jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isaiah O. Betinol | Junshan Lai | Saumya Thakur | Jolene P. Reid
Source: ChemRxiv (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

An Unsupervised Machine Learning Workflow for Assigning and Predicting Generality in Asymmetric Catalysis The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identi-fy-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. We conclude that broadly applicable catalysts may be more adaptative to changes in reactant structure because enantioinduction does not rely on a single set of noncovalent interactions. In contrast, some systems work by engaging in robust noncovalent contacts that do not change significantly in nature when the structure of the reaction component is altered. Finally, our generality techniques permit-ted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating and predicting catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis. Generality ; Workflow ; Computer science ; Catalysis ; Identification (biology) ; Machine learning ; Chemistry ; Artificial intelligence ; Organic chemistry ; Database Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Alexander New | Michael Pekala | Elizabeth A. Pogue | Nam Q. Le | Janna Domenico | Christine Piatko | Christopher D. Stiles
Source: arXiv (Cornell University) (preprint, 2023-08-09)
Field: Materials Science — Materials Chemistry

Evaluating the diversity and utility of materials proposed by generative models Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art generative model, the physics-guided crystal generation model (PGCGM), can be used as part of the inverse design process. We show that the default PGCGM's input space is not smooth with respect to parameter variation, making material optimization difficult and limited. We also demonstrate that most generated structures are predicted to be thermodynamically unstable by a separate property-prediction model, partially due to out-of-domain data challenges. Our findings suggest how generative models might be improved to enable better inverse design. Generative grammar ; Generative Design ; Generative model ; Property (philosophy) ; Inverse ; Computer science ; Process (computing) ; Domain (mathematical analysis) ; Space (punctuation) ; Machine learning ; Artificial intelligence ; Mathematics ; Engineering Machine Learning in Materials Science
Authors: Joachim Schaeffer | Paul Gasper | Esteban Garcia-Tamayo | Raymond Gasper | Masaki Adachi | Juan Pablo Gaviria-Cardona | Simon Montoya‐Bedoya | Anoushka Bhutani | Andrew Schiek | Rhys E. A. Goodall | Rolf Findeisen | Richard D. Braatz | Simon Engelke
Source: arXiv (Cornell University) (preprint, 2023-02-07)
Field: Materials Science — Materials Chemistry

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra. Computer science ; Identifiability ; Identification (biology) ; Equivalent circuit ; Nyquist plot ; Electrical impedance ; Artificial intelligence ; Convolutional neural network ; Capacitive sensing ; Data mining ; Dielectric spectroscopy ; Pattern recognition (psychology) ; Machine learning ; Engineering Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Advanced Battery Technologies Research
Authors: Joachim Schaeffer | Eric Lenz | William C. Chueh | Martin Z. Bazant | Rolf Findeisen | Richard D. Braatz
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data High-dimensional linear regression is important in many scientific fields. This article considers discrete measured data of underlying smooth latent processes, as is often obtained from chemical or biological systems. Interpretation in high dimensions is challenging because the nullspace and its interplay with regularization shapes regression coefficients. The data's nullspace contains all coefficients that satisfy $\mathbf{Xw}=\mathbf{0}$, thus allowing very different coefficients to yield identical predictions. We developed an optimization formulation to compare regression coefficients and coefficients obtained by physical engineering knowledge to understand which part of the coefficient differences are close to the nullspace. This nullspace method is tested on a synthetic example and lithium-ion battery data. The case studies show that regularization and z-scoring are design choices that, if chosen corresponding to prior physical knowledge, lead to interpretable regression results. Otherwise, the combination of the nullspace and regularization hinders interpretability and can make it impossible to obtain regression coefficients close to the true coefficients when there is a true underlying linear model. Furthermore, we demonstrate that regression methods that do not produce coefficients orthogonal to the nullspace, such as fused lasso, can improve interpretability. In conclusion, the insights gained from the nullspace perspective help to make informed design choices for building regression models on high-dimensional data and reasoning about potential underlying linear models, which are important for system optimization and improving scientific understanding. Interpretability ; Regularization (linguistics) ; Linear regression ; Regression ; Regression analysis ; Mathematics ; Computer science ; Lasso (programming language) ; Applied mathematics ; Mathematical optimization ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Sterling G. Baird | Jeet N. Parikh | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that can be indistinguishable from the ground truth observation within the explored dataset bounds dramatically reduces the computational burden of running benchmarks without sacrificing quality, but this requires a large amount of initial data. In the fields of materials science and chemistry, relevant optimization tasks can be challenging due to their complexity, which includes hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated variables. Additionally, they may include mixed numerical and categorical variables that are subject to linear and non-linear constraints. Simulating or experimentally verifying such tasks can be difficult, which is why benchmarks are essential. This study aimed to overcome these challenges by generating 173219 quasi-random hyperparameter combinations across 23 hyperparameters and using them to train CrabNet on the Matbench experimental band gap dataset (Computational runtime: 387 RTX-2080-Ti GPU days). The results were stored in a free-tier shared MongoDB Atlas dataset, creating a regression dataset that maps hyperparameter combinations to metrics such as MAE, RMSE, computational runtime, and model size for the CrabNet model trained on the Matbench experimental band gap benchmark task. To simulate the actual simulations, heteroskedastic noise was incorporated into the regression dataset, and bad hyperparameter combinations were excluded. Percentile ranks were computed within each group of identical parameter sets to capture heteroskedastic noise, rather than assuming Gaussian noise as is done in traditional approaches. This approach can be applied to other benchmark datasets, bridging the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios. Hyperparameter ; Computer science ; Benchmark (surveying) ; Machine learning ; Heteroscedasticity ; Categorical variable ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Neural Network Applications
Authors: Sterling G. Baird | Ramsey Issa | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of "Turing test" by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This study encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days' worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The results were logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate results. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world. Benchmark (surveying) ; Computer science ; Overhead (engineering) ; Noise (video) ; Algorithm ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Data Classification
Authors: Tobias Weber
Source: SoftwareX (article, 2023-07-01)
Field: Chemistry — Spectroscopy

Motion planning for triple-axis spectrometers We present the free and open source software TAS-Paths, a novel system which calculates optimal, collision-free paths for the movement of triple-axis spectrometers. The software features an easy to use graphical user interface, but can also be scripted and used as a library. It allows the user to plan and visualise the motion of the instrument before the experiment and can be used during measurements to circumvent obstacles. The instrument path is calculated in an angular configuration space in order to keep a maximum angular distance from any obstacle. Computer science ; Software ; Obstacle ; Motion planning ; Graphical user interface ; Spectrometer ; Path (computing) ; Computer vision ; Computer graphics (images) ; Computer hardware ; Artificial intelligence ; Physics ; Optics ; Robot ; Operating system Mass Spectrometry Techniques and Applications ; Marine and coastal ecosystems ; Scientific Research and Discoveries
Authors: Marcus M. Noack
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Advanced Gaussian Processes for Function Approximation, Uncertainty Quantification, and Autonomous Experimentation Journal Article Advanced Gaussian Processes for Function Approximation, Uncertainty Quantification, and Autonomous Experimentation Get access Marcus Noack Marcus Noack Lawrence Berkeley National Laboratory, Applied Mathematics and Computational Research Division, Berkeley, CA, USA Corresponding author: MarcusNoack@lbl.gov Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Page 1917, https://doi.org/10.1093/micmic/ozad067.990 Published: 22 July 2023 Function (biology) ; Gaussian ; Gaussian process ; Computer science ; Statistical physics ; Physics ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Zhiling Zheng | He Zhiguo | Omar Khattab | Nakul Rampal | Matei Zaharia | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: arXiv (Cornell University) (preprint, 2023-12-09)
Field: Materials Science — Materials Chemistry

Image and Data Mining in Reticular Chemistry Using GPT-4V The integration of artificial intelligence into scientific research has reached a new pinnacle with GPT-4V, a large language model featuring enhanced vision capabilities, accessible through ChatGPT or an API. This study demonstrates the remarkable ability of GPT-4V to navigate and obtain complex data for metal-organic frameworks, especially from graphical sources. Our approach involved an automated process of converting 346 scholarly articles into 6240 images, which represents a benchmark dataset in this task, followed by deploying GPT-4V to categorize and analyze these images using natural language prompts. This methodology enabled GPT-4V to accurately identify and interpret key plots integral to MOF characterization, such as nitrogen isotherms, PXRD patterns, and TGA curves, among others, with accuracy and recall above 93%. The model's proficiency in extracting critical information from these plots not only underscores its capability in data mining but also highlights its potential in aiding the creation of comprehensive digital databases for reticular chemistry. In addition, the extracted nitrogen isotherm data from the selected literature allowed for a comparison between theoretical and experimental porosity values for over 200 compounds, highlighting certain discrepancies and underscoring the importance of integrating computational and experimental data. This work highlights the potential of AI in accelerating scientific discovery and innovation, bridging the gap between computational tools and experimental research, and paving the way for more efficient, inclusive, and comprehensive scientific inquiry. Computer science ; Benchmark (surveying) ; Categorization ; Bridging (networking) ; Data science ; Data mining ; Artificial intelligence ; Information retrieval Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Nanomaterials in Catalysis
Authors: Vuk Uskoković
Source:  (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

YUCOMAT 2023: An International Advisory Board Member’s Digest The 24th edition of the Yugoslav Conference on Materials (YUCOMAT) organized annually by the Materials Research Society of Serbia (MRS-Serbia) was held in Herceg Novi, Montenegro, from 4 September to 8 September 2023. The conference attracted 212 participants and nearly 200 presenters, 17 of which were plenary speakers, while the rest were assigned regular oral or poster presentations. The participants came from over 20 different countries of the world, the most represented among which were Serbia, Ukraine, Poland, Czech Republic, United States, Montenegro, Lithuania, Italy, Republic of Korea, Romania, Germany, and China. Outside of the five plenary sessions at YUCOMAT and a special session dedicated to MXenes, the conference was divided to five symposia: (i) advanced methods in synthesis and processing of materials; (ii) advanced materials for high-technology applications; (iii) nanostructured materials; (iv) eco-materials and eco-technologies; and (v) biomaterials. In this report, a member of the International Advisory Board of MRS-Serbia gives a digest of the conference, alongside providing a few historical remarks. Montenegro ; Plenary session ; Library science ; China ; Session (web analytics) ; Czech ; Political science ; Geography ; Computer science ; Law ; Regional science MXene and MAX Phase Materials ; Titanium Alloys Microstructure and Properties ; Boron and Carbon Nanomaterials Research
Authors: Barbara Błasiak | Armita Dash | Frank C. J. M. van Veggel | Peter Latta | Bogusław Tomanek
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Biomaterials

Molecular magnetic resonance imaging of prostate cancer using Core/Shell nanoparticles and mice animal model Magnetic resonance imaging ; Prostate cancer ; Animal model ; Magnetic nanoparticles ; Nuclear magnetic resonance ; Nanoparticle ; Core (optical fiber) ; Shell (structure) ; Materials science ; Molecular biophysics ; Cancer ; Medicine ; Nanotechnology ; Physics ; Radiology ; Internal medicine Nanoparticle-Based Drug Delivery
Authors: Nevena Ćelić | G.R. Štrbac | Imre Gut | Nenad Tadić | Ondrej Bošák | S.R. Lukić-Petrović
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — General Materials Science

The investigations of mechanical stability of highly transparent UVC-blocking ZnO-SnO2/PMMA nanocomposite coatings Nanocomposite ; Materials science ; Blocking (statistics) ; Composite material ; Nanotechnology ; Optoelectronics ; Computer science Material Properties and Applications
Authors: Magdalena Podolak | Piotr Roszczenko | V. Ya. Horishny | Roman Lesyk | Krzysztof Bielawski | Anna Bielawska
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Anticancer properties of novel Thiazolidinone derivatives tested in MDA-MB-231 breast cancer cell lines. Breast cancer ; Cancer cell lines ; Cancer ; Chemistry ; Cancer research ; Pharmacology ; Cancer cell ; Medicine ; Internal medicine Synthesis and biological activity
Authors: Patryk Nowak | Artur Sikorski
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

New multicomponent crystals derived from acridine and selected hydroxybenzaldehydes: structural diversity and physicochemical examination Acridine ; Diversity (politics) ; Chemistry ; Computer science ; Organic chemistry ; Political science ; Law Synthesis and Characterization of Heterocyclic Compounds ; Nonlinear Optical Materials Research ; Crystallization and Solubility Studies
Authors: Anna Czaja | Magdalena Zdrowowicz | Janusz Rak
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Halogen substituted 4-Thio-2’-Deoxyuridines as photosensitizers: in vitro studies Thio- ; Halogen ; In vitro ; Chemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry ; Biochemistry Organic Chemistry Cycloaddition Reactions ; Synthesis and Catalytic Reactions ; Organoselenium and organotellurium chemistry
Authors: Richárd Katona | G. Bátor | Edit Tóth-Bodrogi | Tibor Kovács
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Electrochemical examination of chemical decontamination technologies in the aspects of radioactive wastes management Human decontamination ; Radioactive waste ; Waste management ; Electrochemistry ; Environmental science ; Radiochemistry ; Chemistry ; Engineering ; Electrode Graphite, nuclear technology, radiation studies
Authors: Malgorzata Rybczynska | Artur Sikorski
Source: Book of Abstracts (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Multicomponent crystals of nimesulide: design, structures and properties Nimesulide ; Materials science ; Computer science ; Chemistry Crystallography and molecular interactions ; Crystal structures of chemical compounds ; Crystallization and Solubility Studies
Authors: Ivan Pešić | Dana Vasiljević-Radović | Sanja Ostojić | Milena Rašljić Rafajilović | Vesna Radojević | Marija V. Pergal
Source: Book of Abstracts (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Preparation of MXene reinforced polymer nanocomposites Nanocomposite ; Materials science ; Polymer ; Composite material MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Graphene research and applications