jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Neha Ujjwal | Anshbir Singh | Raj Gaurang Tiwari
Source: 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (article, 2022-11-20)
Field: Materials Science — Materials Chemistry

Perceptive Classification for Crystal Structure of ABO3 perovskite-type Oxides Using Machine Learning The goal of machine learning (ML) is to teach a computer system to execute tasks on its own. A machine learning algorithm may be found practically everywhere these days. A frequent application of machine learning algorithms is to extract data and uncover patterns in the data that reveal correlations between different components. Supervised, unsupervised, semi-supervised, and reinforcement learning are all forms of machine learning. Classification, regression, and clustering issues may be solved using ML techniques. Machine learning techniques has been used in this work to predict the lattice type of ABO3 perovskite-type oxide lattices and to determine which approach does this job with the best accuracy. ABO3 perovskite-type oxides of 4,165 samples were used to train the system. There are 13 columns of data for each observation, with a single class column indicating whether the structure is cubic, tetragonal, orthorhombic, or rhombic. Based on the results of the experiments, the Random Forest model performs better than competitors. Machine learning ; Artificial intelligence ; Computer science ; Unsupervised learning ; Tetragonal crystal system ; Learning classifier system ; Cluster analysis ; Computational learning theory ; Orthorhombic crystal system ; Perovskite (structure) ; Crystal structure ; Chemistry ; Crystallography Machine Learning in Materials Science ; Machine Learning and ELM ; Water Quality Monitoring and Analysis
Authors: Maitreyee Sharma Priyadarshini | Simone Venturi | Ivan Zanardi | Marco Panesi
Source: ChemRxiv (preprint, 2022-11-17)
Field: Materials Science — Materials Chemistry

Efficient Quasi-Classical Trajectory Calculations by means of Neural Operator Architectures An accurate description of non-equilibrium chemistry relies on rovibrational state-to-state (StS) kinetics data, which can be obtained through the quasi-classical trajectory (QCT) method for high-energy collisions. However, these calculations still represent one of the major computational bottlenecks in predictive simulations of non-equilibrium reacting gases. This work addresses this limitation by proposing SurQCT, a novel machine learning-based surrogate for efficiently and accurately predicting StS chemical reaction rate coefficients. The QCT emulator is constructed using three independent components: two deep operator networks (DeepONets) for inelastic and exchange processes and a feed-forward neural network (FNN) for the dissociation reactions. SurQCT is tested on the O$_2$+O system, showing a computational speed-up of 85\%. Furthermore, we carry out a StS master equation analysis of an isochoric, isothermal heat bath simulation at various temperatures to study how the predicted rate coefficients impact the accuracy of multiple quantities of interest (QoIs) at the kinetics level (\textit{e.g.}, global quasi-steady state (QSS) dissociation rate coefficients and energy relaxation times). For all these QoIs, the master equation analysis relying on SurQCT data shows an accuracy within 15\% across the entire temperature regime. Isochoric process ; Dissociation (chemistry) ; Artificial neural network ; Computer science ; Operator (biology) ; Isothermal process ; Work (physics) ; Master equation ; Rotational–vibrational spectroscopy ; Thermodynamics ; Applied mathematics ; Chemistry ; Physics ; Mathematics ; Physical chemistry ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Shu-Hui Liu | Fan Zhang | Ronghe Lin | Wei Liu
Source: Chemical Research in Chinese Universities (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Full Metal Species Quantification of Metal Supported Catalysts Through Massive TEM Images Recognition Catalysis ; Metal ; Nanoparticle ; Dispersion (optics) ; Materials science ; Transmission electron microscopy ; Atom (system on chip) ; Carbon fibers ; Nanotechnology ; Chemical physics ; Chemical engineering ; Chemistry ; Metallurgy ; Composite material ; Organic chemistry ; Composite number ; Computer science ; Physics ; Optics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Xue Jiang | Yongzhi Zhao | Jianfang Liu | Baorui Jia | Xuanhui Qu | Mingli Qin
Source: ACS Applied Nano Materials (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures Machine learning (ML) has been transforming materials science, making great strides in property prediction and materials discovery; however, how ML can bring benefits to nanostructure synthesis, given that the synthesis routines of nanostructures are usually complex with strongly correlated, highly dimensional parameters with narrow experimental window, is rarely explored. Here, we choose hollow nanospheres and emulsion interfacial polymerization as the prototype of material and synthesis routine and propose an ML method to capture synthetic intuitions for guiding the preparation. The quantitative relationship between the reactants, synthesis parameters, and the product morphologies is captured, and the contributions of each feature are analyzed to optimize the reactant amounts and synthesis parameters for hollow and homogeneous spheres. The experimental results are generally consistent with the ML predictions, with the f1-scores of 0.750 and 0.607 for hollow/solid and homogeneity classification, respectively. The obtained hollow carbon spheres show a superior oxygen reduction electrocatalytic performance than solid spheres owing to their improved active site numbers. With the increasing structural complexity of nanomaterials, ML-driven synthetic design will endow great potential and promising prospects. Nanostructure ; SPHERES ; Materials science ; Nanomaterials ; Nanotechnology ; Homogeneous ; Homogeneity (statistics) ; Polymerization ; Emulsion polymerization ; Computer science ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Polymer ; Engineering ; Composite material ; Aerospace engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Nency P. Domingues | Seyed Mohamad Moosavi | Leopold Talirz | Christopher P. Ireland | Fatmah Mish Ebrahim | Berend Smit
Source: ChemRxiv (preprint, 2022-06-21)
Field: Chemistry — Inorganic Chemistry

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to obtain the optimal synthesis of a MOF. A synthetic conditions finder was used to derive the experimental protocols and a microwave based high-throughput robotic platform was used for the synthesis of Al-PMOF ([H2TCPP[AlOH]2(DMF3(H2O)2)]). Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). In this work, we carried out a systematic search for the optimal reaction conditions using a microwave assisted reaction synthesis. For this search we used a genetic algorithm and we show that already in the 2nd generation we obtained conditions that give excellent crystallinity and yield close to 80% in much shorter reaction time (50 minutes). In addition, by analysing the failed and partly successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield. Crystallinity ; Yield (engineering) ; Throughput ; Microwave ; Hydrothermal synthesis ; Genetic algorithm ; Work (physics) ; Reaction conditions ; Computer science ; Algorithm ; Materials science ; Hydrothermal circulation ; Nanotechnology ; Biological system ; Machine learning ; Chemistry ; Chemical engineering ; Engineering ; Organic chemistry ; Catalysis ; Mechanical engineering ; Composite material ; Biology ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Multicomponent Synthesis of Heterocycles
Authors: Daniil A. Boiko | Thiago Reschützegger | Benjamín Sánchez-Lengeling | Samuel M. Blau | Gabriel dos Passos Gomes
Source: ChemRxiv (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Stereoelectronics-Aware Molecular Representation Learning The representation of molecular structures is crucial for molecular machine learning strategies. Although graph representations are highly versatile and show their broad applicability, they lack information about the quantum-chemical properties of molecular structures. This work proposes a new way to infuse such information into molecular graphs, using a supervised learning method. As a result, the model is able to predict essential higher-order interactions between electron-rich and electron-deficient localized orbitals. The learned interactions are then used as a representation for the prediction of downstream tasks, improving over QM9 baselines. Representation (politics) ; Computer science ; Molecular graph ; Graph ; Theoretical computer science ; Artificial intelligence ; Feature learning ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Alexandra Wahab | Lara Pfuderer | Eno Paenurk | Renana Gershoni‐Poranne
Source: ChemRxiv (preprint, 2022-04-27)
Field: Materials Science — Materials Chemistry

The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems.Phase 1: cata-condensed Polybenzenoid Hydrocarbons Chemical databases are an essential tool for data-driven investigation of structure-property relationships and design of novel functional compounds. We introduce the first phase of the COMPAS Project – a COMputational database of Polycyclic Aromatic Systems. In this phase, we have developed two datasets containing the optimized ground-state structures and a selec- tion of molecular properties of 34k and 9k cata- condensed polybenzenoid hydrocarbons (at the GFN2-xTB and B3LYP-D3BJ/def2-SVP lev- els, respectively), and have placed them in the public domain. Herein we describe the process of the dataset generation, detail the informa- tion available within the datasets, and show the fundamental features of the generated data. We analyze the correlation between the two types of computation as well as the structure- property relationships of the calculated species. The data and the insights gained from them can inform rational design of novel functional aro- matic molecules for use in, e.g., organic elec- tronics, and can provide a basis for additional data-driven machine- and deep-learning studies in chemistry. Property (philosophy) ; Computer science ; Computation ; Molecule ; Phase (matter) ; Domain (mathematical analysis) ; Process (computing) ; Chemistry ; Theoretical computer science ; Algorithm ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Shengjie Luo | Tianlang Chen | Yixian Xu | Shuxin Zheng | Tie‐Yan Liu | Liwei Wang | Di He
Source: arXiv (Cornell University) (preprint, 2022-10-04)
Field: Materials Science — Materials Chemistry

One Transformer Can Understand Both 2D &amp; 3D Molecular Data Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D graph or define it as a collection of atoms located in a 3D space. For molecular representation learning, most previous works designed neural networks only for a particular data format, making the learned models likely to fail for other data formats. We believe a general-purpose neural network model for chemistry should be able to handle molecular tasks across data modalities. To achieve this goal, in this work, we develop a novel Transformer-based Molecular model called Transformer-M, which can take molecular data of 2D or 3D formats as input and generate meaningful semantic representations. Using the standard Transformer as the backbone architecture, Transformer-M develops two separated channels to encode 2D and 3D structural information and incorporate them with the atom features in the network modules. When the input data is in a particular format, the corresponding channel will be activated, and the other will be disabled. By training on 2D and 3D molecular data with properly designed supervised signals, Transformer-M automatically learns to leverage knowledge from different data modalities and correctly capture the representations. We conducted extensive experiments for Transformer-M. All empirical results show that Transformer-M can simultaneously achieve strong performance on 2D and 3D tasks, suggesting its broad applicability. The code and models will be made publicly available at https://github.com/lsj2408/Transformer-M. Transformer ; Computer science ; ENCODE ; Leverage (statistics) ; Artificial intelligence ; Chemical space ; Machine learning ; Natural language processing ; Drug discovery ; Voltage ; Electrical engineering ; Bioinformatics ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ziyu Hu | Wenjie Wu | Qitean Wang | Xiaohong Shao
Source: The Journal of Physical Chemistry C (article, 2022-07-26)
Field: Materials Science — Materials Chemistry

Theoretical Study of Ba<sub>2</sub>X<sub>6</sub> (X = S, Se, Te) for Thermoelectric Applications Based on First-Principles Calculations and Machine Learning Machine learning techniques such as support vector regression (SVR) and artificial neural network (ANN) algorithms are employed to screen binary barium family (BamXn) compounds with low lattice thermal conductivity. To this aim, the electronic structures and thermoelectric (TE) properties of Ba2X6 (X = S, Se, Te) are studied utilizing the first-principles density functional theory (DFT) together with the semiclassical Boltzmann transport theory (SBTT). The results indicate that Ba2Se6 and Ba2Te6 exhibit low lattice thermal conductivity (∼0.26 and 0.31 W m–1 K–1) at room temperature. The electronic structure calculations show that three Ba2X6 (X = S, Se, Te) compounds are indirect band gap semiconductors with gap values of 1.37, 0.84, and 0.92 eV, respectively. The TE calculations show that the maximum ZT value of Ba2Te6 can be up to 3.51 at 800 K and 1.4 at 300 K. Our results indicate that all Ba2X6 (X = S, Se, Te) host good TE performances covering wide temperature ranges and have potential applications. In addition, the developed methods in this work may help to further search other materials of high TE performances. Thermoelectric effect ; Density functional theory ; Thermal conductivity ; Materials science ; Condensed matter physics ; Semiclassical physics ; Thermoelectric materials ; Electronic structure ; Band gap ; Machine learning ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Computer science ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: William Poole | Thomas E. Ouldridge | Manoj Gopalkrishnan | Erik Winfree
Source: arXiv (Cornell University) (preprint, 2022-05-12)
Field: Materials Science — Materials Chemistry

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference. Computation ; Computer science ; Inference ; Noise (video) ; Class (philosophy) ; Boltzmann constant ; Theoretical computer science ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Neural dynamics and brain function
Authors: Saman Salimi | Farzaneh Radmanesh | Nieck E. Benes | Monika Pilz | David Brown | Sylvie Neyertz
Source: Journal of Molecular Structure (article, 2022-06-12)
Field: Materials Science — Materials Chemistry

Identifying the meta, para and ortho isomers in octa(aminophenyl)silsesquioxane (OAPS) from joint experimental characterizations and theoretical predictions of the IR and NMR spectra Silsesquioxane ; Chemistry ; Siloxane ; NMR spectra database ; Context (archaeology) ; Proton NMR ; Infrared spectroscopy ; Chemical shift ; Nuclear magnetic resonance spectroscopy ; Carbon-13 NMR ; Spectral line ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Polymer Silicone and Siloxane Chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; Organoboron and organosilicon chemistry
Authors: Kiran K. Yalamanchi | Yang Li | Tairan Wang | M. Monge-Palacios | S. Mani Sarathy
Source: Applications in Energy and Combustion Science (article, 2022-09-14)
Field: Chemistry — Organic Chemistry

Large-scale thermochemistry calculations for combustion models Accurate thermochemical properties for chemical species are of vital importance in combustion chemistry research. Group additivity approaches are widely used to generate thermochemistry data used in chemical kinetic models, but this approach has limited accuracy. Following on previous studies by the combustion community, we performed electronic structure calculations to obtain reliable thermochemistry data for a large set of molecules taken from a well-established chemical kinetic model. The developed database consists of 1340 species that contain up to 18 and 5 carbon and oxygen atoms, respectively. The M06-2X/aug-cc-pVTZ level of theory was used for the geometry optimizations, vibrational frequency calculations, and dihedral angle scans. The potential energy of the different species was further refined with different composite methods, and the G3 method with the atomization reaction approach was selected to calculate the enthalpy of formation at 0 K. This information was then used in statistical thermodynamics calculations to obtain standard enthalpies of formation and entropy, as well as heat capacities at different temperatures. Our thermochemistry data exhibits good agreement with existing values in the literature, verifying the accuracy of our approach. Comparisons against group additivity (GA) method are also presented. The database of thermochemistry quantities developed in this study can be used to improve the training GA or machine learning models. The impact of the developed dataset is illustrated by examining the variation in ignition delay times using the updated thermochemistry values. Thermochemistry ; Combustion ; Standard enthalpy of formation ; Chemistry ; Thermodynamics ; Additive function ; Enthalpy ; Computational chemistry ; Statistical physics ; Physical chemistry ; Mathematics ; Physics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Advanced Chemical Physics Studies
Authors: Sai Mani Prudhvi Valleti | Reinis Ignatāns | Sergei V. Kalinin | Vasiliki Tileli
Source: Small (article, 2022-09-05)
Field: Materials Science — Materials Chemistry

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations Analysis of the temperature- and stimulus-dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature-induced phase transition in BaTiO<sub>3</sub> is explored using the machine learning analysis of domain morphologies visualized via variable-temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction-dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions. Computer science ; Dimensionality reduction ; Pattern recognition (psychology) ; Categorical variable ; Biological system ; Feature extraction ; Artificial intelligence ; Curse of dimensionality ; Phase transition ; Feature selection ; Materials science ; Physics ; Machine learning Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Na Lu | Guangshuai Han | Yixuan Sun | Yining Feng | Guang Lin
Source: Research Square (preprint, 2022-08-04)
Field: Materials Science — Materials Chemistry

Artificial intelligence assisted thermoelectric materials design and discovery Abstract Materials discovery from infinite earth repository is considered the bottleneck of each revolutionary technological progress. The discovery of the new materials is hindered by the labor-intensive and time-consuming process. Although machine learning techniques shown the excellent capability for speeding up materials discovery, it is still challenging to obtain effective material feature representations and making a precise prediction of the material properties are still challenging. This work focuses on developing an automatic material design and discovery framework enabled by data-driven AI models. We utilize the material's thermoelectric (TE) properties prediction as a baseline to demonstrate the investigation logistic. The framework works in a closed-loop mode. Starting from the well-established TE materials databases, we develop a TE material encoding method and TE material screening model by using machine learning technology. The models show excellent accuracy for TE properties prediction. Furthermore, the developed AI models have identified 6 novel promising p-type TE materials and 8 novel promising n-type TE materials. Evaluated by the Density-functional Theory (DFT) calculation, the prediction results have a good agreement with the realistic material's TE property. The proposed framework well accelerates the design and discovery of the new functional materials. Thermoelectric effect ; Materials science ; Nanotechnology ; Engineering physics ; Engineering ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials