jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacob C. Hickey | Jakoah Brgoch
Source: Chemistry of Materials (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

The Limits of Proxy-Guided Superhard Materials Screening Extensive effort has gone into discovering new hard and superhard materials. One widely employed approach relies on a priori screening using proxies for Vickers microhardness (Hv) obtained from density functional theory (DFT) calculations. Indeed, several semiempirical equations have been proposed to predict Hv solely using the DFT-generated bulk and shear moduli. Yet, hardness is a complex phenomenon influenced by many factors across multiple length scales, constraining the dependability of these approaches. Each DFT “hardness” calculation also claims to be the most accurate, although a comprehensive comparison among the models is rarely performed. Moreover, none of the models capture the observed load dependency of Vickers hardness measurements. This work evaluates the limits of these moduli-derived hardness calculations by examining a robust set of load-consistent, experimental hardness measurements, computationally estimating Hv values of 307 unique compounds, and building a machine learning model capable of predicting Hv. The resulting analysis was then benchmarked against six experimentally synthesized boride systems with diverse crystal chemistries. In all cases, the DFT proxies quickly break down, whereas the machine learning model proves the most accurate for predicting Hv at a significantly lower computational cost. These findings highlight the challenges of using DFT-based semiempirical hardness equations while reinforcing the power of machine learning for hard and superhard materials discovery. Vickers hardness test ; Density functional theory ; Materials science ; Indentation hardness ; Dependability ; Moduli ; A priori and a posteriori ; Computer science ; Statistical physics ; Algorithm ; Computational chemistry ; Chemistry ; Physics ; Metallurgy Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; MXene and MAX Phase Materials
Authors: Qiyuan Zhao | Sai Mahit Vaddadi | Michael Woulfe | Lawal Adewale Ogunfowora | Sanjay Garimella | Olexandr Isayev | Brett M. Savoie
Source: ChemRxiv (preprint, 2022-11-21)
Field: Materials Science — Materials Chemistry

Comprehensive exploration of graphically defined reaction spaces Extant reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X- D2/TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. Extant taxon ; Chemical space ; Graph ; Molecule ; Chemistry ; Transition state ; Computational chemistry ; Computer science ; Statistical physics ; Physics ; Theoretical computer science ; Organic chemistry ; Drug discovery ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Kohei Makino | Fusataka Kuniyoshi | Jun Ozawa | Makoto Miwa
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Extracting and Analyzing Inorganic Material Synthesis Procedures in the Literature Materials informatics requires large-scale collection and analysis of material synthesis procedures described in the literature for designing materials using computational methods. However, existing studies have not performed the paragraph-level analysis of the procedures. Moreover, since most of the synthesis procedures are described in natural language in articles and technical documents, it is necessary to structure them in a format that can be handled by computers through information extraction. Therefore, in this study, we construct a pipeline system that extracts synthesis procedures from text in the form of a flow graph and analyzes each procedure as a flow graph rather than a set of processes. The extraction system extracts entities by the deep learning model and relations between entities by the rule-based extractor from all paragraphs in the literature and selects procedures that include valid structures of entities and relations. Our evaluation of a benchmark dataset gave micro-averaged F-scores of 0.807, 0.830, and 0.609 for the entity extractor, relation extractor, and pipeline extractor, respectively. We applied this system to a large amount of literature and extracted approximately 90,000 flow graphs (procedures) containing approximately 4 million entities and 3 million relations. We performed several analyses, including taking statistics of the extracted graphs and checking frequent subgraphs for the extracted graphs. Commonly used methods in materials science were confirmed from our analyses; for example, ethanol is often dried by heating at 60 &#x00B0;C, and less-reactive noble gases are rarely included in the products. As a result, we experimentally confirmed that the extracted procedures were reasonable. Computer science ; Extractor ; Paragraph ; Graph ; Information retrieval ; Pipeline transport ; Data mining ; Algorithm ; Theoretical computer science ; Process engineering ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Shiru Lin | Jean-Baptiste Tristan | Yang Wang | Junwei Lucas Bao
Source: Nano Research (article, 2022-07-26)
Field: Materials Science — Materials Chemistry

Dry reforming of methane on doped Ni nanoparticles: Feature-assisted optimizations and ranking of doping metals for direct activations of CH4 and CO2 As a vital energy resource and raw material for many industrial products, syngas (CO and H2) is of great significance. Dry reforming of methane (DRM) is an important approach to producing syngas (with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle) from methane and carbon dioxide, with a lower operational cost as compared to other reforming techniques. However, many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles. A systematic search for highly efficient metallic catalysts, which reduce the reaction barriers for the rate-determining steps and resist carbon deposition, is urgently needed. Nickel is a typical low-cost transition metal for activating the C-H bond in methane. In this work, we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M (M-Ni) by combining density functional theory (DFT) calculations and machine learning (ML). We focus on the two critical steps in DRM-CH4 and CO2 direct activations. We used DFT and slab models for the Ni(111) facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni systems. We used this dataset to train ML models with chemical-knowledge-based features and predicted CH4 and CO2 dissociation energies and barriers, which revealed the composition—activity relationships of the bimetallic catalysts. We also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst screening. We emphasized that ML ranking models would be more valuable than regression models in high-throughput screenings. Finally, we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates, validating the robustness of the trained model. Methane ; Catalysis ; Syngas ; Bimetallic strip ; Materials science ; Carbon dioxide reforming ; Chemical engineering ; Density functional theory ; Methanation ; Hydrogen ; Chemistry ; Organic chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Ting Ye | Baocai Jing | Dingyi Pan
Source: Journal of Computational Physics (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

Intelligent dissipative particle dynamics: Bridging mesoscopic models from microscopic simulations via deep neural networks Mesoscopic physics ; Dissipative particle dynamics ; Granularity ; Statistical physics ; Dissipative system ; Molecular dynamics ; Physics ; Classical mechanics ; Particle (ecology) ; Computer science ; Condensed matter physics ; Quantum mechanics Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Machine Learning in Materials Science
Authors: Satyam Bhatti | Habib Ullah Manzoor | Bruno Michel | Ruy S. Bonilla | Richard Abrams | Ahmed Zoha | Sajjad Hussain | Rami Ghannam
Source: arXiv (Cornell University) (review, 2022-12-26)
Field: Materials Science — Materials Chemistry

Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review Solar photovoltaic (PV) technology has merged as an efficient and versatile method for converting the Sun's vast energy into electricity. Innovation in developing new materials and solar cell architectures is required to ensure lightweight, portable, and flexible miniaturized electronic devices operate for long periods with reduced battery demand. Recent advances in biomedical implantable and wearable devices have coincided with a growing interest in efficient energy-harvesting solutions. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted as game changers in energy harvesting, especially in solar energy materials. In this article, we systematically review a range of ML techniques for optimizing the performance of low-cost solar cells for miniaturized electronic devices. Our systematic review reveals that these ML techniques can expedite the discovery of new solar cell materials and architectures. In particular, this review covers a broad range of ML techniques targeted at producing low-cost solar cells. Moreover, we present a new method of classifying the literature according to data synthesis, ML algorithms, optimization, and fabrication process. In addition, our review reveals that the Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) enables the design of the most promising low-solar cell architecture. Therefore, our review is a critical evaluation of existing ML techniques and is presented to guide researchers in discovering the next generation of low-cost solar cells using ML techniques. Photovoltaic system ; Computer science ; Bayesian optimization ; Solar energy ; Energy harvesting ; Solar cell ; Artificial intelligence ; Process engineering ; Systems engineering ; Energy (signal processing) ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques ; solar cell performance optimization
Authors: Jinu Jeong | Alireza Moradzadeh | N. R. Aluru
Source: The Journal of Physical Chemistry A (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict the interatomic or coarse-grained potential, which is a tremendous challenge. Many approaches have been developed to predict the potential parameters based on various techniques, including the relative entropy method, integral equation theory, etc., but these methods lack transferability and are limited to a specific range of thermodynamic states. Recently, data-driven and machine learning approaches have been developed to overcome such limitations. In this study, we expand the range of thermodynamic states used to train deep inverse liquid-state theory (DeepILST)<sup>1</sup>, a deep learning framework for solving the inverse problem of liquid-state theory. We also assess the performance of DeepILST in coarse-graining various multiatom molecules and identify the molecular characteristics that affect the coarse-graining performance of DeepILST. Granularity ; Statistical physics ; Computer science ; Entropy (arrow of time) ; Range (aeronautics) ; Interatomic potential ; Transferability ; Molecular dynamics ; Physics ; Machine learning ; Thermodynamics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Gabriella Pizzuto | Jacopo de Berardinis | Louis Longley | Hatem Fakhruldeen | Andrew I. Cooper
Source: 2022 International Joint Conference on Neural Networks (IJCNN) (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

SOLIS: Autonomous Solubility Screening using Deep Neural Networks Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups, while being simple and fast to train and, as a result, easily transferable to a robotic platform. Computer science ; Solubility ; Automation ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Sample (material) ; Process engineering ; Artificial neural network ; Throughput ; Biochemical engineering ; Machine learning ; Chemistry ; Engineering ; Mechanical engineering ; Chromatography ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Chemical Sensor Technologies
Authors: Zijiang Yang | Hanghang Chen | Maodu Chen
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Representing globally accurate reactive potential energy surfaces with complex topography by combining Gaussian process regression and neural networks There has been increasing attention in using machine learning technologies, such as neural networks (NNs) and Gaussian process regression (GPR), to model multi-dimensional potential energy surfaces (PESs). A PES constructed using NNs features high accuracy and generalization capability, but a single NN cannot actively select training points as GPR does, resulting in expensive <i>ab initio</i> calculations as the molecular complexity increases. However, a PES constructed using GPR has a slow speed of evaluation and it is difficult to accurately describe a fast-changing potential. Herein, an efficient scheme for representing globally accurate reactive PESs with complex topography based on as few points as possible by incorporating active data selection of GPR into NN fitting is proposed. The validity of this strategy is tested using the BeH<sub>2</sub><sup>+</sup> system, and only 1270 points are automatically sampled. The generalization performance and speed of evaluation of the generated PES are much better than those of the GPR PES constructed using the same dataset. Moreover, an accurate NN PES is fitted by 12 122 points as a benchmark for comparison to further test the global accuracy of the PES obtained using this scheme, and the corresponding results present extremely consistent topography characteristics and calculated Be<sup>+</sup>(<sup>2</sup>S) + H<sub>2</sub> reaction probabilities. Kriging ; Ground-penetrating radar ; Artificial neural network ; Benchmark (surveying) ; Generalization ; Gaussian process ; Computer science ; Gaussian ; Data point ; Potential energy ; Regression ; Algorithm ; Process (computing) ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Mathematics ; Computational chemistry ; Radar ; Chemistry ; Statistics ; Physics ; Geology Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Andrew Dickson White | Glen M. Hocky | Heta A. Gandhi | Mehrad Ansari | Sam Cox | Geemi P. Wellawatte | Subarna Sasmal | Ziyue Yang | Kangxin Liu | Yuvraj Singh | Willmor J. Peña Ccoa
Source: ChemRxiv (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

Do large language models know chemistry? Mostly yes. We systematically evaluate machine learning large language models (LLMs) that generate code in the context of chemistry. We produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points via prompt engineering strategies, like putting copyright notices at the top of files. These dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous. Correctness ; Computer science ; Context (archaeology) ; Benchmark (surveying) ; Code (set theory) ; Set (abstract data type) ; Resource (disambiguation) ; Variety (cybernetics) ; Data science ; Artificial intelligence ; Programming language ; Geography Machine Learning in Materials Science ; Topic Modeling
Authors: Lina Zhang | Shuang Zhang | A. Owens | S. N. Yurchenko | Pavlo O. Dral
Source: Scientific Data (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces High-level ab initio quantum chemical (QC) molecular potential energy surfaces (PESs) are crucial for accurately simulating molecular rotation-vibration spectra. Machine learning (ML) can help alleviate the cost of constructing such PESs, but requires access to the original ab initio PES data, namely potential energies computed on high-density grids of nuclear geometries. In this work, we present a new structured PES database called VIB5, which contains high-quality ab initio data on 5 small polyatomic molecules of astrophysical significance (CH<sub>3</sub>Cl, CH<sub>4</sub>, SiH<sub>4</sub>, CH<sub>3</sub>F, and NaOH). The VIB5 database is based on previously used PESs, which, however, are either publicly unavailable or lacking key information to make them suitable for ML applications. The VIB5 database provides tens of thousands of grid points for each molecule with theoretical best estimates of potential energies along with their constituent energy correction terms and a data-extraction script. In addition, new complementary QC calculations of energies and energy gradients have been performed to provide a consistent database, which, e.g., can be used for gradient-based ML methods. Ab initio ; Quantum chemical ; Computational chemistry ; Quantum ; Chemical physics ; Database ; Chemistry ; Physics ; Computer science ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides
Authors: Weixian Deng | Jihui Sha | Fanglei Xue | Yasaman Jami‐Alahmadi | Kathrin Plath | James A. Wohlschlegel
Source: Analytical Chemistry (article, 2022-11-08)
Field: Chemistry — Spectroscopy

High-Field Asymmetric Waveform Ion Mobility Spectrometry Interface Enhances Parallel Reaction Monitoring on an Orbitrap Mass Spectrometer High-field asymmetric waveform ion mobility spectrometry (FAIMS) enables gas-phase separations on a chromatographic time scale and has become a useful tool for proteomic applications. Despite its emerging utility, however, the molecular determinants underlying peptide separation by FAIMS have not been systematically investigated. Here, we characterize peptide transmission in a FAIMS device across a broad range of compensation voltages (CVs) and used machine learning to identify charge state and three-dimensional (3D) electrostatic peptide potential as major contributors to peptide intensity at a given CV. We also demonstrate that the machine learning model can be used to predict optimized CV values for peptides, which significantly improves parallel reaction monitoring workflows. Together, these data provide insight into peptide separation by FAIMS and highlight its utility in targeted proteomic applications. Ion-mobility spectrometry ; Chemistry ; Orbitrap ; Mass spectrometry ; Peptide ; Waveform ; Analytical Chemistry (journal) ; Ion ; Chromatography ; Voltage Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications