jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anthony Wang | Mahamad Salah Mahmoud | Mathias Czasny | Aleksander Gurlo
Source:  (preprint, 2021-10-21)
Field: Materials Science — Materials Chemistry

CrabNet for explainable deep learning in materials science: bridging the gap between academia and industry Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike. Interpretability ; Bridging (networking) ; Data science ; Computer science ; Popularity ; Informatics ; Element (criminal law) ; Process (computing) ; Property (philosophy) ; Artificial intelligence ; Similarity (geometry) ; Knowledge management ; Management science ; Epistemology ; Engineering ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Yuanyuan Jiang | Jiali Guo | Yjing Liu | Yanzhi Guo | Menglong Li | Xuemei Pu
Source: ChemRxiv (preprint, 2021-01-05)
Field: Materials Science — Materials Chemistry

A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening &lt;p&gt;Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.&lt;/p&gt;&lt;br&gt; Cocrystal ; Computer science ; Artificial intelligence ; Graph ; Generalization ; A priori and a posteriori ; Feature learning ; Artificial neural network ; Deep learning ; Machine learning ; Deep neural networks ; Representation (politics) ; Theoretical computer science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: M.K. Qasim
Source: Egyptian Journal of Chemistry (article, 2021-12-21)
Field: Chemistry — Spectroscopy

A QSRR model for predicting gas chromatography retention indices of essential oils using an improved chemical reaction optimization algorithm A quantitative structure–retention relationship (QSRR) model with chemical reaction optimization algorithm (CROA) for predicting retention indices (RI) of 169 constituents of essential oils is proposed. The proposed model was internal and external validated based on , , , , Y-randomization test, , , and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptors selection and prediction performance of the proposed model for training dataset outperforms the other two used modeling methods. The proposed model shows the highest , , and , and the lowest . For the test dataset, proposed model shows higher external validation value ( = 0.936), and lower value of compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed model is an efficient approach for modeling high dimensional QSRRs and useful for the estimation of RI of essential oils that have not been experimentally tested. Gas chromatography ; Kovats retention index ; Chromatography ; Chemistry Analytical Chemistry and Chromatography
Authors: Sumith Yesudasan
Source: Preprints.org (preprint, 2021-01-08)
Field: Materials Science — Surfaces, Coatings and Films

Achilles: A Tool for Contact Angle Estimation from Molecular Dynamics Simulations In this work, a tool for estimating the contact angle from the molecular dynamics simulations is developed and presented. The tool (Achilles) can detect water droplet on hydrophobic and hydrophilic surfaces. The tool can reconstruct the droplets broken across the periodic boundaries. Further a neighbor density based accurate filter is used to find the droplet liquid vapor interface and a circle is fitted using it after removing the dense layers of water next to solid surface. This fitted circle is solved for contact angle and results are outputted in the form of graphical images and text. The entire content of the internal computations of the tool is broken down into 4 phases and users can monitor the outcomes at every phase through output images. The tool is tested using sample molecular dynamics results of water droplet on hydrophobic and hydrophilic surfaces. We believe this tool can be a good addition to the molecular dynamics simulation community who work on the interfacial physics, droplet evaporation, super hydrophobic surfaces, and wettability etc. Contact angle ; Molecular dynamics ; Wetting ; Work (physics) ; Evaporation ; Materials science ; Dynamics (music) ; Phase (matter) ; Surface (topology) ; Volume of fluid method ; Chemical physics ; Mechanics ; Chemistry ; Physics ; Geometry ; Composite material ; Thermodynamics ; Acoustics ; Computational chemistry ; Mathematics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; nanoparticles nucleation surface interactions
Authors: Linfei Yin | Dongduan Liu | Mingshan Mo
Source: Research Square (preprint, 2021-11-23)
Field: Materials Science — Materials Chemistry

Adaptive multistep model predictive control for tubular grid-connected solid oxide fuel cells Model predictive control ; Controller (irrigation) ; Grid ; Control theory (sociology) ; Computer science ; Oxide ; Renewable energy ; Materials science ; Engineering ; Control (management) ; Mathematics ; Electrical engineering Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Benjamin P. MacLeod | Fraser G. L. Parlane | Connor C. Rupnow | Kevan E. Dettelbach | Michael S. Elliott | Thomas D. Morrissey | Ted H. Haley | Oleksii Proskurin | Michael B. Rooney | Nina Taherimakhsousi | David Dvořák | Hsi Nien Chiu | Christopher E. B. Waizenegger | Karry Ocean | Mehrdad Mokhtari | Curtis P. Berlinguette
Source: arXiv (Cornell University) (preprint, 2021-06-01)
Field: Materials Science — Materials Chemistry

Advancing the Pareto front for thin-film materials using a self-driving laboratory Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between competing objectives. Here we report a self-driving laboratory, "Ada", that defines the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada identified previously untested combustion synthesis conditions that resulted in the discovery of lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C), a temperature difference that makes the coating of different commodity plastic materials possible (e.g., Nafion, polyethersulfone). These conditions enabled us to use combustion synthesis to spray coat uniform palladium films with moderate conductivity (1.1 $\times$ 10$^5$ S m$^{-1}$) at 191 °C. Spray coating at 226 °C yielded films with conductivities (2.0 $\times$ 10$^6$ S m$^{-1}$) comparable to those of sputtered films (2.0 to 5.8 $\times$ 10$^6$ S m$^{-1}$). This work shows how self-driving laboratories can discover materials satisfying multiple objectives. Combustion ; Coating ; Materials science ; Thin film ; Palladium ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Venkateswaran Shekar | Gareth Nicholas | Mansoor Ani Najeeb | Margaret Zeile | Vincent F. Yu | Xiaorong Wang | Dylan Slack | Zhi Li | Philip Nega | Emory M. Chan | Alexander J. Norquist | Joshua Schrier | Sorelle A. Friedler
Source: ChemRxiv (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Active Meta-Learning for Predicting and Selecting Perovskite Crystallization Experiments Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammoniumn lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models. We then evaluated the best three algorithms (PLATIPUS, and active-learning k-Nearest Neighbor and Decision Tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcome compared to other active-learning algorithms and a random baseline. Machine learning ; Artificial intelligence ; Computer science ; Active learning (machine learning) ; Meta learning (computer science) ; Decision tree ; Probabilistic logic ; Online machine learning ; Set (abstract data type) ; Task (project management) ; Engineering Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Machine Learning and ELM
Authors: Alastair Marshall | Thomas Reisser | Phila Rembold | Christoph R. Müller | Jochen Scheuer | Martin Gierse | Tim R. Eichhorn | Jakob M. Steiner | P. Hautle | Tommaso Calarco | Fedor Jelezko | Martin B. Plenio | Simone Montangero | Ilai Schwartz | Matthias M. Müller | Philipp Neumann
Source: Kölner Universitäts PublikationsServer (Universität zu Köln) (preprint, 2021-12-30)
Field: Chemistry — Spectroscopy

Macroscopic Hyperpolarization Enhanced with Quantum Optimal Control Hyperpolarization of nuclear spins enhances nuclear magnetic resonance signals, which play a key role for imaging and spectroscopy in the natural and life sciences. This signal amplification unlocks previously inaccessible techniques, such as metabolic imaging of cancer cells. In this work, electron spins from the photoexcited triplet state of pentacene-doped naphthalene crystals are used to polarize surrounding protons. As existing strategies are rendered less effective by experimental constraints, they are replaced with optimal control pulses designed with RedCRAB. In contrast to previous optimal control approaches, which consider an average single nucleus, this closed-loop optimization is macroscopic. A 28% improvement in signal and 15% faster polarization rate is observed. Additionally, a strategy called Autonomously-optimized Repeated Linear Sweep (ARISE) is introduced to efficiently tailor existing hyperpolarization sequences in the presence of experimental uncertainty to enhance their performance. ARISE is expected to be broadly applicable in many experimental settings. Hyperpolarization (physics) ; Spins ; Polarization (electrochemistry) ; Physics ; Quantum ; Optimal control ; Spectroscopy ; Nuclear magnetic resonance ; Computer science ; Biological system ; Chemistry ; Materials science ; Nuclear magnetic resonance spectroscopy ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Biology ; Mathematical optimization Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Electron Spin Resonance Studies
Authors: Mohammad J. Eslamibidgoli | Fabian P. Tipp | Jenia Jistev | Jasna Janković | Michael Eikerling | Kourosh Malek
Source:  (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for High Throughput Screening of Catalyst Layer Inks for Polymer Electrolyte Fuel Cells The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, in particular the catalyst layers. Essential structural building blocks of catalyst layers are formed during processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and employment of data augmentation that works on multiple scales in the training of robust and accurate classification pipelines. Convolutional neural network ; Computer science ; Throughput ; Context (archaeology) ; Layer (electronics) ; Characterization (materials science) ; Inkwell ; Electrolyte ; Materials science ; Nanotechnology ; Deep learning ; Fabrication ; Electrode ; Artificial intelligence ; Chemistry ; Wireless ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Georgios A. Tritsaris | Stephen Carr | Gabriel R. Schleder
Source: Applied Physics Reviews (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Computational design of moiré assemblies aided by artificial intelligence Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrary layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a bandgap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies. Stacking ; Graphene ; Materials science ; Moiré pattern ; Band gap ; Layer (electronics) ; Nanotechnology ; Computer science ; Optoelectronics ; Physics Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Aryan Deshwal | Cory M. Simon | Janardhan Rao Doppa
Source: ChemRxiv (preprint, 2021-06-23)
Field: Chemistry — Inorganic Chemistry

Bayesian optimization of nanoporous materials Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to find the optimal NPM in a library of NPMs using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure--property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment, while balancing exploitation and exploration. We demonstrate BO by searching a database of covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity. Nanoporous ; Bayesian optimization ; Property (philosophy) ; Probabilistic logic ; Surrogate model ; Function (biology) ; Computer science ; Bayesian probability ; Adsorption ; Task (project management) ; Methane ; Mathematical optimization ; Artificial intelligence ; Materials science ; Machine learning ; Mathematics ; Nanotechnology ; Chemistry ; Engineering ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Ijbpas | N | X Deng | M Song | C Wei | N Amnerkar | K Bhusari | G Sreenivasa | E Jayachandran | B Shivakumar | M Choi | E Kim | H Hahn | T Jiang | K Kuhen | K Wolff | H Yin | K Bieza | J Caldwell | B Bursulaya | T Tuntland | K Zhang | D Karanewsky | Y | M Verma | S Nath Pandeya | K Singh | J Stables | A Raj | R Raghunathan | M Sridevikumaria | N Raman | R Tripathy | A Reiboldt | P Messina | M Iqbal | J Singh | E Bacon | T Angeles | S Yang | M Albom | C Robinson | H Chang | B Ruggeri | J Mallamo | T Bal | B Anand | P Yogeeswari | D Sriram | G Cecile | D Douguet | V Huteau | M Gilles | M Helene | P Sylvie | S Murray | M Pandey | A Mazza | F Adams | A Pesquera | Steven | Q Chen | X Zhu | L Jiang | L Yang | G Fu | Mmm | Santos | Ma | M Borad | N Bhoi | H Prajapati | Patel | D Bhoi | S Rajani | H Rajani | A Agarwal | R Ashutosh | N Goyal | M Chauhana | S Gupta | P Selvam | M Chandramohan | E Clercq | M Witvrouw | C Pannecouque | Z Che | Y Tian | S Liu | M Hu | G Chen
Source: International Journal of Biology Pharmacy and Allied Sciences (article, 2021-09-25)
Field: Chemistry — Organic Chemistry

SYNTHESIS, MOLECULAR DOCKING, AND BIOLOGICAL EVALUATION OF SOME NOVELINDOLE DERIVATIVES CONTAIN 1,3-THIAZOLE In present study, microwave assistedsynthesis of some novel Indole derivatives contain 1, 3thiazole was performed. All the synthesized products were purified through column chromatography and structures of these compounds were characterized by IR, 1 H NMR and mass spectral data. All the final derivatives were screened for their anticancer, antimicrobial activity and Molecular docking studies. All their efficacywas compared with standard drugs.The synthesized compound 4b, 4e, 4i and 4j are showed good Anticanceractivities whereas others exhibited significant activities. The compound 4c, 4d, 4e and 4i are has showed maximum antibacterial activity compare with streptomycin as a standard drug. The molecular docking studies of these novel derivatives of indole showed good agreement with the biological results when their binding pattern and affinity towards the active site of EGFR was investigate. Molecular docking study has been performed for all the synthesized compounds with MCFAcetylcholine-binding protein (AChBP) and results obtained are quite promising. Thiazole ; Docking (animal) ; Combinatorial chemistry ; Chemistry ; Computational biology ; Pharmacology ; Stereochemistry ; Biology ; Medicine Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Biological Evaluation
Authors: Eugen Hruška | Ariel Gale | Fang Liu
Source:  (preprint, 2021-10-08)
Field: Materials Science — Materials Chemistry

Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations. Redox ; Solvent ; Solvation ; Chemistry ; Density functional theory ; Solvent models ; Implicit solvation ; Solvent effects ; Computational chemistry ; Chemical physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants