jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md. Johurul Islam | M. Jhahan | Marina Khatun | M. N. I. Khan | Mohammad Jellur Rahman | Md. Fakhrul Islam | Abdullah Al-Momin | Md. Monjarul Alam
Source: Journal of Materials Science Materials in Electronics (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

Influence of Mg substitution on structural, magnetic and electrical properties of Zn-Cu ferrites Substitution (logic) ; Materials science ; Metallurgy ; Computer science Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Magneto-Optical Properties and Applications
Authors: Pushkar Ghanekar | Siddharth Deshpande | Jeffrey Greeley
Source: ChemRxiv (preprint, 2021-08-23)
Field: Materials Science — Materials Chemistry

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions. Catalysis ; Heterogeneous catalysis ; Chemical physics ; Adsorption ; Chemistry ; Substrate (aquarium) ; Materials science ; Nanotechnology ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Esther Heid | William H. Green
Source: ChemRxiv (preprint, 2021-08-11)
Field: Materials Science — Materials Chemistry

Machine learning of reaction properties via learned representations of the condensed graph of reaction The estimation of chemical reaction properties such as activation energies, rates or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction, and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark datasets for future work. All datasets and the developed reaction GCNN model are available online, free of charge and open-source. Cheminformatics ; Computer science ; Benchmark (surveying) ; Convolutional neural network ; Artificial intelligence ; Graph ; Embedding ; Machine learning ; Molecular graph ; Theoretical computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Runzhao Li | J.M. Herreros | A. Tsolakis | Wenzhao Yang
Source: Journal of Molecular Graphics and Modelling (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

Machine learning and deep learning enabled fuel sooting tendency prediction from molecular structure Artificial intelligence ; Computer science ; Machine learning ; Deep learning ; Convolutional neural network ; Artificial neural network ; Feature extraction ; Regression ; Feature (linguistics) ; Quantitative structure–activity relationship ; Modular design ; Algorithm ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Petroleum Processing and Analysis
Authors: Yingfang Yuan | Wenjun Wang | Wei Pang
Source: Proceedings of the Genetic and Evolutionary Computation Conference (article, 2021-06-21)
Field: Materials Science — Materials Chemistry

A systematic comparison study on hyperparameter optimisation of graph neural networks for molecular property prediction Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied to predict molecular properties. However, a direct impediment is to select appropriate hyperparameters to achieve satisfactory performance with lower computational cost. Meanwhile, many molecular datasets are far smaller than many other datasets in typical deep learning applications. Most hyperparameter optimization (HPO) methods have not been explored in terms of their efficiencies on such small datasets in the molecular domain. In this paper, we conducted a theoretical analysis of common and specific features for two state-of-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline. Experimental studies are carried out on several benchmarks in MoleculeNet, from different perspectives to investigate the impact of RS, TPE, and CMA-ES on HPO of GNNs for molecular property prediction. In our experiments, we concluded that RS, TPE, and CMA-ES have their individual advantages in tackling different specific molecular problems. Finally, we believe our work will motivate further research on GNN as applied to molecular machine learning problems in chemistry and materials sciences. Hyperparameter ; Property (philosophy) ; Artificial neural network ; Range (aeronautics) ; Graph ; Molecular graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Machine Learning and Data Classification
Authors: Max Pinheiro | Fuchun Ge | Nicolas Ferré | Pavlo O. Dral | Mario Barbatti
Source:  (preprint, 2021-08-16)
Field: Materials Science — Materials Chemistry

Choosing the right molecular machine learning potential Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one. Computer science ; Artificial intelligence ; Machine learning ; Observable ; Potential energy ; Energy cost ; Energy (signal processing) ; Data science ; Environmental economics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Carl E. Belle | Vural Aksakallı | Salvy P. Russo
Source: Journal of Cheminformatics (article, 2021-05-27)
Field: Materials Science — Materials Chemistry

A machine learning platform for the discovery of materials For photovoltaic materials, properties such as band gap [Formula: see text] are critical indicators of the material's suitability to perform a desired function. Calculating [Formula: see text] is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as [Formula: see text] of a wide range of materials. Algorithm ; Computer science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Zun Wang | Chong Wang | Sibo Zhao | Shiqiao Du | Yong Xu | Bing-Lin Gu | Wenhui Duan
Source: Science China Physics Mechanics and Astronomy (article, 2021-09-28)
Field: Materials Science — Materials Chemistry

Symmetry-adapted graph neural networks for constructing molecular dynamics force fields Molecular dynamics ; Computer science ; Force field (fiction) ; Statistical physics ; Artificial neural network ; Translation (biology) ; Theoretical computer science ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Fuel Cells and Related Materials
Authors: Vanga Sreekar Goud | M.R. Rahul | Gandham Phanikumar
Source: Scripta Materialia (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Prediction of growth velocity of undercooled multicomponent metallic alloys using a machine learning approach Supercooling ; Materials science ; Artificial neural network ; Gradient boosting ; Random forest ; Experimental data ; Machine learning ; Artificial intelligence ; Microstructure ; Entropy (arrow of time) ; Computer science ; Metallurgy ; Thermodynamics ; Mathematics ; Statistics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; High Entropy Alloys Studies
Authors: Shuhui Guan | Cheng Shang | Zhi‐Pan Liu
Source: Chinese Journal of Chemistry (article, 2021-06-16)
Field: Materials Science — Materials Chemistry

Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review<sup>†</sup> Abstract Energy materials featuring the capability to store and release chemical energy reversibly involve generally complex geometrical structures with multiple elements. It has been a great challenge to establish the quantitative relationship between the structure of materials and their dynamic physicochemical properties. In recent years, machine learning (ML) technique has demonstrated its great power in accelerating the research on energy materials. This topical review introduces the key ingredients and typical applications of ML to energy materials. We mainly focus on the ML based atomic simulation via ML potentials in different architectures/implementations, including high dimensional neural networks (HDNN), Gaussian approximation potential (GAP), moment tensor potentials (MTP) and stochastic surface walking global optimization with global neural network potential (SSW‐NN) method. Three cases studies, namely, Si, LiC and LiTiO systems, are presented to demonstrate the ability of ML simulation in assessing the thermodynamics and kinetics of complex material systems. We highlight that the SSW‐NN method provides an automated solution for global potential energy surface data collection, ML potential construction and ML simulation, which boosts the current ability for large‐scale atomic simulation and thus holds the great promise for fast property evaluation and material discovery. Artificial neural network ; Moment (physics) ; Molecular dynamics ; Chemistry ; Gaussian ; Statistical physics ; Energy (signal processing) ; Implementation ; Computer science ; Nanotechnology ; Artificial intelligence ; Computational chemistry ; Physics ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Elizabeth J. Kautz
Source: Patterns (article, 2021-06-18)
Field: Materials Science — Materials Chemistry

Predicting material microstructure evolution via data-driven machine learning Predicting microstructure evolution can be a formidable challenge, yet it is essential to building microstructure-processing-property relationships. Yang et al. offer a new solution to traditional partial differential equation-based simulations: a data-driven machine learning approach motivated by the practical needs to accelerate the materials design process and deal with incomplete information in the real world of microstructure simulation. Microstructure ; Computer science ; Materials science ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Elizaveta Kozyr | Aram L. Bugaev | С. А. Гуда | Alexander A. Guda | Kirill A. Lomachenko | K. Janssens | Simon Smolders | Dirk De Vos | А. В. Солдатов
Source: The Journal of Physical Chemistry C (article, 2021-12-12)
Field: Materials Science — Materials Chemistry

Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning X-ray absorption spectroscopy is a powerful tool for the characterization of local atomic structure. Commonly, bond lengths and coordination numbers are extracted from the extended energy region of the spectrum (extended X-ray absorption fine structure, EXAFS). However, for many diluted systems, such as homogeneous catalysts, with a low concentration of the active component and under in situ or operando conditions, one cannot collect sufficient EXAFS data for a quantitative analysis. Considering the case of a homogeneous ruthenium-based catalyst, where the ligand surrounding the ruthenium atoms can change from Br to CO depending on the reaction conditions, we establish here an effective machine learning approach based on the descriptor analysis of spectral features. After the training procedure, the algorithm predicts both the ligand surrounding ruthenium and the distances to Br and CO ligands. The prediction quality of the approach was verified by means of a cross-validation procedure applied to the mixture of compounds and was validated for experimental spectra of reference RuBr3 and [RuBr2(CO)3]2 complexes. This work describes a practical route to improve classical fingerprint analysis and linear combination fit by more sophisticated data science algorithms. Extended X-ray absorption fine structure ; Ruthenium ; XANES ; Homogeneous ; Catalysis ; Coordination number ; Fingerprint (computing) ; Absorption spectroscopy ; Absorption (acoustics) ; Ligand (biochemistry) ; Spectroscopy ; Chemistry ; Spectral line ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Physics ; Optics ; Thermodynamics ; Ion ; Chromatography ; Organic chemistry Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Analytical chemistry methods development
Authors: Ryan Henderson | Djork-Arné Clevert | Floriane Montanari
Source: arXiv (Cornell University) (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Improving Molecular Graph Neural Network Explainability with\n Orthonormalization and Induced Sparsity Rationalizing which parts of a molecule drive the predictions of a molecular\ngraph convolutional neural network (GCNN) can be difficult. To help, we propose\ntwo simple regularization techniques to apply during the training of GCNNs:\nBatch Representation Orthonormalization (BRO) and Gini regularization. BRO,\ninspired by molecular orbital theory, encourages graph convolution operations\nto generate orthonormal node embeddings. Gini regularization is applied to the\nweights of the output layer and constrains the number of dimensions the model\ncan use to make predictions. We show that Gini and BRO regularization can\nimprove the accuracy of state-of-the-art GCNN attribution methods on artificial\nbenchmark datasets. In a real-world setting, we demonstrate that medicinal\nchemists significantly prefer explanations extracted from regularized models.\nWhile we only study these regularizers in the context of GCNNs, both can be\napplied to other types of neural networks\n Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Thiruselvi Devaraj | Salma Aathika | Yuvarani Mani | D. Jagadiswary | S. Jebarani Evangeline | Anuradha Dhanasekaran | Shanmugam Palaniyandi | S. Sivanesan
Source: Journal of Hazardous Materials (article, 2021-03-24)
Field: Chemistry — Organic Chemistry

Application of Artificial Neural Network as a nonhazardous alternative on kinetic analysis and modeling for green synthesis of cobalt nanocatalyst from Ocimum tenuiflorum Cobalt ; Artificial neural network ; Raw material ; Materials science ; Chemistry ; Computer science ; Metallurgy ; Organic chemistry ; Machine learning Nanomaterials for catalytic reactions
Authors: Zheng Yu | Qitong Liu | Izabela Szlufarska | Bu Wang
Source: Physical Review Materials (article, 2021-01-11)
Field: Materials Science — Ceramics and Composites

Structural signatures for thermodynamic stability in vitreous silica: Insight from machine learning and molecular dynamics simulations The structure-thermodynamic stability relationship in vitreous silica is investigated using machine learning and a library of 24 157 inherent structures generated from melt-quenching and replica exchange molecular dynamics simulations. We find that the thermodynamic stability, i.e., enthalpy of the inherent structure $({e}_{\mathrm{IS}})$, can be accurately predicted by both linear and nonlinear machine learning models from numeric structural descriptors commonly used to characterize disordered structures. We find that short-range features become less indicative of thermodynamic stability below the fragile-to-strong transition. On the other hand, medium-range features, especially those between 2.8 and $\ensuremath{\sim}6$ \AA{}, show consistent correlations with ${e}_{\mathrm{IS}}$ across the liquid and glass regions, and are found to be the most critical to the stability prediction in this study among features from different length scales. Based on the machine learning models, a set of five structural features that are the most predictive of the silica glass stability is identified. Stability (learning theory) ; Materials science ; Molecular dynamics ; Chemical stability ; Enthalpy ; Thermodynamics ; Silica glass ; Replica ; Structural stability ; Range (aeronautics) ; Statistical physics ; Glass transition ; Chemical physics ; Machine learning ; Computer science ; Computational chemistry ; Physics ; Polymer ; Chemistry Glass properties and applications ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Rémi Pétuya | Samantha Durdy | Dmytro Antypov | Michael W. Gaultois | Neil G. Berry | George R. Darling | Alexandros P. Katsoulidis | Matthew S. Dyer | Matthew J. Rosseinsky
Source: Angewandte Chemie (article, 2021-12-08)
Field: Chemistry — Inorganic Chemistry

Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry Abstract The choice of metal and linker together define the structure and therefore the guest accessibility of a metal‐organic framework (MOF), but the large number of possible metal‐linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5 % certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three‐dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components’ chemistry and the MOF porosity. Pore dimensions of the guest‐accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations. Linker ; Metal-organic framework ; Prioritization ; Computer science ; Chemistry ; Metal ; Artificial intelligence ; Combinatorial chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Abhishek Sengupta | G. Naresh | Astha Mishra | Diksha Parashar | Priyanka Narad
Source: Advances in protein chemistry and structural biology (review, 2021-01-01)
Field: Chemistry — Spectroscopy

Proteome analysis using machine learning approaches and its applications to diseases Artificial intelligence ; Computer science ; Proteomics ; Machine learning ; Deep learning ; Field (mathematics) ; Informatics ; Identification (biology) ; Data science ; Big data ; Data mining ; Engineering ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics