and Extreme Gradient Boost (XGBoost) algorithm showed remarkable performance on predicting linear selectivity. The method can also comprehensively map the correlation between reaction conditions and the results. The accuracy of the prediction results was verified by experimental data. Hydroformylation ; Selectivity ; Random forest ; Machine learning ; Artificial intelligence ; Experimental data ; Computer science ; Yield (engineering) ; Molecular descriptor ; Biological system ; Catalysis ; Chemistry ; Mathematics ; Quantitative structure–activity relationship ; Thermodynamics ; Organic chemistry ; Statistics ; Physics ; Rhodium Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organometallic Complex Synthesis and Catalysis
Authors: Alexandru B. Georgescu | Peiwen Ren | Bhatt, Harshul | Christopher Karpovich | Samanta, Bipasa | Elsa Olivetti | James M. Rondinelli
Source: arXiv (Cornell University) (preprint, 2024-03-21)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Learning Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the complexity of underlying mechanisms, and the challenges of experimental validation. Here, we present a physics-informed machine learning framework that accelerates the discovery of thermally driven MIT materials. Using a trained classifier, we screen a crystal structure database to identify promising candidates for higher fidelity simulations. We focus on Ca$_2$Fe$_3$O$_8$, CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and use density functional theory (DFT) to determine their electronic and magnetic ground states and assess their microscopic MIT mechanisms. We further apply machine learning regression models to estimate their transition temperatures and employ synthesis prediction tools to identify likely precursors and reaction routes. This integrated approach reduces the time and effort required to identify, understand, and synthesize new MIT materials, providing a generalizable pathway for accelerating correlated quantum materials discovery. Density functional theory ; Probabilistic logic ; Computer science ; Combinatorial synthesis ; Nanotechnology ; Materials science ; Microelectronics ; Throughput ; Artificial intelligence ; Machine learning ; Chemistry ; Combinatorial chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Shashank Shekhar Mishra | Upendra Kumar | Bincy Babu Raj | Hyunseok Ko | Thakur Prasad Yadav
Source: Physica B Condensed Matter (article, 2024-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifaceted investigation into the impact of Ga substitution for Al in V<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si115.svg" display="inline" id="d1e1184"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>MnAl inverse Heusler alloys Substitution (logic) ; Scalable Vector Graphics ; Computer science ; Computer graphics (images) ; World Wide Web ; Programming language Heusler alloys: electronic and magnetic properties ; Advanced Welding Techniques Analysis ; Intermetallics and Advanced Alloy Properties
Authors: Leif Seute | Eric Hartmann | Jan Stühmer | Frauke Gräter
Source: arXiv (Cornell University) (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Grappa -- A Machine Learned Molecular Mechanics Force Field Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting Grappa force field outperformstabulated and machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, RNA and - showcasing its extensibility to uncharted regions of chemical space - radicals at state-of-the-art MM accuracy. We demonstrate Grappa's transferability to macromolecules in MD simulations from a small fast folding protein up to a whole virus particle. Our force field sets the stage for biomolecular simulations closer to chemical accuracy, but with the same computational cost as established protein force fields. Field (mathematics) ; Molecular mechanics ; Computer science ; Force field (fiction) ; Physics ; Classical mechanics ; Artificial intelligence ; Mathematics ; Molecular dynamics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Yanliang Zhao | Wei Zhang | Lei Sun | Xiaoxian Li | Wei Deng | Liang Zhang
Source: Crystals (article, 2024-01-03)
Field: Chemistry — Inorganic Chemistry

Wulff Constructions for an Equilibrium MFI-Type Zeolite Shape Modelling under Different Conditions As an MFI-type zeolite, ZSM-5 zeolite has wide applications in industry, such as in the fine chemical, petrochemical, and coal chemical industries. However, shape control of ZSM-5 nanocrystals constitutes one of the major challenges of current nanotechnology. Here, the MFI framework structure was used as the theoretical model of pure silicon ZSM-5 to investigate the surface energy and Wulff shape. The models with different crystal surfaces were simulated by molecular dynamics (MD) with the assistance of machine learning potentials (MLPs). The factors influencing the crystal surface energy, such as temperature, pH, and ionic concentration, have been studied in detail. Depending on the calculated surface energies, the crystal surface morphology and its ratio were obtained by means of the Wulff theorem. The results show that the area in the equilibrium shape of the (110) surface is usually the largest, and its proportion varies with external conditions. A high temperature and high concentration of the aluminum source promoted the growth of the (110) crystal surface, and the theoretical value of the crystal surface ratio was as high as 90%. This study provides theoretical insight into the synthesis of zeolites with different morphologies of all-silicon or low-aluminum. Zeolite ; Crystal (programming language) ; Materials science ; Silicon ; Nanocrystal ; Ionic bonding ; Surface energy ; Aluminium ; Surface (topology) ; Nanotechnology ; Chemical physics ; Chemical engineering ; Catalysis ; Chemistry ; Composite material ; Metallurgy ; Geometry ; Ion Zeolite Catalysis and Synthesis ; Graphene research and applications ; Mesoporous Materials and Catalysis
Authors: Mingze Zheng | Brandon C. Bukowski
Source: ChemRxiv (preprint, 2024-02-19)
Field: Chemistry — Inorganic Chemistry

Probing the role of acid site distribution on water structure in aluminosilicate zeolites: insights from molecular dynamics Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals de-rived from bio-based feedstocks. Zeolites are porous catalysts used extensively due to their shape-selective adsorption and confinement interactions; However the kinetics of zeolite-catalyzed reactions are significantly impacted by the presence of water, which may affect product selectivity and intrinsic rate constants depending on transition state polarity. In this study, we employed machine learning force fields (MLFFs) to accelerate ab initio molecular dynamics (AIMD) simulations and enhance the phase space exploration of water configurations in a model Brønsted acid zeolite, H-AFI. We interrogated the structure of adsorbed water based on the Si/Al ratio and acid site distribution to disentangle the impact of acid site density and distribution on water matrix organization as a function of water loading. We integrated adsorption thermodynamics, vibrational spectroscopy simulations, and local density maps to interrogate the spatial orientation of adsorbed water clusters and their degree of hydrogen bonding. Our analysis unveiled the intricate interplay between zeolite structure, Brønsted acid site location, and water where spatially disparate acid sites nucleate extended clusters that span siliceous regions of the zeolite. We found that the length scale of ordered water regions is directly related to the Si/Al ratio and spatial distribution of Al sites. These findings provide insights into the molecular-level structure of water in microporous aluminosilicate micropores and demonstrate how acid sites can be used to control water activity which has applications to heterogeneous catalysis and adsorptive separations. Aluminosilicate ; Molecular dynamics ; Dynamics (music) ; Distribution (mathematics) ; Chemistry ; Chemical physics ; Computational chemistry ; Catalysis ; Organic chemistry ; Psychology ; Mathematics Zeolite Catalysis and Synthesis ; Chemical and Physical Properties in Aqueous Solutions ; Clay minerals and soil interactions
Authors: Anthony Onwuli | Keith T. Butler | Aron Walsh
Source: APL Machine Learning (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Ionic species representations for materials informatics High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and physical properties. We explore several methods for developing embedding vectors of elements decorated with oxidation states. Graphs generated from 110 160 crystals are used to train representations of 84 elements that form 336 species. Clustering these learned representations of ionic species in low-dimensional space reproduces expected chemical heuristics, particularly the separation of cations from anions. We show that these representations have enhanced expressive power for property prediction tasks involving inorganic compounds. We expect that ionic representations, necessary for the description of mixed valence and complex magnetic systems, will support more powerful machine learning models for materials. Ionic bonding ; Informatics ; Computer science ; Data science ; Ion ; Chemistry ; Engineering ; Electrical engineering ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Shengli Jiang | Adji Bousso Dieng | Michael A. Webb
Source: ChemRxiv (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

Property-Guided Generation of Complex Polymer Topologies Using Variational Autoencoders The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting and engineering polymer properties. Although machine learning is increasingly used in polymer science, applications to address architecturally complex polymers are nascent. Here, we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties. Following the construction of a dataset featuring 1,342 polymers with linear, cyclic, branch, comb, star, or dendritic structures, we employ a multi-task learning framework that effectively reconstructs and classifies polymer topologies while predicting their dilute-solution radii of gyration. This framework enables the generation of novel polymer topologies with target size, which is subsequently validated through molecular simulation. These capabilities are then exploited to contrast rheological properties of topologically distinct polymers with otherwise similar dilute-solution behavior. This research opens new avenues for engineering polymers with more intricate and tailored properties with machine learning. Network topology ; Polymer ; Computer science ; Topology (electrical circuits) ; Materials science ; Biomimetics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Takayoshi Yoshimura | Hiromoto Kato | Shunto Oikawa | Taichi Inagaki | Tetsunori Sugawara | T. Miyao | Takamitsu Matsubara | Hiroharu Ajiro | Mikiya Fujii | Yu‐ya Ohnishi | Miho Hatanaka
Source: ChemRxiv (preprint, 2024-03-13)
Field: Materials Science — Materials Chemistry

CopDDB: a descriptor database for copolymers and its application to the machine learning Polymer informatics, which involves the application of data-driven science to polymers, has attracted considerable interest. However, developing adequate descriptors for polymers, particularly copolymers, to facilitate machine learning models with limited datasets remains a challenge. To address this issue, we computed sets of parameters, including reaction energies and activation barriers of elementary reactions in the early stage of radical polymerization, for 2500 radical-monomer pairs derived from 50 commercially available monomers, and constructed an open database named “Copolymer Descriptor Database (CopDDB).” Furthermore, we built machine learning models using our descriptors as explanatory variables and physical properties such as the reactivity ratio, monomer conversion, monomer composition ratio, and molecular weight as objective variables. These models achieved high predictive accuracies, demonstrating the potential of our descriptors to advance the field of polymer informatics. Copolymer ; Computer science ; Database ; Artificial intelligence ; Materials science ; Composite material ; Polymer Machine Learning in Materials Science
Authors: Ryuhei Sato | Lewis J. Conway | Di Zhang | Chris J. Pickard | Kazuto Akagi | Kartik Sau | Hao Li | Shin‐ichi Orimo
Source: ChemRxiv (preprint, 2024-06-19)
Field: Materials Science — Materials Chemistry

Hydrogen Absorption Promoted by Surface Melting: Guidelines for High-Pressure Superhydride Synthesis Synthesis of novel inorganic materials involving molecular absorption is challenging owing to the control of interfacial reactions. Here, we reveal the kinetic role of applied pressure in the interfacial reaction. Specifically, we used machine-learning potential molecular dynamics simulations to investigate the initial stage of superhydride formation in calcium hydrides. Upon contact with high-pressure H2, the surface of CaH2 melts, leading to CaH4 formation. This surface melting proceeds via CaH4 liquid phase as an intermediate state. Therefore, the high pressure reduces not only the hydrogenation (CaH2 + H2 ↔ CaH4) enthalpy but also the activation energy of the reaction via liquid product phase. This advantage of high-pressure synthesis is applicable to any interfacial reactions involving molecular absorption, opening new avenue to inorganic material synthesis. From these thermodynamics, we also propose superhydride synthesis guidelines based on bulk properties: superhydride (product) melting temperature and pressure-dependent hydrogenation enthalpy, readily determined through supplementary calculations during structure prediction workflows. Absorption (acoustics) ; Enthalpy ; Phase (matter) ; Chemistry ; Thermodynamics ; High pressure ; Molecular dynamics ; Hydrogen ; Kinetic energy ; Surface pressure ; Materials science ; Chemical physics ; Physical chemistry ; Computational chemistry ; Organic chemistry Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Lei Geng | Y. R. Niu | Zhitao Xiao | Hua-Qing Yin
Source: Computational Materials Science (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

AGT: Enhancing many-body interactions in material property prediction Computer science ; Curse of dimensionality ; Benchmarking ; Encoding (memory) ; Representation (politics) ; Materials science ; Algorithm ; Biological system ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: A. Nayamadi Mahmoodabadi | M. Modarresi | A. Mogulkoc
Source: Applied Physics Letters (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Atomic fingerprints for high throughput screening of 2D monolayers In materials science, artificial intelligence is used to create machine learning models to predict material properties and discover different compounds. An atomic fingerprint is designed to reflect the structure and atomic features of 2D monolayer crystals. A neural network-based model is trained using fingerprints and the computational 2D materials database to predict 2D monolayers. The model predicted the formation energies of crystalline compounds, including some previously unexplored monolayers, which could be potential candidates for future technologies. Throughput ; Monolayer ; High-throughput screening ; Materials science ; Computer science ; Nanotechnology ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Alex J. Lee | Joshua A. Rackers | Shivesh Pathak | William P. Bricker
Source: PLoS ONE (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Building an ab initio solvated DNA model using Euclidean neural networks Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level. Ab initio ; Biomolecule ; Chemical physics ; Ab initio quantum chemistry methods ; Physics ; Quantum ; Molecule ; Euclidean geometry ; Statistical physics ; Quantum chemistry ; Computational chemistry ; Chemistry ; Nanotechnology ; Quantum mechanics ; Materials science ; Mathematics ; Geometry ; Supramolecular chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods