jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Davide Tisi | Federico Grasselli | Lorenzo Gigli | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2024-01-23)
Field: Materials Science — Materials Chemistry

Thermal conductivity of Li$_3$PS$_4$ solid electrolytes with ab initio accuracy The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable, due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this paper, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r$^2$SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state electrolyte, Li$_3$PS$_4$, in all its polymorphs ($α$, $β$, and $γ$). By comparing MD estimates with lattice methods on the low-temperature, nondiffusive $γ$-Li$_3$PS$_4$, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for nondiffusive phases. Finally, for the ion-conducting $α$ and $β$ phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases. Ab initio ; Thermal conduction ; Lattice (music) ; Chemical physics ; Thermal conductivity ; Thermodynamics ; Electrolyte ; Materials science ; Ionic conductivity ; Leverage (statistics) ; Fast ion conductor ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Computer science ; Organic chemistry ; Machine learning Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Jingwen Zhang | Junjie Zhang | Guo‐Qiang Bao | Zehan Li | Xiaobo Li | Te‐Huan Liu | Ronggui Yang
Source: Applied Physics Letters (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Prediction of phonon properties of cubic boron nitride with vacancy defects and isotopic disorders by using a neural network potential Cubic boron nitride (c-BN) is a promising ultra-wide bandgap semiconductor for high-power electronic devices. Its thermal conductivity can be substantially modified by controlling the isotope abundance and by the quality of a single crystal. Consequently, an understanding of the phonon transport in c-BN crystals, with both vacancy defects and isotopic disorders at near-ambient temperatures, is of practical importance. In the present study, a neural network potential (NNP) for c-BN has been developed, which has facilitated the investigation of phonon properties under these circumstances. As a result, the phonon dispersion and the three- and four-phonon scattering rates that were predicted with this NNP were in close agreement with those obtained from density-functional theory (DFT) calculations. The thermal conductivities of the c-BN crystals were also investigated, with boron (B) vacancies ranging from 0.0% to 0.6%, by using equilibrium molecular dynamics simulations based on the Green-Kubo formula. These simulations accurately capture vacancy-induced phonon softening, localized vibration modes, and phonon localization effects. As has previously been experimentally prepared, four isotope-modified c-BN samples were selected for analyses in the evaluation of the impact of isotopic disorders. The calculated thermal conductivities aligned well with the DFT benchmarks. In addition, the present study was extended to include a c-BN crystal with a natural abundance of B atoms, which also contained B vacancies. Reasonable thermal conductivities and vibrational characteristics, within the temperature range of 250–500 K, were then obtained. Vacancy defect ; Boron nitride ; Phonon ; Boron ; Materials science ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Physics ; Nuclear physics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials
Authors: Zhiyuan Li | Bo Zhao | Hongbin Zhang | Yixuan Zhang
Source: Faraday Discussions (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

High-throughput calculations and machine learning modeling of <sup>17</sup>O NMR in non-magnetic oxides The only NMR-active oxygen isotope, oxygen-17 (<sup>17</sup>O), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, <sup>17</sup>O solid-state NMR offers unique insights into local structures and finds significant applications in the studies of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of <sup>17</sup>O solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and CASTEP to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have chosen over 7100 binary, ternary, and quaternary compounds from the Materials Project database and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the <sup>17</sup>O NMR parameters using machine learning techniques, further enhancing our ability to predict and understand <sup>17</sup>O NMR parameters in oxide crystals. Throughput ; Workflow ; Computer science ; Materials science ; Nuclear magnetic resonance ; Physics ; Operating system Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Matthias Welsch | Steffen Hirte | Johannes Kirchmair
Source: Journal of Chemical Information and Modeling (article, 2024-09-25)
Field: Materials Science — Materials Chemistry

Deciphering Molecular Embeddings with Centered Kernel Alignment Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's efficacy depends on selecting a kernel that adequately captures the underlying properties of the compared models. The model analysis tool was designed for neural networks (NNs) with their invariance to data rotation in mind and has been successfully employed in various scientific domains. However, CKA has rarely been adopted in cheminformatics, partly because of the popularity of the random forest (RF) machine learning algorithm, which is not rotationally invariant. In this work, we present the adaptation of CKA that builds on the RF kernel to match the properties of RF. As part of the method validation, we show that the model analysis method is well-correlated with the prediction similarity of RF models. Furthermore, we demonstrate how CKA with the RF kernel can be utilized to analyze and explain the behavior of RF models derived from molecular and rooted fingerprints. Kernel (algebra) ; Computer science ; Computational biology ; Artificial intelligence ; Mathematics ; Biology ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Meng Zhang | Koki Hibi | Junya Inoue
Source: Physical review. B./Physical review. B (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Highly accurate and efficient potential for bcc iron assisted by artificial neural networks Atomic forces and energies, calculated by interatomic potential, are fundamental components of molecular dynamics (MD) and Monte Carlo (MC) simulations. Compared with traditional potentials, machine-learning (ML) potentials trained by using extensive density-functional theory databases exhibit high accuracy in predicting physical and chemical properties of materials, but their transferability often faces constraints. To address this limitation, physically informed neural network (PINN) potentials have been developed. These models synergistically combine the strengths of ML with physics-based bond-order interatomic potentials, aiming for both improved accuracy and broader applicability. However, a major limitation remains: the low performance of PINN potentials, hindering large-scale simulations. This work introduces a potential framework by incorporating an artificial neural network (ANN) into typical potential functions, which not only improves the transferability compared with the ANN potential, but also significantly improves the performance of ML potentials. The developed ANN assistant potential for body-centered cubic (bcc) iron demonstrates exceptional accuracy in property predictions while boasting remarkable computational efficiency. Its performance utilizing a single graphics processing unit (GPU) card overcomes both 12-message passing interface central processing unit -only ML potential and GPU-accelerated ML potential by achieving speedups of 201\ifmmode\times\else\texttimes\fi{} and 26\ifmmode\times\else\texttimes\fi{}, respectively. The proposed approach has a potential to provide a powerful way to develop high accurate and efficient potentials even in the other systems. Transferability ; Computer science ; Artificial neural network ; Interatomic potential ; Graphics processing unit ; Molecular dynamics ; Graphics ; Artificial intelligence ; Computational science ; Embedded atom model ; Density functional theory ; Machine learning ; Algorithm ; Chemistry ; Computational chemistry ; Parallel computing Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Chaewon Lee | Shuan Chen | Kai Tzu-iunn Ong | Jinyoung Yeo | Yousung Jung
Source: ChemRxiv (preprint, 2024-10-30)
Field: Materials Science — Materials Chemistry

Noise Analysis and Data Refinement for Chemical Reactions from US Patents via Large Language Models The extraction of chemical reactions from U.S. Patent and Trademark Office (USPTO) documents has enabled significant advancements in machine learning models for organic synthesis. While the USPTO dataset offers a large and diverse collection of reaction data, recent studies have identified issues such as inconsistent or missing chemical entries, impacting data quality. To address these challenges, we employed fine-tuned large language models (LLMs) to revisit experimental sections in the US patents, performing a comprehensive analysis of noisy reaction data. Our findings demonstrate that LLMs produce fewer false reactions compared to existing datasets and reveal that many reactions in US patents involve multiple experimental steps, previously overlooked by standard extraction methods. Our analysis suggests that untraceable references and erroneous chemical names are primary sources of data noise. We also identify reaction types with high susceptibility to these issues, recommending scientists avoid using those high-risk reaction data. Noise (video) ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Data Quality and Management
Authors: Ting Zong | Xia Liu | Xingyu Zhang | Qingsheng Yang
Source: The Journal of Chemical Physics (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Efficient characterization of double-cross-linked networks in hydrogels using data-inspired coarse-grained molecular dynamics model The network structure within polymers significantly influences their mechanical properties, including their strength, toughness, and fatigue resistance. All-atom molecular dynamics (AAMD) simulations offer a method to investigate the energy dissipation mechanism within polymers during deformation and fracture; Such an approach is, however, computationally inefficient when used to analyze polymers with complex network structures, such as the common chemically double-networked hydrogels. Alternatively, coarse-grained molecular dynamics (CGMD) models, which reduce the computational degrees of freedom by concentrating a set of adjacent atoms into a coarse-grained bead, can be employed. In CGMD simulations, a coarse-grained force field (CGFF) is a critical factor affecting the simulation accuracy. In this paper, we proposed a data-based method for predicting the CGFF parameters to improve the simulation efficiency of complex cross-linked network in polymers. Here, we utilized a typical chemically double-networked hydrogel as an example. An artificial neural network was selected, and it was trained with the tensile stress-strain data from the CGMD simulations using different CGFF parameters. The CGMD simulations using the predicted CGFF parameters show good agreement with the AAMD simulations and are almost fifty times faster. The data-inspired CGMD model presented here broadens the applicability of molecular dynamics simulations to cross-linked polymers and has the potential to provide insights that will aid the design of polymers with desirable mechanical properties. Molecular dynamics ; Self-healing hydrogels ; Materials science ; Polymer ; Toughness ; Force field (fiction) ; Computational chemistry ; Chemical physics ; Computer science ; Composite material ; Chemistry ; Polymer chemistry Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications ; Advanced Polymer Synthesis and Characterization
Authors: Zezhu Zeng | Zheyong Fan | Simoncelli, Michele | Chen Chen | Ting Liang | Yue Chen | G. Thornton | Bingqing Cheng
Source: arXiv (Cornell University) (preprint, 2024-07-26)
Field: Materials Science — Materials Chemistry

Lattice distortion leads to glassy thermal transport in crystalline Cs$_3$Bi$_2$I$_6$Cl$_3$ The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs$_3$Bi$_2$I$_6$Cl$_3$ is perplexing and lacking theoretical explanations. Here, we first experimentally measure such its thermal transport behavior from 20~K to 300~K, after synthesizing Cs$_3$Bi$_2$I$_6$Cl$_3$ single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs$_3$Bi$_2$I$_6$Cl$_3$ has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the Wigner formulation of thermal transport, we reproduce the experimental thermal conductivities based on lattice-distorted structures. This study thus provides a framework for predicting and understanding glassy thermal transport in materials with strong lattice disorder. Thermal ; Lattice (music) ; Materials science ; Condensed matter physics ; Distortion (music) ; Physics ; Thermodynamics ; Optoelectronics Phase-change materials and chalcogenides ; Glass properties and applications ; Optical properties and cooling technologies in crystalline materials
Authors: Kevin Rossi | Andrea Ruiz‐Ferrando | Dario Faust Akl | Victor Gimenez Abalos | Javier Heras‐Domingo | Romain Graux | Xiao Hai | Jiong Lu | Dario García-Gasulla | Núria López | Javier Pérez‐Ramírez | Sharon Mitchell
Source: Advanced Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Quantitative Description of Metal Center Organization and Interactions in Single‐Atom Catalysts (Adv. Mater. 5/2024) Single-Atom Catalysts In article number 2307991, Nuria López, Javier Pérez-Ramírez, Sharon Mitchell, and co-workers introduce open-access, machine-learning image analysis tools that can readily discriminate metal center organization in single-atom heterogeneous catalysts. The standardized approach rapidly detects features, including the fraction, size, and geometry of metal clusters present, offering valuable insights for confirming synthesis quality and advancing precision catalyst design. Materials science ; Center (category theory) ; Catalysis ; Atom (system on chip) ; Metal ; Nanotechnology ; Crystallography ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Materials Characterization Techniques ; Catalysis and Oxidation Reactions
Authors: Siqi Hu | Quan Li | Haowen Sun | Xueqian Lv | Miao Song | Yi Zhao | Ruiqian Zhang | Chonghong Zhang | Yao Shen | Yue Liu | Tongxiang Fan
Source: Materials Characterization (article, 2024-03-14)
Field: Materials Science — Surfaces, Coatings and Films

Introducing vacancy defects by rapid quenching in FCC metal for deep learning micrograph dataset Materials science ; Micrograph ; Vacancy defect ; Quenching (fluorescence) ; Metallurgy ; Metal ; Crystallography ; Composite material ; Scanning electron microscope ; Optics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Industrial Vision Systems and Defect Detection
Authors: Zetian Mao | Wenwen Li | Jethro Tan
Source: npj Computational Materials (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Dielectric tensor prediction for inorganic materials using latent information from preferred potential Abstract Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development. Existing machine learning models have focused on predicting scalar polycrystalline dielectric constants, neglecting the directional nature of dielectric tensors essential for material design. This study leverages multi-rank equivariant structural embeddings from a universal neural network potential to enhance predictions of dielectric tensors. We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs 2 Ti(WO 4 ) 3 (band gap E g = 2.93eV, dielectric constant ε = 180.90) and CsZrCuSe 3 (anisotropic ratio α r = 121.89). The results demonstrate our model’s accuracy in predicting dielectric tensors and its potential for discovering novel dielectric materials. Dielectric ; Tensor (intrinsic definition) ; Materials science ; Computer science ; Mathematics ; Pure mathematics ; Optoelectronics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Irene Caro-Campos | Marta María González-Barrios | Óscar J. Durá | Erik Fransson | José J. Plata | David Ávila‐Brande | Javier Fdez. Sanz | Jesús Prado‐Gonjal | Antonio M. Márquez
Source: Chemistry of Materials (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites The exploration of large chemical spaces in search of new thermoelectric materials requires the integration of experiments, theory, simulations, and data science. The development of high-throughput strategies that combine DFT calculations with machine learning has emerged as a powerful approach to discovering new materials. However, experimental validation is crucial to confirm the accuracy of these workflows. This validation becomes especially important in understanding the transport properties that govern the thermoelectric performance of materials since they are highly influenced by synthetic, processing, and operating conditions. In this work, we explore the thermal conductivity of Cu-based sulvanites by using a combination of theoretical and experimental methods. Previous discrepancies and significant variations in reported data for Cu<sub>3</sub>VS<sub>4</sub> and Cu<sub>3</sub>VSe<sub>4</sub> are explained using the Boltzmann Transport Equation for phonons and by synthesizing well-characterized defect-free samples. The use of machine learning approaches for extracting high-order force constants opens doors to charting the lattice thermal conductivity across the entire Cu-based sulvanite family-finding not only materials with κ <sub><i>l</i></sub> values below 2 W m<sup>-1</sup> K<sup>-1</sup> at moderate temperatures but also rationalizing their thermal transport properties based on chemical composition. Thermal conductivity ; Lattice (music) ; Materials science ; Thermal ; Statistical physics ; Conductivity ; Thermodynamics ; Condensed matter physics ; Physics ; Chemistry ; Physical chemistry ; Composite material Thermal Expansion and Ionic Conductivity ; Advanced Thermoelectric Materials and Devices ; Thermal and Kinetic Analysis
Authors: Hong Mao | Chenyang Xie | Jingwen Pan | Qingzheng Cao | Xiaohong Zhang | Yun Jun Luo | Yong Du | Ning Han
Source: Materials Today Communications (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

Spatiotemporal prediction of solidified dendrites based on convolutional long-short-term neural network Materials science ; Convolutional neural network ; Artificial neural network ; Term (time) ; Long-term prediction ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Computer science Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Iron and Steelmaking Processes
Authors: Jacob T. Sivak | Saeed S. I. Almishal | Mary Kathleen Caucci | Yueze Tan | Dhiya Srikanth | Matthew Furst | L. Q. Chen | Christina M. Rost | Jon‐Paul Maria | Susan B. Sinnott
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rock salt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery. Interatomic potential ; Entropy (arrow of time) ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Abu Bakar Siddique | Nasir Ali | Muhammad Hamraz | Saadut Ullah Khan | Shaukat Ali Khattak
Source: Computational Condensed Matter (article, 2024-06-09)
Field: Materials Science — Materials Chemistry

Prediction of magnetic nature of oxide compositions by using machine learning models Oxide ; Materials science ; Condensed matter physics ; Artificial intelligence ; Computer science ; Physics ; Metallurgy Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Geochemistry and Geologic Mapping