jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zongyi Ma | Zhiliang Pan
Source: Scientific Reports (article, 2023-07-15)
Field: Materials Science — Materials Chemistry

Efficient machine learning of solute segregation energy based on physics-informed features Machine learning models solute segregation energy based on appropriate features of segregation sites. Lumping many features together can give a decent accuracy but may suffer the curse of dimensionality. Here, we modeled the segregation energy with efficient machine learning using physics-informed features identified based on solid physical understanding. The features outperform the many features used in the literature work and the spectral neighbor analysis potential features by giving the best balance between accuracy and feature dimension, with the extent depending on machine learning algorithms and alloy systems. The excellence is attributed to the strong relevance to segregation energies and the mutual independence ensured by physics. In addition, the physics-informed features contain much less redundant information originating from the energy-only-concerned calculations in equilibrium states. This work showcases the merit of integrating physics in machine learning from the perspective of feature identification other than that of physics-informed machine learning algorithms. Curse of dimensionality ; Machine learning ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Energy (signal processing) ; Dimension (graph theory) ; Relevance (law) ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Zhuhao Wen | Hao-qin LIN | Weimin Chen | Kewu Bai | Lijun Zhang
Source: Transactions of Nonferrous Metals Society of China (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

High-throughput exploration of composition-dependent elasto-plastic and diffusion properties of refractory multi-element Ti-Nb-Zr-W alloys A combinatorial material synthesis and analysis method can offer a great promise of mapping the elasto-plastic and diffusion properties in refractory multi-element alloys of Ti-Nb-Zr-W system with various compositions. Six groups of Ti-Nb-Zr-W quaternary diffusion couples annealed at 1273 K for 25 h were prepared and measured by electron probe microanalysis and nanoindentation tests. Subsequently, the composition-dependent elasto-plastic properties in a wide composition range of Ti-Nb-Zr-W system were obtained by using two analytical methods including Oliver-Pharr analysis and reverse analysis algorithms. Moreover, elasto-plastic property and interdiffusion coefficient databanks of BCC Ti-Nb-Zr-W system were established by using the machine learning and pragmatic numerical inverse methods, respectively. A hot workability parameter defined by the ratio of Young's modulus to main interdiffusion coefficient was proposed to evaluate the behavior during the hot working. Finally, wear resistance of refractory multi-element alloys was also discussed and verified by the experimental results. The results reveal that Ti-rich refractory multi-element alloys with the low W content have high hardness, good wear resistance, high stress, and excellent processability during the hot working, while the relatively high W content cannot improve the elasto-plastic properties of Ti-rich refractory alloys. Materials science ; Nanoindentation ; Refractory metals ; Diffusion ; Metallurgy ; Niobium ; Modulus ; Refractory (planetary science) ; Composite material ; Thermodynamics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Li, Zhiyi | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Frederik Wenkel | Luis T. Díaz Müller | Jama Hussein Mohamud | Ali Parviz | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Kerstin Kläser | Josef Dean | Cas Wognum | Maciej Sypetkowski | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Ioannis Koutis | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Therence Bois | Andrew Fitzgibbon | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. Computer science ; Task (project management) ; Machine learning ; Artificial intelligence ; Scale (ratio) ; Resource (disambiguation) ; Graph ; Process (computing) ; Theoretical computer science ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Gianmattia Sgueglia | Michail D. Vrettas | Marco Chino | Alfonso De Simone | Angela Lombardi
Source: Journal of Chemical Information and Modeling (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.com/vrettasm/MetalHawk), a machine learning-based approach to perform simultaneous classification of metal site coordination number and geometry through artificial neural networks (ANNs), which were trained using the Cambridge Structural Database (CSD) and Metal Protein Data Bank (MetalPDB). We demonstrate that the CSD-trained model can be used to classify sites belonging to the most common coordination numbers and geometry classes with balanced accuracy equal to 96.51% for CSD-deposited metal sites. The CSD-trained model was also found to be capable of classifying bioinorganic metal sites from the MetalPDB database, with balanced accuracy equal to 84.29% on the whole PDB data set and to 91.66% on manually reviewed sites in the PDB validation set. Moreover, we report evidence that the output vectors of the CSD-trained model can be considered as a proxy indicator of metal-site distortions, showing that these can be interpreted as a low-dimensional representation of subtle geometrical features present in metal site structures. Artificial neural network ; Computer science ; Artificial intelligence ; Protein Data Bank ; Set (abstract data type) ; Protein Data Bank (RCSB PDB) ; Data set ; Data mining ; Metal ; Representation (politics) ; Coordination number ; Pattern recognition (psychology) ; Biological system ; Algorithm ; Geometry ; Chemistry ; Mathematics ; Protein structure ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: David Helminiak | Hang Hu | Julia Laskin | Dong Hye Ye
Source: IEEE Transactions on Computational Imaging (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's field of view are often neither relevant to underlying biological structures nor chemically informative, MSI presents as a prime candidate for integration with sparse and dynamic sampling algorithms. During a scan, stochastic models determine which locations probabilistically contain information critical to the generation of low-error reconstructions. Decreasing the number of required physical measurements thereby minimizes overall acquisition times. A Deep Learning Approach for Dynamic Sampling (DLADS), utilizing a Convolutional Neural Network (CNN) and encapsulating molecular mass intensity distributions within a third dimension, demonstrates a simulated 70% throughput improvement for Nanospray Desorption Electrospray Ionization (nano-DESI) MSI tissues. Evaluations are conducted between DLADS, a Supervised Learning Approach for Dynamic Sampling, with Least-Squares regression (SLADS-LS), and a Multi-Layer Perceptron (MLP) network (SLADS-Net). When compared with SLADS-LS, limited to a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>m</mml:mi><mml:mtext>/</mml:mtext><mml:mi>z</mml:mi></mml:mrow></mml:math> channel, as well as multichannel SLADS-LS and SLADS-Net, DLADS respectively improves regression performance by 36.7%, 7.0%, and 6.2%, resulting in gains to reconstruction quality of 6.0%, 2.1%, and 3.4% for acquisition of targeted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>m</mml:mi><mml:mtext>/</mml:mtext><mml:mi>z</mml:mi></mml:mrow></mml:math>. Computer science ; Artificial intelligence ; Sampling (signal processing) ; Perceptron ; Deep learning ; Convolutional neural network ; Algorithm ; Pattern recognition (psychology) ; Artificial neural network ; Computer vision Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science
Authors: Shodai Hasebe | Kan Hatakeyama‐Sato | Kenichi Oyaizu | Toru Asahi | Hideko Koshima
Source: ACS Omega (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Prediction of Photochromism of Salicylideneaniline Crystals Using a Data Mining Approach Salicylideneanilines (SAs) are photochromic compounds that undergo enol-keto photoisomerization in the solid state. Research over the past 60 years has revealed empirically that SAs with steric and planar conformations tend to be photochromic and nonphotochromic, respectively. However, increasing counterexamples in the recent literature raise questions about the nature of the relationship between structure and photochromism in SA crystals and whether the photochromism of SA crystals is predictable. This study is the first to construct a data set on SA crystals and conduct a comprehensive analysis to investigate the relationship between molecular and crystal structures and photochromism. A data mining approach revealed that the dihedral angle is the most dominant structural parameter for photochromism, followed by the Hirshfeld surface volume. SAs with neutral bulky hydrocarbon groups, such as the <i>tert</i>-butyl group, tend to be photochromic because such SAs have steric conformation and a loosely packed structure. In contrast, SAs with fluorine, pyridine, and pyrazine are less likely to be photochromic due to their planar conformation and densely packed structures. The photochromism of the SA crystals in our data set was predicted with high accuracy (>85%) using machine learning. The results of this study provide a useful reference for designing SA crystals with desired photochromic properties. Photochromism ; Steric effects ; Dihedral angle ; Chemistry ; Crystallography ; Materials science ; Photochemistry ; Molecule ; Stereochemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Porphyrin and Phthalocyanine Chemistry ; Radical Photochemical Reactions
Authors: Jing Wang | Xin Wang | Hua Zhu | Dingguo Xu
Source: The Journal of Physical Chemistry C (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials The chain or network of hydroxyl groups (OH–) is crucial in determining the structure and function of materials, especially in hydroxyapatite (HAP), a mineral essential for human bones. HAP exhibits a linear arrangement of OH– along the c-axis, which determines its phase transition, dielectric, and piezoelectric properties. However, the mechanism underlying OH– reorientation with temperature remains elusive using traditional experimental and theoretical methods. To address this, we developed a machine learning atomistic potential for HAP using an active learning algorithm, which achieved density functional theory-level accuracy in describing OH– of HAP. The machine learning molecular dynamics simulations revealed that the reorientation of OH– in HAP with temperature occurs through ″flip-flop″ motion, rather than proton transfer. This process starts at about 473 K and accelerates with increasing temperature, consistent with the experimentally observed transformation from the monoclinic to hexagonal phase. At 973 K and above, the rapid “flip-flop” reorientation process leads to an undetermined orientation of OH– along the c-axis. These findings highlight the potential of machine learning-accelerated molecular dynamics simulations in unraveling the microscopic mechanisms underlying the hydrogen bond network in complex multicomponent materials at the atomic level. Monoclinic crystal system ; Hydrogen bond ; Dielectric ; Molecular machine ; Materials science ; Molecular dynamics ; Phase (matter) ; Piezoelectricity ; Density functional theory ; Chemical physics ; Phase transition ; Proton ; Nanotechnology ; Crystallography ; Computational chemistry ; Chemistry ; Thermodynamics ; Molecule ; Physics ; Composite material ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Calcium Carbonate Crystallization and Inhibition
Authors: Federico Ottomano | Giovanni de Felice | Vladimir V. Gusev | Taylor D. Sparks
Source: Digital Discovery (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Not as simple as we thought: a rigorous examination of data aggregation in materials informatics Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy. Simple (philosophy) ; Informatics ; Data science ; Computer science ; Epistemology ; Engineering ; Philosophy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Evan V. Miu | James R. McKone | Giannis Mpourmpakis
Source: Physical Review Letters (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Global and Local Connectivities Describe Hydrogen Intercalation in Metal Oxides We introduce global connectivity, Ω, which quantifies the detailed connectivity of a material and accurately captures reactivity behavior. Here, we demonstrate that combining global and local connectivity describes how metal oxides interact with hydrogen. Blending density functional theory, graph theory, and machine learning we built a reactivity model which accurately predicts hydrogen intercalation potentials of different metal oxides experimentally measured in the lab. The use of global connectivity can accelerate materials design through the development of novel structure-property relationships. Intercalation (chemistry) ; Density functional theory ; Reactivity (psychology) ; Hydrogen ; Metal ; Materials science ; Property (philosophy) ; Characterization (materials science) ; Graph theory ; Chemical physics ; Computer science ; Nanotechnology ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Rupayan Biswas | Upakarasamy Lourderaj | N. Sathyamurthy
Source: Journal of Chemical Sciences (article, 2023-03-25)
Field: Materials Science — Materials Chemistry

Artificial neural networks and their utility in fitting potential energy curves and surfaces and related problems Artificial neural network ; Potential energy ; Computer science ; Artificial intelligence ; Curve fitting ; Ab initio ; Function (biology) ; Data point ; Energy (signal processing) ; Potential energy surface ; Machine learning ; Mathematics ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Filippo Bigi | Sergey N. Pozdnyakov | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Wigner kernels: body-ordered equivariant machine learning without a basis Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that have been pursued, the description of local atomic environments in terms of their neighbor densities has been used widely and very succesfully. We propose a novel density-based method which involves computing ``Wigner kernels''. These are fully equivariant and body-ordered kernels that can be computed iteratively with a cost that is independent of the radial-chemical basis and grows only linearly with the maximum body-order considered. This is in marked contrast to feature-space models, which comprise an exponentially-growing number of terms with increasing order of correlations. We present several examples of the accuracy of models based on Wigner kernels in chemical applications, for both scalar and tensorial targets, reaching state-of-the-art accuracy on the popular QM9 benchmark dataset, and we discuss the broader relevance of these ideas to equivariant geometric machine-learning. Equivariant map ; Computer science ; Representation (politics) ; Scalar (mathematics) ; Basis (linear algebra) ; Benchmark (surveying) ; Artificial intelligence ; Algorithm ; Mathematics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jeremy A. McCulloch | Skyler R. St. Pierre | Kevin Linka | Ellen Kuhl
Source: arXiv (Cornell University) (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

On sparse regression, Lp-regularization, and automated model discovery Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem-specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias; only L0 regularization allows us to transparently fine-tune the trade-off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user-defined properties. Interpretability ; Leverage (statistics) ; Computer science ; Machine learning ; Regularization (linguistics) ; Regression ; Artificial intelligence ; Artificial neural network ; Knowledge extraction ; Model selection ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Lenz Fiedler | Normand A. Modine | Kyle D. Miller | Attila Cangi
Source: Physical review. B./Physical review. B (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Machine learning the electronic structure of matter across temperatures We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions. Electronic structure ; Range (aeronautics) ; Ionic bonding ; Density functional theory ; Observable ; Electronic density ; Materials science ; Artificial neural network ; Computer science ; Statistical physics ; Machine learning ; Physics ; Condensed matter physics ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; nanoparticles nucleation surface interactions