jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guan-Ming Chen | Zi-Hao Ye | Zhiming Li | Junliang Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Universal Descriptors of 'Quasi Transition States' for Small-Data-Driven Asymmetric Catalysis Prediction in Machine Learning Model Transition (genetics) ; Computer science ; Transition state ; Catalysis ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Topic Modeling
Authors: Julian Götz | E. G. Richards | Iain A. Stepek | Yu Takahashi | Yi‐Lin Huang | Louis Bertschi | Bertran Rubi | Jeffrey W. Bode
Source: ChemRxiv (preprint, 2024-05-12)
Field: Materials Science — Materials Chemistry

Predicting Three-Component Reaction Outcomes from 40k Miniaturized Reactant Combinations Efficient drug discovery relies on accessing diverse small molecules expediently and reliably. Improvements to reliability through machine learning predictions are hampered by poor availability of high-quality reaction data. Here, we introduce an on-demand synthesis platform based on a three-component reaction that delivers drug-like molecules overnight. Miniaturization and automation enable the execution and analysis of 50,000 reactions on a 3 microliter scale with distinct substrates, producing the largest public reaction outcome dataset. With machine learning, we accurately predict the result of unknown reactions and analyze the impact of data set size on model training. This study advances the on-demand synthesis of drug-like molecules through concatenating chemoselective reactions and provides a sufficiently large data set to critically evaluate emerging machine learning approaches to predicting chemical reactivity. Computer science ; Reliability (semiconductor) ; Component (thermodynamics) ; Drug discovery ; Automation ; Set (abstract data type) ; Miniaturization ; Scale (ratio) ; Machine learning ; Data mining ; Combinatorial chemistry ; Artificial intelligence ; Nanotechnology ; Chemistry ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Siddharth Sonti | Chenghan Sun | Zekun Chen | Robert M. Kowalski | J. Kowalski | Davide Donadio | Surl-Hee Ahn | Ambarish Kulkarni
Source: Journal of Chemical Theory and Computation (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Stability and Dynamics of Zeolite-Confined Gold Nanoclusters Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains <i>ab initio</i> accuracy across a range of temperatures (300-1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts. Nanoclusters ; Zeolite ; Nanotechnology ; Stability (learning theory) ; Molecular dynamics ; Chemical physics ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Catalysis ; Machine learning ; Organic chemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: An Niza El Aisnada | Kajjana Boonpalit | Robin van der Kruit | Koen M. Draijer | Jon López-Zorrilla | Masahiro Miyauchi | Akira Yamaguchi | Nongnuch Artrith
Source: The Journal of Physical Chemistry C (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Cost-Effective Strategy of Enhancing Machine Learning Potentials by Transfer Learning from a Multicomponent Data Set on ænet-PyTorch Machine learning potentials (MLPs) offer efficient and accurate material simulations, but constructing the reference ab initio database remains a significant challenge, particularly for catalyst-adsorbate systems. Training an MLP with a small data set can lead to overfitting, thus limiting its practical applications. This study explores the feasibility of developing computationally cost-effective and accurate MLPs for catalyst-adsorbate systems with a limited number of ab initio references by leveraging a transfer learning strategy from subsets of a comprehensive public database. Using the Open Catalyst Project 2020 (OC20)-a data set closely related to our system of interest-we pretrained MLP models on OC20 subsets using the ænet-PyTorch framework. We compared several strategies for database subset selection. Our findings indicate that MLPs constructed via transfer learning exhibit better generalizability than those constructed from scratch, as demonstrated by the consistency in the dynamics simulations. Remarkably, transfer learning enhances the stability and accuracy of MLPs for the CuAu/H<sub>2</sub>O system with approximately 600 reference data points. This approach achieved excellent extrapolation performance in molecular dynamics simulations for the larger CuAu/6H<sub>2</sub>O system, maintaining stable and accurate predictions for up to 250 ps, whereas MLPs without transfer learning become unstable before reaching 50 ps. We also examine the potential limitations of this strategy. This work proposes an alternative, cost-effective approach for constructing MLPs for the challenging simulation of catalytic systems. Finally, we anticipate that this methodology will pave the way for broader applications in materials science and catalysis research, facilitating more efficient and accurate simulations across various systems. Computer science ; Set (abstract data type) ; Transfer of learning ; Artificial intelligence ; Net (polyhedron) ; Machine learning ; Transfer (computing) ; Data set ; Mathematics ; Parallel computing ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yonghyuk Lee | Xiaobo Chen | Sabrina M. Gericke | Meng Li | Dmitri N. Zakharov | Ashley R. Head | Judith Yang | Anastassia N. Alexandrova
Source: ChemRxiv (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Machine-Learning-Driven Exploration of Surface Reconstructions of Reduced Rutile TiO2 Titanium dioxide (TiO2) is widely used as catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO2 surfaces undergo complex surface reconstructions that endow unique properties, but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results experimentally. To illustrate their catalytic implications, we examined the behavior of Rh single atoms on the reconstructed surfaces, focusing on CO2 activation, the rate-limiting step in the RWGS reaction. Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO2, with potential implications for catalyst design. Rutile ; Catalysis ; Materials science ; Anatase ; Workflow ; Phase diagram ; Limiting ; Oxygen ; Titanium dioxide ; Oxygen storage ; Nanotechnology ; Surface (topology) ; Chemical engineering ; Chemical physics ; Phase (matter) ; Computer science ; Chemistry ; Mechanical engineering ; Composite material ; Geometry ; Engineering ; Mathematics Machine Learning in Materials Science ; Cloud Computing and Resource Management
Authors: Gianmarco Terrones | Shih-Peng Huang | Matt Rivera | Shuwen Yue | Alondra Hernandez | Heather J. Kulik
Source: ChemRxiv (preprint, 2024-04-22)
Field: Chemistry — Inorganic Chemistry

WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water in air and the environment. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and artificially limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve generality of predictions but require diverse experimental MOF stability data to be trained. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semi-automated manuscript analysis to curate the new data set WS24. The additional data is shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with previously developed activation stability models to carry out genetic algorithms to quickly screen ~10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (i.e., for activation, in water, and in acid). Model analysis and genetic algorithm results uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment. Data set ; Set (abstract data type) ; Stability (learning theory) ; Computer science ; Data mining ; Environmental science ; Artificial intelligence ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Advanced Nanomaterials in Catalysis
Authors: Edgar Acuña | Christopher A. Kendziora | Robert Furstenberg | Christopher J. Breshike | Drew C. Kendziora
Source:  (article, 2024-06-07)
Field: Chemistry — Analytical Chemistry

Machine learning algorithms for analytes classification based on simulated spectra We seek to detect and classify chemical threats based on their infrared spectra. Specifically, we are interested in utilizing spectral signatures observed with standoff technologies that interrogate analyte micro-particles on relevant substrate surfaces such as glass, metal and plastics. In this work, we have applied six Machine Learning algorithms to classify analytes based on their infrared spectra. Two synthetic datasets were used, the first one containing 40 analytes and the second one containing 55 analytes. In both datasets the analytes were synthetically placed onto 9 substrates. The 40 analytes dataset contains 18,000 spectra, 450 for each analyte with mass loading varying from 1 to 50 &mu;g/cm<sup>2</sup>. The 55 analytes dataset consists of 49,500 spectra, 900 for each analyte and mass loadings in the range 1 to 100 &mu;g/cm<sup>2</sup>. Two of the algorithms used in this work are coming from the statistical field; k nearest neighbors (k-NN) and Logistic Regression. The Support Vector Machine algorithm was developed by the Machine Learning community. Multilayer Perceptrons (MLP) as well as Convolutional Neural Networks are considered Deep Learning Algorithms. In addition to that, we have considered the hybrid deep learning algorithm one dimensional CNN-LSTM. Our experimental results lead us to the conclusion that k-NN and logistic regression outperform deep learning algorithms for our synthetic data sets. However, after dimensionality reduction using PCA, the accuracy of k-NN decreases and the performance of deep learning algorithms improves. We also considered the effect of mass loadings and added noise on the performance of the classifiers. Computer science ; Artificial intelligence ; Machine learning ; Analyte ; Algorithm ; Pattern recognition (psychology) ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Drew Behrendt | Sayan Banerjee | Jiahao Zhang | Andrew M. Rappe
Source: Physical Review Materials (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Discovery of wurtzite solid solutions with enhanced piezoelectric response using machine learning While many piezoelectric materials are known, there is still great potential to improve on the figures of merit of existing materials through compositional doping and forming solid solutions. Specifically, it has been shown that doping and alloying wurtzite-structured materials can improve the piezoelectric response; however, a vast compositional space has remained unexplored. In this work, we apply a multilevel screening protocol combining machine learning, chemical intuition, and thermodynamics to systematically discover dopant combinations in the wurtzite material space that improve the desired piezoelectric response. Through our protocol, we use computationally inexpensive screening calculations to consider more than 3000 possible ternary wurtzite solid solutions from nine different wurtzite base systems: AlN, BeO, CdS, CdSe, GaN, ZnO, ZnS, ZnSe, and AgI. Finally, based on thermodynamic analysis and explicit piezoelectric response calculations, we predict 11 materials with improved piezoelectric response, due to the incorporation of electropositive dopants. Wurtzite crystal structure ; Piezoelectricity ; Materials science ; Dopant ; Ternary operation ; Doping ; Nanotechnology ; Engineering physics ; Optoelectronics ; Zinc ; Computer science ; Composite material ; Metallurgy ; Physics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Susan Meñez Aspera | Gerardo Valadez Huerta | Yūsuke Nanba | Kaoru Hisama | Michihisa KOYAMA
Source: Journal of Computer Chemistry Japan (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning in Catalysis: Analysis and Prediction of CO Adsorption on Multi-elemental Nanoparticle using Metal Coordination-based Regression Model Information about molecular adsorption strength is important in every catalytic reaction. The ability to compare and determine relevant molecular active sites of interaction is necessary for fast screening of potential catalysts specially in a vast spectrum of probable candidates. In this study, we used the metal-coordination of the adsorption sites as a descriptor of the adsorption energy of CO on the PtRuIr ternary alloy nanoparticle. Using multiple regression model, we are able to predict the adsorption energy and specify some important descriptors that controls the strength of CO adsorption energy. This will enable a fast prediction of CO adsorption energy on PtRuIr nanoparticles with varying compositions and possible different morphologies using only the information of the structure of the catalyst. And open up the possibility of predicting adsorption interaction of other combinations of alloys with higher number of metallic compositions for fast screening of appropriate molecule-surface interaction. Adsorption ; Ternary operation ; Catalysis ; Nanoparticle ; Molecule ; Metal ; Materials science ; Chemistry ; Computer science ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xiaoyi Xu | Tao Du | Morten M. Smedskjær
Source: Journal of Non-Crystalline Solids (article, 2024-12-31)
Field: Materials Science — Ceramics and Composites

Mechanical properties of zeolitic imidazolate framework crystal-glass composites: A molecular dynamics study Zeolitic imidazolate framework (ZIF) glasses have potential applications as battery materials or gas separation membranes, but this requires an improvement in their fracture resistance. In this study, we investigate the mechanical properties and fracture mechanism of ZIF crystal-glass composites using molecular dynamics simulations based on a recently developed machine learning force field. The composites are made from ZIF-4 or ZIF-zni crystals embedded in a melt-quenched ZIF-4 glass matrix. ZIF-4 and ZIF-zni share the same chemical composition (ZnIm<sub>2</sub>, where Im is imidazole), but ZIF-zni has a denser crystal structure. By examining ZIF-4 glass based composites with different sizes and shapes of ZIF-4 and ZIF-zni crystals, we study the relationship between atomic-scale structure and mechanical properties. Our findings reveal that the structural differences between ZIF-4 and ZIF-zni crystals lead to distinct mechanical behaviors. The composites based on the stiffer ZIF-zni crystals exhibit greater resistance to irreversible atomic rearrangements compared to those based on ZIF-4 crystals, allowing for crack deflection around the crystals, thereby slightly increasing fracture toughness. Furthermore, the morphology of the crystals plays a crucial role in determining the crack path, influencing both crack deflection and the structural arrangement ability in the elastic state. Overall, the study identifies the key atomic-scale factors, such as the zinc bond switching propensity, for optimizing the mechanical properties of ZIF crystal-glass composites. Composite material ; Zeolitic imidazolate framework ; Materials science ; Molecular dynamics ; Crystal (programming language) ; Imidazolate ; Chemistry ; Metal-organic framework ; Computational chemistry ; Adsorption ; Computer science ; Physical chemistry Glass properties and applications ; Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography
Authors: Clelia Middleton | Basile F. E. Curchod | Thomas J. Penfold
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Partial density of states representation for accurate deep neural network predictions of X-ray spectra The performance of a machine learning (ML) algorithm for chemistry is highly contingent upon the architect's choice of input representation. This work introduces the partial density of states (p-DOS) descriptor: a novel, quantum-inspired structural representation which encodes relevant electronic information for machine learning models seeking to simulate X-ray spectroscopy. p-DOS uses a minimal basis set in conjunction with a guess (non-optimised) electronic configuration to extract and then discretise the density of states (DOS) of the absorbing atom to form the input vector. We demonstrate that while the electronically-focused p-DOS performs well in isolation, optimal performance is achieved when supplemented with nuclear structural information imparted <i>via</i> a geometric representation. p-DOS provides a description of the key electronic properties of a system which is not only concise and computationally efficient, but also independent of molecular size or choice of basis set. It can be rapidly generated, facilitating its application with large training sets. Its performance is demonstrated using a wide variety of examples at the sulphur K-edge, including the prediction of ultrafast X-ray spectroscopic signal associated with photoexcited 2(5<i>H</i>)-thiophenone. These results highlight the potential for ML models developed using p-DOS to contribute to the interpretation and prediction of experimental results <i>e.g. in operando</i> measurements of batteries and/or catalysts and femtosecond time-resolved studies, especially those made possible by emergent cutting-edge technologies, especially X-ray free electron lasers. Spectral line ; Representation (politics) ; Artificial neural network ; X-ray ; Statistical physics ; Pattern recognition (psychology) ; Physics ; Chemistry ; Artificial intelligence ; Computer science ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Patrycja Kwiek | Małgorzata Jakubowska
Source: Algorithms (article, 2024-08-01)
Field: Chemistry — Analytical Chemistry

Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a deep neural network regression model. This approach utilizes a self-designed color template that was developed based on an initial series of studies and digital imaging. Using the equalized histogram of the R, G, B channels of the digital template and its photo, a color mapping strategy was computed. By applying this approach, the histograms were adjusted and the colors of photos taken with a smartphone were standardized. The proposed algorithm was developed for a series of images where the entire surface roughly maintained a uniform color and the differences in color between the photographs of individual objects were minor. This optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for the deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the vitamin C concentration range from 0 to 87.72 µg·mL−1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL−1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48–2.29 µg·mL−1. The consistency of the predicted concentration results with actual data, expressed as R2, ranged between 0.9956 and 0.9999 for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surfaces of solutions, which is a common problem in liquid samples. This color-matching algorithm has universal character, and its scope of application is not limited. Artificial intelligence ; Computer science ; Histogram ; Pattern recognition (psychology) ; Color histogram ; Consistency (knowledge bases) ; Color normalization ; Histogram matching ; Artificial neural network ; Template matching ; Computer vision ; Color image ; Mathematics ; Image (mathematics) ; Image processing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Biosensors and Analytical Detection