jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richard Tran | Janice Lan | Muhammed Shuaibi | Brandon M. Wood | Siddharth Goyal | Abhishek Das | Javier Heras‐Domingo | Adeesh Kolluru | Ammar Rizvi | Nima Shoghi | Anuroop Sriram | Félix Therrien | Jehad Abed | Oleksandr Voznyy | Edward H. Sargent | Zachary W. Ulissi | C. Lawrence Zitnick
Source: arXiv (Cornell University) (preprint, 2022-06-17)
Field: Materials Science — Materials Chemistry

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations (~9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide pre-defined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ~36% improvement in energy predictions when combining the chemically dissimilar OC20 and OC22 datasets via fine-tuning. Similarly, we achieved a ~19% improvement in total energy predictions on OC20 and a ~9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. Dataset and baseline models are open sourced, and a public leaderboard is available to encourage continued community developments on the total energy tasks and data. Benchmark (surveying) ; Baseline (sea) ; Computer science ; Scaling ; Range (aeronautics) ; Oxide ; Set (abstract data type) ; Training set ; Energy (signal processing) ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: Chemistry of Materials (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurization of polyhedral deformations with respect to such an aristotype. Applied to a data set of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviors, associated with predominately corner, edge, face, and mixed connectivity between neighboring octahedral units. Through this analysis, we identify SrCrO3 as a material with tunable multiferroic behavior. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach. Octahedron ; Representation (politics) ; Irreducible representation ; Crystal structure ; Perovskite (structure) ; Multiferroics ; Ideal (ethics) ; Crystal (programming language) ; Materials science ; Range (aeronautics) ; Crystallography ; Set (abstract data type) ; Symmetry (geometry) ; Basis (linear algebra) ; Computer science ; Mathematics ; Chemistry ; Geometry ; Pure mathematics ; Ferroelectricity ; Optoelectronics Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Perovskite Materials and Applications
Authors: Brahim Benmouna | Raziyeh Pourdarbani | Sajad Sabzi | Rubén Fernández-Beltrán | Ginés Garcı́a-Mateos | José Miguel Molina‐Martínez
Source: Remote Sensing (article, 2022-12-16)
Field: Chemistry — Analytical Chemistry

Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions. Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; Linear discriminant analysis ; Classifier (UML) ; Machine learning ; Artificial neural network ; Harmony search ; Mathematics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Adarsh V. Kalikadien | Evgeny A. Pidko | Vivek Sinha
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

<i>ChemSpaX</i> : exploration of chemical space by automated functionalization of molecular scaffold Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, <i>ChemSpaX</i>, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of <i>ChemSpaX</i> by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that <i>ChemSpaX</i> generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO-LUMO gaps for transition metal complexes. <i>ChemSpaX</i> is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery. Chemical space ; Workflow ; Computer science ; Python (programming language) ; Surface modification ; Nanotechnology ; Molecule ; Scaffold ; Steric effects ; Computational science ; Materials science ; Chemistry ; Drug discovery ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Peerasait Prachaseree | Emma Lejeune
Source: arXiv (Cornell University) (article, 2022-02-02)
Field: Materials Science — Materials Chemistry

Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks From designing architected materials to connecting mechanical behavior across scales, computational modeling is a critical tool in solid mechanics. Recently, there has been a growing interest in using machine learning to reduce the computational cost of physics-based simulations. Notably, while machine learning approaches that rely on Graph Neural Networks (GNNs) have shown success in learning mechanics, the performance of GNNs has yet to be investigated on a myriad of solid mechanics problems. In this work, we examine the ability of GNNs to predict a fundamental aspect of mechanically driven emergent behavior: the connection between a column's geometric structure and the direction that it buckles. To accomplish this, we introduce the Asymmetric Buckling Columns (ABC) dataset, a dataset comprised of three sub-datasets of asymmetric and heterogeneous column geometries where the goal is to classify the direction of symmetry breaking (left or right) under compression after the onset of instability. Because of complex local geometry, the "image-like" data representations required for implementing standard convolutional neural network based metamodels are not ideal, thus motivating the use of GNNs. In addition to investigating GNN model architecture, we study the effect of different input data representation approaches, data augmentation, and combining multiple models as an ensemble. While we were able to obtain good results, we also showed that predicting solid mechanics based emergent behavior is non-trivial. Because both our model implementation and dataset are distributed under open-source licenses, we hope that future researchers can build on our work to create enhanced mechanics-specific machine learning pipelines for capturing the behavior of complex geometric structures. Computer science ; Artificial neural network ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Representation (politics) ; Theoretical computer science Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Ankit Roy | Indranil Roy | Louis J. Santodonato | Ganesh Balasubramanian
Source: JOM (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Data-Guided Feature Identification for Predicting Specific Heat of Multicomponent Alloys Workflow ; Valence electron ; Materials science ; Alloy ; Homogeneous ; Valence (chemistry) ; Feature (linguistics) ; Specific heat ; Lattice (music) ; Phonon ; Lattice constant ; Computer science ; Statistical physics ; Thermodynamics ; Electron ; Chemistry ; Metallurgy ; Condensed matter physics ; Physics ; Database Machine Learning in Materials Science ; High Entropy Alloys Studies ; Chalcogenide Semiconductor Thin Films
Authors: Florence H. Vermeire | Yunsie Chung | William H. Green
Source: ChemRxiv (preprint, 2022-06-08)
Field: Materials Science — Materials Chemistry

Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures The solubility of organic molecules is crucial in organic synthesis and industrial chemistry, it is important in the design of many phase separation and purification units, and it controls the migration of many species into the environment. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5,000 experimental solid solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute’s sublimation enthalpy, are also presented. Solubility ; Solvation ; Enthalpy ; Thermodynamics ; Solvent ; Chemistry ; Aqueous solution ; Sublimation (psychology) ; Molecule ; Hildebrand solubility parameter ; Enthalpy change of solution ; Organic chemistry ; Physics Crystallization and Solubility Studies ; Process Optimization and Integration ; Analytical Chemistry and Chromatography
Authors: Matthew J. Burn | Paul L. A. Popelier
Source: Journal of Computational Chemistry (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine. Gaussian process ; Kriging ; Computer science ; Process (computing) ; Active learning (machine learning) ; Force field (fiction) ; Set (abstract data type) ; Field (mathematics) ; Machine learning ; Gaussian ; Regression ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Marcin Abram | Keith Burghardt | Greg Ver Steeg | Aram Galstyan | Rémi Dingreville
Source: npj Computational Materials (article, 2022-09-22)
Field: Materials Science — Materials Chemistry

Inferring topological transitions in pattern-forming processes with self-supervised learning Abstract The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes. Microstructure ; Spinodal decomposition ; Materials science ; Computer science ; Artificial intelligence ; Process (computing) ; Phase transition ; Spinodal ; Statistical physics ; Topology (electrical circuits) ; Phase (matter) ; Physics ; Mathematics ; Condensed matter physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Block Copolymer Self-Assembly
Authors: Xuan Dong | Xiangkun Gao | Rong Wang | Chao Liu | Jiayue Wu | Qing Huang
Source: International journal of medicinal mushrooms (article, 2022-10-05)
Field: Chemistry — Analytical Chemistry

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market. Lentinula ; Mushroom ; Polysaccharide ; Near-infrared spectroscopy ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Mathematics ; Auricularia ; Edible mushroom ; Computer science ; Food science ; Pattern recognition (psychology) ; Chemistry ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Myeonghun Lee | Minseon Kim | Kyoungmin Min
Source: Materials Today Communications (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Evaluation of principal features for predicting bulk and shear modulus of inorganic solids with machine learning Materials science ; Feature selection ; Shear modulus ; Artificial neural network ; Elastic modulus ; Artificial intelligence ; Machine learning ; Modulus ; Pattern recognition (psychology) ; Computer science ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Daigo Furuya | Takuya Miyashita | Yoshio Miura | Yuma Iwasaki | Masato Kotsugi
Source: Science and Technology of Advanced Materials Methods (article, 2022-06-30)
Field: Materials Science — Materials Chemistry

Autonomous synthesis system integrating theoretical, informatics, and experimental approaches for large-magnetic-anisotropy materials We developed an autonomous and efficient system for synthesising ferromagnetic materials with large magnetocrystalline anisotropy by integrating theoretical, informatics, and experimental approaches. By combining the first-principles calculation of the magnetic anisotropy with Bayesian optimisation, we virtually screened candidate materials, comprising four elements and four-layer periods, from various magnetic multilayers. We employed the expected improvement as the acquisition function and Matern52 as the kernel function, to develop a robust machine learning model. We fabricated the top three predicted magnetic materials under laboratory conditions by monoatomic layer deposition and evaluated their magnetic anisotropy using a superconducting quantum interference device (SQUID). Ultimately, we demonstrated that [Fe/Co/Fe/Ni]13 is a novel ferromagnetic material whose magnetic anisotropy exceeds that of L10-FeNi- and L10-FeCo-type alloys. Furthermore, the origin of the perpendicular magnetic anisotropy was derived from the spin-conserving as well as the spin-flip terms. We determined that Bayesian optimisation offers promising configurability features in terms of the electronic structure that extend beyond the empirical knowledge and human intuition. Anisotropy ; Informatics ; Materials science ; Computer science ; Condensed matter physics ; Physics ; Engineering ; Electrical engineering ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Xiaowen Lin | Feili Li | Song Wang | Jun Xie | Qiannan Pan | Ping Wang | Changhua Xu
Source: Food and Bioprocess Technology (article, 2022-12-13)
Field: Chemistry — Analytical Chemistry

A Novel Method Based on Multi-Molecular Infrared (MM-IR) AlexNet for Rapid Detection of Trace Harmful Substances in Flour RGB color model ; Infrared ; TRACE (psycholinguistics) ; Computer science ; Asynchronous communication ; Chemistry ; Artificial intelligence ; Physics ; Telecommunications ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chih-Hsuan Yang | Balaji Sesha Sarath Pokuri | Xian Yeow Lee | Sangeeth Balakrishnan | Chinmay Hegde | Soumik Sarkar | Baskar Ganapathysubramanian
Source: Computational Materials Science (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Multi-fidelity machine learning models for structure–property mapping of organic electronics Leverage (statistics) ; Computer science ; Property (philosophy) ; Fidelity ; High fidelity ; Deep learning ; Artificial intelligence ; Offset (computer science) ; Machine learning ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Jiangcheng Xu | Yun Zhang | Jiale Han | An Su | Haoran Qiao | Chengyun Zhang | Jing Tang | Xi Shen | Bin Sun | Wenbo Yu | Silong Zhai | Xinqiao Wang | Yejian Wu | Weike Su | Hongliang Duan
Source: Organic Chemistry Frontiers (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model Transformer, a sequence-to-sequence deep learning model, is capable of predicting the reaction intermediates of radical cascade cyclization. This study provides a novel approach to help chemists discover the mechanisms of organic reactions. Cascade ; Chemistry ; Transformer ; Sequence (biology) ; Combinatorial chemistry ; Artificial intelligence ; Computer science ; Chromatography ; Engineering ; Biochemistry ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis