jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daria Torodii | Jacob B. Holmes | Pinelopi Moutzouri | Sten O. Nilsson Lill | Manuel Cordova | Arthur C. Pinon | Kristof Grohe | Sebastian Wegner | Okky Dwichandra Putra | Stefan Norberg | Anette Welinder | Staffan Schantz | Lyndon Emsley
Source: Faraday Discussions (article, 2024-07-17)
Field: Chemistry — Spectroscopy

Crystal structure validation of verinurad <i>via</i> proton-detected ultra-fast MAS NMR and machine learning The recent development of ultra-fast magic-angle spinning (MAS) (>100 kHz) provides new opportunities for structural characterization in solids. Here, we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of <sup>1</sup>H resolution improvement at ultra-fast MAS and use solely <sup>1</sup>H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to <sup>13</sup>C-detected experiments, without the need to employ dynamic nuclear polarization. Proton ; Crystal structure ; Crystallography ; Materials science ; Chemistry ; Physics ; Nuclear physics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Solid-state spectroscopy and crystallography
Authors: Yifei Zhu | Mengge Li | Chao Xu | Zhenggang Lan
Source: Scientific Data (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research. Excited state ; Quantum chemistry ; Ground state ; Quantum chemical ; Chemistry ; Molecule ; Volume (thermodynamics) ; Quantum ; Chemical physics ; Computational chemistry ; Atomic physics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Xiaojie Wu | Qiming Sun | Zhichen Pu | Tianze Zheng | Wenzhi Ma | Yan Wen | Yu Xia | Zhengxiao Wu | Mian Huo | Xiang Li | Weiluo Ren | Sheng Gong | Yumin Zhang | Weihao Gao
Source: arXiv (Cornell University) (preprint, 2024-04-15)
Field: Materials Science — Materials Chemistry

Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https: //github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionality including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform which we demonstrate in this work through a range of tests. When performing DFT calculations on modern GPU platforms, GPU4PySCF delivers 30 times speedup over a 32-core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium-ion batteries, and accelerating neural-network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is natural choice that we can now recommend for many industrial quantum chemistry applications. Python (programming language) ; Computer science ; Speedup ; Computational science ; Quantum chemical ; Implementation ; Density functional theory ; Parallel computing ; Computer architecture ; Chemistry ; Operating system ; Software engineering ; Computational chemistry ; Molecule Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yuval Elbaz | Maytal Caspary Toroker
Source: Scientific Reports (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

From density functional theory to machine learning predictive models for electrical properties of spinel oxides This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of band gap and conductivity. By altering the composition of spinel oxides, the model was able to predict high conductivity for spinels with high nickel content and to match experimental trends for manganese cobalt spinels. The ability to predict material properties is especially important in energy conversion devices such as batteries and supercapacitors where redox reactions take place. Spinel ; Conductivity ; Materials science ; Manganese ; Density functional theory ; Electrical resistivity and conductivity ; Electronic structure ; Cobalt ; Band gap ; Computer science ; Chemistry ; Optoelectronics ; Computational chemistry ; Physical chemistry ; Metallurgy ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Copper-based nanomaterials and applications
Authors: Changsheng Zhu | Wenjing Dong | Zihao Gao | Lijun Wang | Guangzhao Li
Source: Computational Materials Science (article, 2024-03-23)
Field: Materials Science — Materials Chemistry

Deep Potential fitting and mechanical properties study of MgAlSi alloy Alloy ; Materials science ; Metallurgy MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Yazhou Wang | Changquan Wu | Wenjing Ji | Yao Wu | Shangquan Zhao | Xuerui Yang | Yong Li | Naigen Zhou
Source: Journal of Colloid and Interface Science (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Machine learning-assisted precision inverse design research of ternary cathode materials: A new paradigm for material design Ternary operation ; Nanotechnology ; Cathode ; Block (permutation group theory) ; Computer science ; Process engineering ; Systems engineering ; Materials science ; Engineering ; Electrical engineering ; Mathematics ; Programming language Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Nabil Zerrouki | Amina Zouina Ait Djafer | Fouzi Harrou | S. Lafane | S. Abdelli-Messaci | Ying Sun
Source: Measurement (article, 2024-04-27)
Field: Materials Science — Polymers and Plastics

Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films Artificial intelligence ; Convolutional neural network ; Machine learning ; Thresholding ; Segmentation ; Computer science ; Deep learning ; Pattern recognition (psychology) ; Artificial neural network ; Materials science ; Image (mathematics) Transition Metal Oxide Nanomaterials ; Photoacoustic and Ultrasonic Imaging ; Infrastructure Maintenance and Monitoring
Authors: Nihang Fu | Lai Wei | Jianjun Hu
Source: The Journal of Physical Chemistry Letters (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction Deep learning models have been widely used for high-performance material property prediction. However, training such models usually requires a large amount of labeled data, which are usually unavailable. Self-supervised learning (SSL) methods have been proposed to address this data scarcity issue. Herein, we present DSSL, a physics-guided dual SSL framework, for graph neural network-based material property prediction, which combines node masking-based generative SSL with atomic coordinate perturbation-based contrastive SSL strategies to capture local and global information about input crystals. Moreover, we achieve physics-guided pretraining by using the macroproperty (e.g., elasticity)-related microproperty prediction of atomic stiffness as an additional pretext task. We pretrain our DSSL model on the Materials Project database and fine-tune it with 10 material property data sets. The experimental results demonstrate that teaching neural networks some physics using the SSL strategy can afford ≤26.89% performance improvement compared to that of the baseline models. Computer science ; Property (philosophy) ; Artificial neural network ; Artificial intelligence ; Machine learning ; Graph ; Deep learning ; Graphics ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: André Nicolle | Sili Deng | Matthias Ihme | Nursulu Kuzhagaliyeva | Emad Al Ibrahim | Aamir Farooq
Source: Journal of Chemical Information and Modeling (review, 2024-01-29)
Field: Materials Science — Materials Chemistry

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment. Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Context (archaeology) ; Chemical space ; Biological system ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shahed Haghiri | Claudia Viquez Rojas | Sriram Bhat | Olexandr Isayev | Lyudmila V. Slipchenko
Source: Journal of Chemical Theory and Computation (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials Deep learning Neural Networks (NN) have been developed in the field of molecular modeling for the purpose of circumventing the high computational cost of quantum-mechanical calculations while rivaling their accuracies. Although these networks have found great success, they generally lack the ability to accurately describe long-range interactions, which makes them unusable for extended molecular systems. Herein, we provide a method for partially retraining the deep learning general-use neural network ANI, in which the long-range interactions are represented via atomic electrostatic potentials. The electrostatic potentials, generated with polarizable effective fragment potentials (EFP), are used as an additional input feature for the network. This new ANI/EFP network can predict solute-solvent interaction energies on a trained data set with a kcal/mol accuracy. It also shows promise in predicting the interaction energies of a solute in solvent environments that have not been included in a training data set. The proposed protocol can be taken as an example and further developed, leading to highly accurate and transferable neural network potentials capable of handling long-range interactions and extended molecular systems. Fragment (logic) ; Range (aeronautics) ; Computer science ; Artificial neural network ; Biological system ; Materials science ; Artificial intelligence ; Biology ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Valentina Rein | Hao Gao | Hendrik H. Heenen | Wissal Sghaier | Anastasios C. Manikas | Christos Tsakonas | Mehdi Saedi | Johannes T. Margraf | Costas Galiotis | G. Renaud | Oleg Konovalov | Irene M. N. Groot | Karsten Reuter | Maciej Jankowski
Source: ACS Nano (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

<i>Operando</i> 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 <i>in situ</i> radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH<sub>4</sub>-to-H<sub>2</sub> 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 ; Chemical engineering ; Copper ; Chemical physics ; Chemistry ; Organic chemistry ; Metallurgy Graphene research and applications ; Diamond and Carbon-based Materials Research ; Thermal properties of materials
Authors: Huiju Lee | Yi Xia
Source: Applied Physics Letters (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force–displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science. Phonon ; Interatomic potential ; Condensed matter physics ; Harmonic ; Materials science ; Physics ; Statistical physics ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Linfeng Yu | Kexin Dong | Qi Yang | Yi Zhang | Zheyong Fan | Xiong Zheng | Huimin Wang | Zhenzhen Qin | Guangzhao Qin
Source: npj Computational Materials (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport The physical/chemical properties undergo significant transformations in the different states arising from phase transition. However, due to the lack of a dynamic perspective, transitional mesophases are largely underexamined, constrained by the high resource burden of first principles. Here, using molecular dynamics (MD) simulations empowered by the machine-learning potential, we proffer an innovative paradigm for phase transition: regulating the thermal transport properties via the transitional mesophase triggered by a uniaxial force field. We investigate the mechanical, electrical, and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase transition. Notably, we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition point. Through machine-learning-driven MD simulations, we achieved high-precision atomic-level simulations of Janus-graphene. The results show that thermal vibration-induced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal resistance. The investigation not only endows us with a novel perspective on mesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties. Mesophase ; Phonon ; Anisotropy ; Condensed matter physics ; Thermal ; Materials science ; Transition (genetics) ; Physics ; Chemistry ; Liquid crystal ; Thermodynamics ; Optics Thermal properties of materials ; Force Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Xi Zhang | Yutian Zhang | Yun-Peng Wang | Yiran Li | Shixuan Du | Yuyang Zhang | Sokrates T. Pantelides
Source: Physical review. B./Physical review. B (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Structural and mechanical properties of monolayer amorphous carbon and boron nitride Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic Monte Carlo (kMC) simulations using machine-learning potentials with density-functional theory-level accuracy. An intuitive order parameter, namely, the areal fraction ${F}_{\mathrm{x}}$ occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that ${F}_{\mathrm{x}}$ captures the essence of the DOD: Samples with the same ${F}_{\mathrm{x}}$ but different sizes and arrangements of crystallites, obtained using two distinct kMC procedures, have virtually identical radial distribution functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are mainly determined by ${F}_{\mathrm{x}}$ and are insensitive to the sizes and specific arrangements and to some extent the numbers and area distributions of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the continuous random network region and to be influenced by crystallites in distinct ways that toughen the material. The present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for two-dimensional materials. Crystallite ; Monolayer ; Boron nitride ; Amorphous solid ; Materials science ; Characterization (materials science) ; Molecular dynamics ; Crystallography ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Physics ; Chemistry ; Metallurgy Graphene research and applications ; Thermal properties of materials ; Diamond and Carbon-based Materials Research
Authors: Fenfei Wei | Bingqing Ge | Peipei Dong | Qiang Wan | Xixi Hu | Sen Lin
Source: Science China Materials (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Uncovering the active sites of single atom-doped rutile oxides during methane activation by data-driven approach Rutile ; Methane ; Doping ; Atom (system on chip) ; Materials science ; Chemical engineering ; Mineralogy ; Chemistry ; Computer science ; Optoelectronics ; Engineering ; Embedded system ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science