jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paul Guibourg | Léo Dontot | P.-M. Anglade | B. Gervais
Source: Journal of Chemical Theory and Computation (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

DFTB Simulation of Charged Clusters Using Machine Learning Charge Inference We present a modification to self-consistent charge density functional-based tight binding (SCC-DFTB), which allows computation based on approximate atomic charges. We obtain these charges by means of a machine learning (ML) process that combines a Coulomb model with a neural network. This allows us to avoid the SCC cycles in the SCC-DFTB calculation while maintaining its accuracy. The main input of the model is the atomic positions characterized by a set of atom-centered symmetry functions. The charge inference from our ML algorithm is as close as 10<sup>-2</sup> units of charge from the exact SCC solution. Our ML-DFTB approach provides a good approximation of the density matrix and of the energy and forces with only a single diagonalization. This is a significant computational saving with respect to the complete SCC algorithm, which allows us to investigate a bigger ensemble of atoms. We show the quality of our approach in the case of charged silicon carbide (SiC) clusters. The ML-DFTB potential energy surface (PES) mimics the SCC-DFTB PES rather well, despite its simplicity. This allows us to obtain the same geometric structure ordering with respect to energy for small clusters. The dissociation barriers for ion emission are well-reproduced, which opens the way to investigating ion field emission and charged cluster stability. The ML-DFTB approach is obviously not limited to charged clusters or SiC materials. It opens a new route to investigate larger clusters than those investigated by standard SCC-DFTB, as well as surface and solid-state chemistry at the atomic level. Tight binding ; Cluster (spacecraft) ; Charge (physics) ; Algorithm ; Inference ; Computer science ; Molecular physics ; Statistical physics ; Physics ; Materials science ; Chemistry ; Computational chemistry ; Electronic structure ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Yunhua Lu | Jintao Yu | Qingwei Zhang | Junan Zhang | Chao Zhang | Qiuyan Bi
Source: Materials Today Communications (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Data-driven deep learning prediction of boron-doped graphene work function Materials science ; Graphene ; Boron ; Doping ; Work (physics) ; Work function ; Nanotechnology ; Function (biology) ; Optoelectronics ; Mechanical engineering ; Organic chemistry Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Mingzi Sun | Bolong Huang
Source: Advanced Energy Materials (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Direct Machine Learning Predictions of C<sub>3</sub> Pathways (Adv. Energy Mater. 13/2024) Machine Learning In article number 2400152, Bolong Huang and Mingzi Sun demonstrated the first-principles machine learning method to directly predict the reaction trends towardsmulti-carbon products for the first time by electroreduction of CO2, which is realized simply based on the database constructed by previous density functional theory calculations, supplying fast screening strategies for advanced atomic catalysts in the future. Materials science ; Energy (signal processing) ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science
Authors: Angelo Frei | Markus Orsi
Source: ChemRxiv (preprint, 2024-10-17)
Field: Materials Science — Surfaces, Coatings and Films

ELECTRUM: An Electron Configuration-based Universal Metal Fingerprint for Transition Metal Compounds Machine learning has experienced a drastic rise in interest and applications in all fields of chemistry, enabling researchers to leverage large chemical datasets to gain novel insights. The success of machine learning-driven projects in chemistry hinges on three key factors: access to robust and comprehensive datasets, a well-defined objective, and effective molecular representations that convert chemical structures into machine-readable formats. Transition metal complexes have lagged behind their organic counterparts on all three of these avenues. The large diversity of structures, coordination numbers and modes have made its translation to a machine-readable format an ongoing challenge. Here we introduce ELECTRUM, an electron configuration-based universal metal fingerprint for transition metal compounds. Its lightweight implementation enables the straightforward conversion of any transition metal complex into a simple fingerprint. Utilising a novel dataset generated from the Cambridge Structural Database (CSD), we demonstrate that ELECTRUM effectively captures the structural diversity of transition metal complexes. By plotting nearest-neighbor relationships in ELECTRUM space, we reveal meaningful clustering in two-dimensional representations. Furthermore, we use the ELECTRUM encoding to train machine learning models on the prediction of metal complex coordination numbers from ligand structures and metal identity alone. We show that on a subset of this data, we can train models to predict the oxidation state of metal complexes. These case studies showcase the potential of ELECTRUM as an easy-to-implement fingerprint for metal complexes. We rely on the community to further test, validate, and improve it. Fingerprint (computing) ; Transition metal ; Metal ; Electron ; Electron configuration ; Materials science ; Chemical physics ; Chemistry ; Atomic physics ; Crystallography ; Metallurgy ; Physics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Corrosion Behavior and Inhibition
Authors: Harshit Sharma | Gaurav Arora | Raj Kumar | Suman Debnath | Suchart Siengchin
Source: Research Square (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Analysis of Hardness in Polypropylene/Carbon Nanotube and Low-DensityPolyethylene/Carbon Nanotube Composites Carbon nanotube ; Polypropylene ; Composite material ; Materials science ; Nanotube ; Carbon nanotube metal matrix composites ; Carbon fibers ; Composite number Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Daniela Andrade Damasceno | Camilo Augusto Fernandes Salvador | Bruno Fedosse Zornio | Caetano R. Miranda
Source: Materials Today Communications (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Multiscale exploration of Ti-Nb-Zr-based alloys for enhanced bioimplant performance Materials science ; Metallurgy Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Advanced materials and composites
Authors: Guobin Zhao | Logan M. Brabson | Saumil Chheda | Ju Huang | Haewon Kim | K. Liu | K. Mochida | Thang Duc Pham | Prerna Prerna | Gianmarco Terrones | Sunghyun Yoon | Lionel Zoubritzky | François‐Xavier Coudert | Maciej Harańczyk | Heather J. Kulik | Mohamad Moosavi | David S. Sholl | Ilja Siepmann | Randall Q. Snurr | Yongchul G. Chung
Source: ChemRxiv (preprint, 2024-12-12)
Field: Chemistry — Inorganic Chemistry

CoRE MOF DB: a curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening We present an updated version of the CoRE MOF database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of a MOF structure. DDEC06 partial atomic charges of MOFs were assigned based on a machine learning model. Gibbs-Ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications. Workflow ; Computer science ; Metal-organic framework ; Core (optical fiber) ; Process (computing) ; Database ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Adsorption ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Max Hodapp
Source: Computational Materials Today (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

Exact average many-body interatomic interaction model for random alloys Understanding the physical origin of mechanisms in random alloys that lead to the formation of microstructures requires an understanding of their average behavior and, equally important, the role of local fluctuations around the average. Material properties of random alloys can be computed using direct simulations on random configurations. However, some properties are very difficult to compute, for others it is not even fully understood how to compute them using random sampling, in particular, interaction energies between multiple defects. To that end, we develop an atomistic model that does the averaging on the level of interatomic potentials. With such an average interatomic interaction model the problem of averaging via random sampling is bypassed since the problem of computing material properties on random configurations reduces to the problem of computing material properties on single crystals, the average alloy, which can be done using standard techniques.To be predictive, we develop our average model on the class of linear machine-learning interatomic potentials (MLIPs). To that end, using tools from higher-order statistics, we derive an analytic expansion of average many-body per-atom energies of linear MLIPs in terms of average tensor products of the feature vectors of the underlying machine-learning model that scales linearly with the size of an atomic neighborhood. In order to avoid forming higher-order tensors composed of products of feature vectors, we develop an implementation using equivariant tensor network (ETN) potentials, a class of linear MLIPs, that contracts the feature vectors to small-sized tensors, and then takes the average. We validate the average ETN potential by demonstrating the convergence of direct Monte Carlo simulations to the exact value for properties of the NbMoTaW medium-entropy alloy. Simulating average properties of dislocations has thus far only been possible with average embedded atom method potentials that, however, predict artificial polarized cores. Here, we show that average ETN potentials predict the compact screw dislocation core structure, in agreement with density functional theory. Hence, we anticipate that our model will become useful for understanding mechanistic origins of material properties and for developing predictive models of mechanical properties of random alloys. Statistical physics ; Materials science ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Thermal properties of materials
Authors: Natalya A. Grachyova | E. V. Fomin | Alexander E. Mayer
Source: Dynamics (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Theoretical Model of Structural Phase Transitions in Al-Cu Solid Solutions under Dynamic Loading Using Machine Learning The development of dynamic plasticity models with accounting of interplay between several plasticity mechanisms is an urgent problem for the theoretical description of the complex dynamic loading of materials. Here, we consider dynamic plastic relaxation by means of the combined action of dislocations and phase transitions using Al-Cu solid solutions as the model materials and uniaxial compression as the model loading. We propose a simple and robust theoretical model combining molecular dynamics (MD) data, theoretical framework and machine learning (ML) methods. MD simulations of uniaxial compression of Al, Cu and Al-Cu solid solutions reveal a relaxation of shear stresses due to a combination of dislocation plasticity and phase transformations with a complete suppression of the dislocation activity for Cu concentrations in the range of 30–80%. In particular, pure Al reveals an almost complete phase transition from the FCC (face-centered cubic) to the BCC (body-centered cubic) structure at a pressure of about 36 GPa, while pure copper does not reveal it at least till 110 GPa. A theoretical model of stress relaxation is developed, taking into account the dislocation activity and phase transformations, and is applied for the description of the MD results of an Al-Cu solid solution. Arrhenius-type equations are employed to describe the rates of phase transformation. The Bayesian method is applied to identify the model parameters with fitting to MD results as the reference data. Two forward-propagation artificial neural networks (ANNs) trained by MD data for uniaxial compression and tension are used to approximate the single-valued functions being parts of constitutive relation, such as the equation of state (EOS), elastic (shear and bulk) moduli and the nucleation strain distance function describing dislocation nucleation. The developed theoretical model with machine learning can be further used for the simulation of a shock-wave structure in metastable Al-Cu solid solutions, and the developed method can be applied to other metallic systems, including high-entropy alloys. Materials science ; Plasticity ; Solid solution ; Phase (matter) ; Solid mechanics ; Phase transition ; Dislocation ; Cubic crystal system ; Thermodynamics ; Relaxation (psychology) ; Statistical physics ; Condensed matter physics ; Physics ; Composite material ; Metallurgy Microstructure and mechanical properties ; Nuclear Materials and Properties ; Metal and Thin Film Mechanics
Authors: Salilaporn Nuankaew | Nattawut Boonyuen | Kanjana Thumanu | Natapol Pornputtapong
Source: Heliyon (article, 2024-02-23)
Field: Chemistry — Analytical Chemistry

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy <i>Aspergillus</i> section <i>Nigri</i> (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry. Systematics ; Fourier transform infrared spectroscopy ; Synchrotron radiation ; Section (typography) ; Fourier transform ; Fourier transform spectroscopy ; Analytical Chemistry (journal) ; Infrared ; Spectroscopy ; Synchrotron ; Infrared spectroscopy ; Materials science ; Physics ; Chemistry ; Optics ; Computer science ; Biology ; Chromatography ; Botany ; Organic chemistry ; Taxonomy (biology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mycotoxins in Agriculture and Food
Authors: Hongyang Dong | Simon D. M. Jacques | Keith T. Butler | Olof Gutowski | Ann‐Christin Dippel | Martin von Zimmerman | Andrew M. Beale | Antonis Vamvakeros
Source: ChemRxiv (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

A neural network reconstruction approach for obtaining parallax-free X-ray powder diffraction computed tomography data from large samples In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample's chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical Lithium-ion battery. Parallax ; Computed tomography ; Tomography ; X-ray ; Artificial neural network ; Powder diffraction ; Computer science ; Iterative reconstruction ; Image (mathematics) ; Computer graphics (images) ; Computer vision ; Artificial intelligence ; Optics ; Physics ; Radiology ; Medicine ; Nuclear magnetic resonance X-ray Diffraction in Crystallography ; Medical Imaging Techniques and Applications ; Advanced X-ray and CT Imaging
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2024-08-02)
Field: Materials Science — Materials Chemistry

Powerful and Reliable Prediction using Latent Variables of Experimentally Unobservable Reactions in Organic Synthesis In this study, a novel machine learning algorithm was designed to assist in the development of organic reactions. This algorithm addresses the complexities inherent in batch- type organic reactions, including the necessity for numerous experiments and the effects of intricate characteristics of reaction pathways. By integrating molecular relationships and actual yields from observable reactions, the algorithm is used to estimate untested yields via extrapolation. An approach based on Bayesian optimization and dual annealing optimization is employed to compute expected values and evaluate plausibility. The algorithm’s dual-loop 2 structure, incorporating latent variables and experimental values, maximizes the coefficient of determination. Physicochemical aspects of the algorithm are validated using natural bond orbital charges, and its utility in synthesizing perfluoroiodinated naphthalenes is demonstrated. The algorithm exhibits potential for application in predicting experimentally unobservable reactions, thereby advancing the field of synthetic organic chemistry. Unobservable ; Extrapolation ; Latent variable ; Observable ; Computer science ; Dual (grammatical number) ; Bayesian probability ; Algorithm ; Biological system ; Chemistry ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Econometrics ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: C. S. Green | Sharif Ahmed | Shashidhara Marathe | Liam Perera | Alberto Leonardi | Killian Gmyrek | Daniele Dini | James Le Houx
Source: arXiv (Cornell University) (preprint, 2024-09-11)
Field: Materials Science — Materials Chemistry

Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features, before spatially resolved X-ray diffraction computed tomography was carried out to characterise the homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development. Computer science ; Synchrotron ; Artificial intelligence ; Machine learning ; Physics ; Optics Machine Learning in Materials Science
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Powerful and Reliable Prediction using Latent Variables of Experimentally Unobservable Reactions in Organic Synthesis In this study, a novel machine learning algorithm was designed to assist in the development of organic reactions. This algorithm addresses the complexities inherent in batch- type organic reactions, including the necessity for numerous experiments and the effects of intricate characteristics of reaction pathways. By integrating molecular relationships and actual yields from observable reactions, the algorithm is used to estimate untested yields via extrapolation. An approach based on Bayesian optimization and dual annealing optimization is employed to compute expected values and evaluate plausibility. The algorithm’s dual-loop 2 structure, incorporating latent variables and experimental values, maximizes the coefficient of determination. Physicochemical aspects of the algorithm are validated using natural bond orbital charges, and its utility in synthesizing perfluoroiodinated naphthalenes is demonstrated. The algorithm exhibits potential for application in predicting experimentally unobservable reactions, thereby advancing the field of synthetic organic chemistry. Unobservable ; Extrapolation ; Latent variable ; Computer science ; Observable ; Dual (grammatical number) ; Algorithm ; Bayesian probability ; Biological system ; Mathematical optimization ; Chemistry ; Artificial intelligence ; Mathematics ; Econometrics ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography