jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Surajit Nandi | Jonas Busk | Peter Bjørn Jørgensen | Tejs Vegge | Arghya Bhowmik
Source: Journal of Chemical Information and Modeling (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 data set. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob's ladder. The simple correction method is effective for all rungs but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 data set that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05 eV of the G4MP2 method. Density functional theory ; Linear regression ; Simple (philosophy) ; Computer science ; Set (abstract data type) ; Limit (mathematics) ; Regression ; Data set ; Distribution (mathematics) ; Algorithm ; Applied mathematics ; Computational chemistry ; Biological system ; Chemistry ; Mathematics ; Artificial intelligence ; Machine learning ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Yoshiaki Maeda | Tsuyoshi Tanaka
Source: Marine Biotechnology (review, 2022-04-09)
Field: Materials Science — Biomaterials

Molecular Insights into Lipoxygenases in Diatoms Based on Structure Prediction: a Pioneering Study on Lipoxygenases Found in Pseudo-nitzschia arenysensis and Fragilariopsis cylindrus Diatom ; Nitzschia ; Biology ; Lipoxygenase ; Genome ; Biochemistry ; Enzyme ; Polyunsaturated fatty acid ; Computational biology ; Gene ; Ecology ; Phytoplankton ; Fatty acid Diatoms and Algae Research ; Genomics and Phylogenetic Studies ; Microbial Natural Products and Biosynthesis
Authors: Sirawit Pruksawan | Fuke Wang
Source: Advanced Composite Materials (review, 2022-06-07)
Field: Materials Science — Polymers and Plastics

Modeling of toughening effect in rigid particulate-filled polymer composites by artificial intelligence: a review Low fracture toughness is the Achilles heel of many polymers for practical application, particularly for long-term and special applications such as medical implants and critical part design. The toughening of polymers is a complicated process, and the toughening mechanisms in many composite systems have not yet been fully understood. Machine learning (ML) has emerged as a promising tool for modeling various complex systems in the field of materials science. In this respect, ML appears to be an effective, affordable, and accurate technique in the modeling of the toughening process. This review article provides an overview of the recent ML-based approach for analyzing toughening effects in rigid particulate-filled polymer composites. The results summarized here would contribute to further possibilities for researchers to explore emerging trends for designing toughing and high-performance polymers. Toughening ; Materials science ; Composite material ; Composite number ; Polymer ; Particulates ; Fracture toughness ; Toughness ; Heel ; Process (computing) ; Computer science ; Structural engineering Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes ; Polymer crystallization and properties
Authors: Tomoki Hirosawa | Frank Schäfer | Hideaki Maebashi | Hiroyasu Matsuura | Masao Ogata
Source: Journal of the Physical Society of Japan (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Data-Driven Reconstruction of Spectral Conductivity and Chemical Potential Using Thermoelectric Transport Properties The spectral conductivity, i.e., the electrical conductivity as a function of the Fermi energy, is a cornerstone in determining the thermoelectric transport properties of electrons. However, the spectral conductivity depends on sample-specific properties such as carrier concentrations, vacancies, charge impurities, chemical compositions, and material microstructures, making it difficult to relate the experimental result with the theoretical prediction directly. Here, we propose a data-driven approach based on machine learning to reconstruct the spectral conductivity and chemical potential from the thermoelectric transport data. Using this machine learning method, we first demonstrate that the spectral conductivity and temperature-dependent chemical potentials can be recovered within a simple toy model. In a second step, we apply our method to experimental data in doped one-dimensional telluride Ta4SiTe4 [Inohara, et al., Appl. Phys. Lett. 110, 183901 (2017)] to reconstruct the spectral conductivity and chemical potential for each sample. Furthermore, the thermal conductivity of electrons and the maximal figure of merit ZT are estimated from the reconstructed spectral conductivity, which provides accurate estimates beyond the Wiedemann–Franz law. Our study clarifies the connection between the thermoelectric transport properties and the low-energy electronic states of real materials, and establishes a promising route to incorporate experimental data into traditional theory-driven workflows. Thermoelectric effect ; Conductivity ; Materials science ; Thermoelectric materials ; Thermal conductivity ; Electron ; Condensed matter physics ; Thermal conduction ; Seebeck coefficient ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Bahdan Zviazhynski | G. J. Conduit
Source: Applied Intelligence (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Unveil the unseen: Exploit information hidden in noise Abstract Noise and uncertainty are usually the enemy of machine learning, noise in training data leads to uncertainty and inaccuracy in the predictions. However, we develop a machine learning architecture that extracts crucial information out of the noise itself to improve the predictions. The phenomenology computes and then utilizes uncertainty in one target variable to predict a second target variable. We apply this formalism to PbZr 0.7 Sn 0.3 O 3 crystal, using the uncertainty in dielectric constant to extrapolate heat capacity, correctly predicting a phase transition that otherwise cannot be extrapolated. For the second example – single-particle diffraction of droplets – we utilize the particle count together with its uncertainty to extrapolate the ground truth diffraction amplitude, delivering better predictions than when we utilize only the particle count. Our generic formalism enables the exploitation of uncertainty in machine learning, which has a broad range of applications in the physical sciences and beyond. Computer science ; Exploit ; Diffraction ; Ground truth ; Formalism (music) ; Amplitude ; Noise (video) ; Artificial intelligence ; Statistical physics ; Machine learning ; Optics ; Physics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Time Series Analysis and Forecasting
Authors: Yu Wang | Chao Pang | Yuzhe Wang | Yi Jiang | Junru Jin | Sirui Liang | Quan Zou | Leyi Wei
Source: arXiv (Cornell University) (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their applications in real retrosynthesis scenarios. Here, we propose MechRetro, a chemical-mechanism-driven graph learning framework for interpretable retrosynthetic prediction and pathway planning, which learns several retrosynthetic actions to simulate a reverse reaction via elaborate self-adaptive joint learning. By integrating chemical knowledge as prior information, we design a novel Graph Transformer architecture to adaptively learn discriminative and chemically meaningful molecule representations, highlighting the strong capacity in molecule feature representation learning. We demonstrate that MechRetro outperforms the state-of-the-art approaches for retrosynthetic prediction with a large margin on large-scale benchmark datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we identify efficient synthetic routes via an interpretable reasoning mechanism, leading to a better understanding in the realm of knowledgeable synthetic chemists. We also showcase that MechRetro discovers a novel pathway for protokylol, along with energy scores for uncertainty assessment, broadening the applicability for practical scenarios. Overall, we expect MechRetro to provide meaningful insights for high-throughput automated organic synthesis in drug discovery. Retrosynthetic analysis ; Artificial intelligence ; Computer science ; Machine learning ; Graph ; Margin (machine learning) ; Mechanism (biology) ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Piers A. Townsend | Elliot H. E. Farrar | Matthew N. Grayson
Source: ACS Omega (article, 2022-07-21)
Field: Chemistry — Organic Chemistry

Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment Fast and accurate computational approaches to predicting reactivity in sulfa-Michael additions are required for high-throughput screening in toxicology (e.g., predicting excess aquatic toxicity and skin sensitization), chemical synthesis, covalent drug design (e.g., targeting cysteine), and data set generation for machine learning. The kinetic glutathione chemoassay is a time-consuming in chemico method used to extract kinetic data in the form of log(<i>k</i> <sub>GSH</sub>) for organic electrophiles. In this work, we use density functional theory to compare the use of transition states (TSs) and enolate intermediate structures following C-S bond formation in the prediction of log(<i>k</i> <sub>GSH</sub>) for a diverse group of 1,4 Michael acceptors. Despite the widespread use of transition state calculations in the literature to predict sulfa-Michael reactivity, we observe that intermediate structures show much better performance for the prediction of log(<i>k</i> <sub>GSH</sub>), are faster to calculate, and easier to obtain than TSs. Furthermore, we show how linear combinations of atomic charges from the isolated Michael acceptors can further improve predictions, even when using inexpensive semiempirical quantum chemistry methods. Our models can be used widely in the chemical sciences (e.g., in the prediction of toxicity relevant to the environment and human health, synthesis planning, and the design of cysteine-targeting covalent inhibitors), and represent a low-cost, sustainable approach to reactivity assessment. Reactivity (psychology) ; Electrophile ; Covalent bond ; Michael reaction ; Chemistry ; Computer science ; Computational chemistry ; Cysteine ; Quantitative structure–activity relationship ; Human health ; Glutathione ; Combinatorial chemistry ; Biochemical engineering ; Machine learning ; Organic chemistry ; Catalysis ; Enzyme Sulfur-Based Synthesis Techniques ; Chemical Reaction Mechanisms ; Organic and Inorganic Chemical Reactions
Authors: Indranil Roy | Bojun Feng | Subhrajit Roychowdhury | Sandipp Krishnan Ravi | Rajnikant V. Umretiya | Christopher Reynolds | Sayan Ghosh | Raúl B. Rebak | Andrew Hoffman
Source: Research Square (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

Understanding oxidation of Fe-Cr-Al alloys through Explainable Artificial Intelligence Abstract The effect of alloy composition and oxidation condition on specific mass gain of FeCrAl alloys was studied and analyzed using a combination of experimental and AI approaches. A Neural Network (NN) classification model was used on the experimental FeCrAl dataset produced at GE Research from steam oxidation studies at both high (~ 1000°C) and low temperature (~ 400°C). Furthermore, using the Shapley Additive exPlanations (SHAP) explainable Artificial Intelligence (XAI) tool, we explore how the NN can identify an alloy at specific oxidation condition to form a protective oxide or not. We found high Al and Cr concentration increases the chances of forming protective oxide layer, which is consistent with literature studies. Contrary to Al and Cr, the presence of Mo in FeCrAl creates thick unprotective oxide scale that results in high mass gain per unit area. Oxide ; Alloy ; Artificial neural network ; Layer (electronics) ; Materials science ; Scale (ratio) ; Chemical engineering ; Artificial intelligence ; Computer science ; Metallurgy ; Biological system ; Nanotechnology ; Physics ; Engineering Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors
Authors: Vimal Kumar Deshmukh | Mridul Singh Rajput | Harendra Kumar Narang
Source: Advances in Materials and Processing Technologies (article, 2022-10-12)
Field: Chemistry — Electrochemistry

Performance evaluation and comparison of machine learning algorithms for prediction of electrodeposited copper ions Machine-learning prediction algorithms have been used in different scenarios by earlier researchers. This article found the application of these algorithms for predictions of the material deposition rate of copper ions through high-speed selective jet electrodeposition (HSSJED). The presented article emphasises the selection of the best suitable machine-learning prediction algorithms among Gaussian Process Regression (GPR), Support Vector Machine (SVM) regression and Linear Regression (LR) for prediction and compares them with each other for selection of the best algorithm which agrees with experimental data. The comparison of performances of these machine-learning algorithms has been done taking root-mean-square error (RMSE), R2, mean-squared error (MSE) and mean absolute error (MAE) as a basic measure for the same, also various graphs have been plotted and discussed for better understanding. The data set was collected by performing the HSSJED experiment on various parameters like electrolyte composition, electrode gap and applied dc potential. K-fold cross-validation method has been adopted for validation. GPR predictions were found better than the other two which also agree with the experimental output as the quantitative comparison has been done and results are discussed. The average values of RMSE, R2, MSE and MAE were 0.057668, 0.96, 0.0032432 and 0.045733 for GPR. Mean squared error ; Kriging ; Support vector machine ; Machine learning ; Algorithm ; Linear regression ; Artificial intelligence ; Regression ; Computer science ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Statistical and Computational Modeling ; Advanced Statistical Process Monitoring
Authors: Nathan C. Drucker | Tongtong Liu | Zhantao Chen | Ryotaro Okabe | Abhijatmedhi Chotrattanapituk | Thanh Nguyen | Yao Wang | Mingda Li
Source: Synchrotron Radiation News (article, 2022-07-04)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering Machine learning has been highly successful in boosting the re-search for neutron and X-ray scattering in the past few years [1, 2]. Fordiffraction, machine learning has shown great promise in phase map-ping [3, 4] and crystallographic information determination [5, 6]. Insmall-angle scattering, machine learning shows the power in reachingsuper-resolution [7, 8], reconstructing structures for macromolecules[9], and building structure-property relations [10]. As for absorptionspectroscopy, machine learning has enabled the rapid inverse searchfor optimized structures [11, 12] with improved spectral interpretability[13, 14]. Overall, as a data-driven approach, the success of the machine-learning-based scattering analysis depends on a few criteria, including:• Quantity of available experimental data, and feasibility to extractcertain data labels;• Quality of experimental data that can separate the intrinsic effect(e.g., materials properties) from extrinsic influence (e.g., instru-mental or data artifacts);• Feasibility to generate high volume of computational data;• Accuracy of computational data that can simulate the experimen-tal data. Neutron scattering ; Neutron ; X-ray ; Scattering ; Nuclear physics ; Materials science ; Small-angle neutron scattering ; Nuclear engineering ; Physics ; Optics ; Engineering Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Geunwoo Kim | Ryo Yamada | Tomohiro Takaki | Yasushi Shibuta | Munekazu Ohno
Source: Computational Materials Science (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning Anisotropy ; Isothermal process ; Dendrite (mathematics) ; Convolutional neural network ; Inverse ; Materials science ; Phase (matter) ; Artificial neural network ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Statistical physics ; Algorithm ; Biological system ; Thermodynamics ; Physics ; Mathematics ; Optics ; Geometry ; Statistics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Jamie Milne | Qian Chen | David Hargreaves | Yinhai Wang | Julie Wilson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-28)
Field: Materials Science — Materials Chemistry

Not getting in too deep: a practical deep learning approach to routine crystallisation image classification Abstract Using a relatively small training set of ∼16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions. Author summary X-ray crystallography can provide structural information on the molecules that play important roles in cell function and allow interactions vital for drug design to be elucidated. However, the technique requires the molecules to be crystallised and obtaining sufficiently high-quality crystals can require hundreds of experiments under different conditions. Robots have been designed to perform the microscopic experiments with imaging systems to record the results over time but automated classification of all these images is becoming essential. In this work we compare different classifiers that can be used without the need for vast computational resources and show that an ensemble classifier which combines the different strengths of four deep-learning networks is able to correctly classify the experimental results into one of eight different categories 94% of the time. Classifier (UML) ; Computer science ; Artificial intelligence ; Deep learning ; Machine learning ; Pattern recognition (psychology) ; Ensemble learning ; Data mining Machine Learning in Materials Science ; Enzyme Structure and Function ; Computational Drug Discovery Methods
Authors: Hang Hu | David Helminiak | Manxi Yang | Daisy Unsihuay | Ryan T. Hilger | Dong Hye Ye | Julia Laskin
Source: ChemRxiv (preprint, 2022-06-16)
Field: Chemistry — Spectroscopy

High-Throughput Mass Spectrometry Imagingwith Dynamic Sparse Sampling Mass spectrometry imaging (MSI) enables label-free mapping for hundreds of molecules in biological samples, with high sensitivity and unprecedented specificity. Conventional MSI experiments are relatively slow, limiting their utility for applications requiring rapid data acquisition, such as intraoperative tissue analysis or 3D imaging. Recent advances in MSI technology focus on improving spatial resolution and molecular coverage, further increasing acquisition times. Herein, a deep learning approach for dynamic sampling (DLADS) reduces the number of required measurements to improve MSI throughput, in comparison with conventional methods. DLADS trains a deep learning model to dynamically predict molecularly informative tissue locations for active mass spectra sampling and reconstructs high-fidelity molecular images, using only the sparsely sampled information. Hardware and software integration of DLADS with nanospray desorption electrospray ionization (nano-DESI) MSI demonstrates a 2.3-fold improvement in throughput with a line-wise acquisition mode. Meanwhile, simulations indicate that a 5 to 10-fold throughput improvement may be achieved using the pointwise acquisition mode. Mass spectrometry imaging ; Throughput ; Computer science ; Data acquisition ; Software ; Sampling (signal processing) ; Mass spectrometry ; Focus (optics) ; Artificial intelligence ; Computer hardware ; Computational science ; Chemistry ; Computer vision ; Physics ; Optics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science