jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tyler Sours | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2022-12-22)
Field: Chemistry — Inorganic Chemistry

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PES) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) dataset (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivates further MLP development for nanoporous materials with near-ab initio accuracy. Nanoporous ; Density functional theory ; Ab initio ; Zeolite ; Artificial neural network ; Materials science ; Flexibility (engineering) ; Computer science ; Force field (fiction) ; Artificial intelligence ; Statistical physics ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Mathematics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Lan Yang | Jichen Li | Feiyang Chen | Kuang Yu
Source: ChemRxiv (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

A Transferrable Range-Separated Force Field for Water: Combining the Power of Both Physically-Motivated Models and Machine Learning Techniques An accurate, transferrable, and computationally efficient potential energy surface (PES) is of paramount importance for all molecular mechanics simulations. In this work, using water as example, we demonstrate how one can construct a reliable force field by combining the advantages of both physically-motivated and data-driven machine learning (ML) methods. Different to the existing water models based on many-body expansion, we adopt a separation scheme purely based on distances, and systematically investigate how the long-range asymptotic terms increase the transferability and the data efficiency of the ML potential. We also show how the ML model can be an ideal tool to fit the short-range interactions which used to post great challenges to the conventional physically-motivated force fields. The water force field we obtain is highly accurate and transferrable in different environments, and the distance-based separation scheme is easy to be extended to general molecular systems. Through this study, we show how the information we learn from small clusters can be extrapolated into larger systems, thus providing a general recipe for the intermolecular force field development at CCSD(T) level of theory in future. Transferability ; Force field (fiction) ; Computer science ; Range (aeronautics) ; Construct (python library) ; Field (mathematics) ; Intermolecular force ; Ideal (ethics) ; Scheme (mathematics) ; Machine learning ; Work (physics) ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Lan Yang | Jichen Li | Feiyang Chen | Kuang Yu
Source: ChemRxiv (preprint, 2022-09-09)
Field: Materials Science — Materials Chemistry

A Transferrable Range-Separated Force Field for Water: Combining the Power of Both Physically-Motivated Models and Machine Learning Techniques An accurate, transferrable, and computationally efficient potential energy surface (PES) is of paramount importance for all molecular mechanics simulations. In this work, using water as example, we demonstrate how one can construct a reliable force field by combining the advantages of both physically-motivated and data-driven machine learning (ML) methods. Different to the existing water models based on many-body expansion, we adopt a separation scheme purely based on distances, and systematically investigate how the long-range asymptotic terms increase the transferability and the data efficiency of the ML potential. We also show how the ML model can be an ideal tool to fit the short-range interactions which used to post great challenges to the conventional physically-motivated force fields. The water force field we obtain is highly accurate and transferrable in different environments, and the distance-based separation scheme is easy to be extended to general molecular systems. Through this study, we show how the information we learn from small clusters can be extrapolated into larger systems, thus providing a general recipe for the intermolecular force field development at CCSD(T) level of theory in future. Transferability ; Force field (fiction) ; Computer science ; Range (aeronautics) ; Construct (python library) ; Field (mathematics) ; Intermolecular force ; Scheme (mathematics) ; Ideal (ethics) ; Work (physics) ; Statistical physics ; Machine learning ; Artificial intelligence ; Mathematics ; Physics ; Mechanical engineering ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Krishnanand Mallayya | Joshua Straquadine | Matthew Krogstad | Maja D. Bachmann | Anisha Singh | R. Osborn | Stephan Rosenkranz | I. R. Fisher | Eun-Ah Kim
Source: arXiv (Cornell University) (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

Bragg glass signatures in Pd$_x$ErTe$_3$ with X-ray diffraction Temperature Clustering (X-TEC) The Bragg glass phase is a nearly perfect crystal with glassy features predicted to occur in vortex lattices and charge density wave systems in the presence of disorder. Detecting it has been challenging despite its sharp theoretical definition in terms of diverging correlation lengths. Here, we present evidence supporting a Bragg glass phase in the systematically disordered charge density wave material PdxErTe3. We do this using comprehensive x-ray data and a machine learning analysis tool called X-ray temperature clustering, or X-TEC. We establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable this analysis, we introduced a high-throughput measure of inverse correlation length that we call peak spread. The detection of Bragg glass order and the resulting phase diagram advance our understanding of the complex interplay between disorder and fluctuations significantly. Moreover, the use of our analysis technique to target fluctuations through a high-throughput measure of peak spread can revolutionize the study of fluctuations in scattering experiments. TEC ; X-ray ; X-ray crystallography ; Diffraction ; Crystallography ; Materials science ; Cluster analysis ; Geology ; Optics ; Physics ; Chemistry ; Computer science ; Astronomy ; Ionosphere ; Artificial intelligence Phase-change materials and chalcogenides
Authors: Bonita Goh | Yafei Wang | Phalgun Nelaturu | Michael Moorehead | Dimitris Papailiopoulous | Dan J. Thoma | Santanu Chaudhuri | Jason Hattrick‐Simpers | Kumar Sridharan | Adrien Couet
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Materials Chemistry

A High Throughput Experimentation &amp; Material Informatics Platform for the Discovery of Molten Salt Reactor Candidate Structural Materials ASME Sec(III) Div(5) code-certified structural alloys for Molten (halide) Salt Reactors (MSRs) such as 800H, SS316, and IN617 have significant Cr content. This makes them readily-degradable in molten halide salts due to the thermodynamic favorability of soluble chromium halide formation. It is therefore imperative to discover alloys with corrosion resistance that exceeds those of currently-certified alloys, which also possess the necessary high hardness and irradiation-resistance at reactor operating temperatures as a prerequisite for the licensing and deployment of MSRs. The largely-unexplored quasi-infinite quarternary FeCrMnNi High Entropy Alloy space shows promise to yield desired alloys. We present a high-throughput (HTP) process demonstrating a turnaround time from fabrication to corrosion-testing and analysis of 1 week for 25 samples. 70 alloy samples of 1cm 2 were each corrosion-tested on 0.3g salt droplets in isolated corrosion environments. The HTP platform includes the development of an in-situ high-temperature electrochemical sensor system capable of automating the analysis of dissolved corrosion product analytes in the salt. The results of the 70 corrosion tests were used to train a Machine Learning model to predict corrosion performance metrics (eg. Elemental corrosion concentration into salt) based on an input vector parametrizing the physical properties (“descriptors”) of the alloys in the sample set. The model was tested on 20 additional samples and demonstrated conservative predictive capability as well as facilitated the reduction of input feature space from 62 dimensions to 4 dimensions with negligible loss in predictive accuracy. Corrosion ; Molten salt ; Materials science ; Halide ; Metallurgy ; Alloy ; Salt (chemistry) ; Characterization (materials science) ; Chemistry ; Inorganic chemistry ; Nanotechnology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Lei Guo | Jiyang Dong | Xiangnan Xu | Zhichao Wu | Yinbin Zhang | Yongwei Wang | Peng‐Fei Li | Chao Zhao | Zongwei Cai
Source: ChemRxiv (preprint, 2022-03-06)
Field: Chemistry — Spectroscopy

Divide-and-conquer: a flexible deep learning strategy for exploring metabolic heterogeneity from mass spectrometry imaging data There is growing awareness that metabolic heterogeneity of organism provides vital insight into the disease with molecular mechanism and personalized therapy. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration how disease progress aberrant phenotypes, even carcinogenesis and metastasis. Mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of organism based on the in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous region-of-interest (ROIs) or spatially sporadic ROIs. We demonstrate that the novel learning strategy successfully obtain sub-regions that are statistically linked to invasion status and molecular phenotypes of breast cancer, as well as organizing principles during developmental phase. Identification (biology) ; Mass spectrometry imaging ; Computational biology ; Divide and conquer algorithms ; Computer science ; Mechanism (biology) ; Phenotype ; Organism ; Biology ; Artificial intelligence ; Machine learning ; Bioinformatics ; Mass spectrometry ; Genetics ; Chemistry ; Gene Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Huolin L. Xin | Mike Hu
Source: arXiv (Cornell University) (preprint, 2022-10-21)
Field: Materials Science — Materials Chemistry

MnEdgeNet -- Accurate Decomposition of Mixed Oxidation States for Mn XAS and EELS L2,3 Edges without Reference and Calibration Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurement of the Mn L2,3 edges is straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with a three-element oxidation state composition label. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. X-ray absorption spectroscopy ; Absorption spectroscopy ; Spectroscopy ; Offset (computer science) ; Spectral line ; Chemistry ; Valence (chemistry) ; Analytical Chemistry (journal) ; Computer science ; Optics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrocatalysts for Energy Conversion
Authors: Gerardo Campos-Villalobos | Giuliana Giunta | Susana Marín-Aguilar | Marjolein Dijkstra
Source: arXiv (Cornell University) (preprint, 2022-07-13)
Field: Materials Science — Materials Chemistry

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically-symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally-efficient simulations of model systems consisting of colloidal particles with anisotropic shape: mixtures of colloidal rods and nonadsorbing polymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer. We find good agreement with results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well-described by the orientation-dependent ML potentials. Rod ; Symmetry (geometry) ; Orientation (vector space) ; Particle (ecology) ; Anisotropy ; Ellipsoid ; Materials science ; Nanorod ; Statistical physics ; Chemical physics ; Colloid ; Circular symmetry ; Classical mechanics ; Biological system ; Physics ; Nanotechnology ; Geometry ; Optics ; Chemistry ; Mathematics Machine Learning in Materials Science ; Material Dynamics and Properties ; Computational Drug Discovery Methods
Authors: Klaas Dijkstra | Maya Aghaei | Femke Jaarsma | Martin Dijkstra | Rudy Folkersma | Jan Jäger | Jaap van de Loosdrecht
Source: arXiv (Cornell University) (preprint, 2022-03-21)
Field: Chemistry — Analytical Chemistry

On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging The importance of plastic waste recycling is undeniable. In this respect, computer vision and deep learning enable solutions through the automated analysis of short-wave-infrared hyper-spectral images of plastics. In this paper, we offer an exhaustive empirical study to show the importance of efficient model selection for resolving the task of hyper-spectral image segmentation of various plastic flakes using deep learning. We assess the complexity level of generic and specialized models and infer their performance capacity: generic models are often unnecessarily complex. We introduce two variants of a specialized hyper-spectral architecture, PlasticNet, that outperforms several well-known segmentation architectures in both performance as well as computational complexity. In addition, we shed lights on the significance of signal pre-processing within the realm of hyper-spectral imaging. To complete our contribution, we introduce the largest, most versatile hyper-spectral dataset of plastic flakes of four primary polymer types. Computer science ; Segmentation ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Computational complexity theory ; Task (project management) ; Signal processing ; Machine learning ; Algorithm ; Engineering ; Systems engineering ; Digital signal processing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Rintaro Yanagi | Ren Togo | Takahiro Ogawa | Miki Haseyama
Source:  (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Rubber Material Retrieval System using Electron Microscope Images for Rubber Material Development For developing valuable rubber materials, machine learning-based computer-aided analysis systems have been attracting a lot of attention. However, these systems mainly focus on analyzing the table and textual data, and the electron microscope images including the rich material information have not been enough analyzed. By effectively using these electron microscope images, further support for the material discovery is realized. In this paper, we present a material information retrieval system via electron microscope image space. Our system aims to support visually and comprehensively grasping the relationships between various rubber materials and those properties. By effectively using the electron microscope image space for material information retrieval, it is expected that the advances in material development are further accelerated. Natural rubber ; Microscope ; Electron microscope ; Computer science ; Focus (optics) ; Digital microscope ; Materials science ; Scanning electron microscope ; Artificial intelligence ; Computer vision ; Composite material ; Optics ; Physics Machine Learning in Materials Science
Authors: Nathaniel Bobbitt | Kaihang Shi | Benjamin J. Bucior | Haoyuan Chen | Nathaniel Tracy-Amoroso | Zhao Li | Yangzesheng Sun | Julia Merlin | Ilja Siepmann | Daniel W. Siderius | Randall Q. Snurr
Source:  (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

MOFX-DB: An online database of computational adsorption data for nanoporous materials. Machine learning and data mining coupled with molecular\nmodeling\nhave become powerful tools for materials discovery. Metal–organic\nframeworks (MOFs) are a rich area for this due to their modular construction\nand numerous applications. Here, we make data from several previous\nlarge-scale studies in MOFs and zeolites from our groups (and new\ndata for N&lt;sub&gt;2&lt;/sub&gt; and Ar adsorption in MOFs) easily accessible\nin one place. The database includes over three million simulated adsorption\ndata points for H&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, CO&lt;sub&gt;2&lt;/sub&gt;, Xe,\nKr, Ar, and N&lt;sub&gt;2&lt;/sub&gt; in over 160 000 MOFs and 286 zeolites,\ntextural properties like pore sizes and surface areas, and the structure\nfile for each material. We include metadata about the Monte Carlo\nsimulations to enable reproducibility. The database is searchable\nby MOF properties, and the data are stored in a standardized JavaScript\nObject Notation format that is interoperable with the NIST adsorption\ndatabase. We also identify several MOFs that meet high performance\ntargets for multiple applications, such as high storage capacity for\nboth hydrogen and methane or high CO&lt;sub&gt;2&lt;/sub&gt; capacity plus good\nXe/Kr selectivity. By providing this data publicly, we hope to facilitate\nmachine learning studies on these materials, leading to new insights\non adsorption in MOFs and zeolites. Nanoporous ; Computer science ; Adsorption ; Database ; Materials science ; Nanotechnology ; Chemistry ; Physical chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Adsorption and biosorption for pollutant removal
Authors: Kunyan Shen | Na Cheng | Jianwei Zhao | Jin Hou
Source: Computational Materials Science (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Correlation between the breaking behavior and the initial microstructural defects of the metallic nanowires: An approach from statistical analysis Slip (aerodynamics) ; Nanowire ; Materials science ; Lüders band ; Phonon ; Condensed matter physics ; Microstructure ; Physics ; Composite material ; Nanotechnology ; Thermodynamics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Surface and Thin Film Phenomena
Authors: Stephen J. Pennycook | Jiadong Dan | Xiaoxu Zhao | Shoucong Ning | Wu Zhou | Qian He | N. Duane Loh
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Navigating the Nanoworld: Automatic Feature Recognition Our ability to navigate materials and classify both atomic structure and defects is key to developing new materials with improved properties, such as piezoelectrics [1], thermoelectrics [2] and 2D materials So far this process has been mostly human based, relying on our finding the critical defects and deciding which are important ourselves. However, machine learning is beginning to enable automatic classification of defects and structures without human supervision or the need for training datasets. Such capability could lead to improved analysis of structural evolution during in-situ experiments, or even to a self-driving microscope in the future. Feature (linguistics) ; Computer science ; Action recognition ; Action (physics) ; Content (measure theory) ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Richard L. Apodaca
Source: Front Matter (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

18 SMILES Problems as Seen through Twitter Polls SMILES is a de facto standard for chemical data exchange. It's routinely found in public-facing databases, supported by most widely-used cheminformatics toolkits, and for the last few years has even appeared in the context of machine learning. The problem is that the language was never completely specified. The claim that one of chemistry's most important data formats is incompletely specified may seem extraordinary. Context (archaeology) ; De facto Machine Learning in Materials Science ; Scientific Computing and Data Management ; History and advancements in chemistry