jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Improved decision making with similarity based machine learning: applications in chemistry Abstract Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, ‘the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy. Curse of dimensionality ; Computer science ; Scarcity ; Similarity (geometry) ; Chemical space ; Machine learning ; Artificial intelligence ; Space (punctuation) ; Feature (linguistics) ; Data mining ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sékou-Oumar Kaba | Benjamin Groleau-Paré | Marc-Antoine Gauthier | A.–M. S. Tremblay | Simon Verret | C. Gauvin-Ndiaye
Source: Physical Review Materials (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Prediction of large magnetic moment materials with graph neural networks and random forests Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators, and magnetic refrigeration systems. Discovering materials with large magnetic moments is therefore an increasing priority. Here, using state-of-the-art machine learning methods, we scan the Inorganic Crystal Structure Database (ICSD) of hundreds of thousands of existing materials to find those that are ferromagnetic and have large magnetic moments. Crystal graph convolutional neural networks (CGCNNs), materials graph network (MEGNet), and random forests are trained on the Materials Project database that contains the results of high-throughput density functional theory (DFT) predictions. For random forests, we use a stochastic method to select nearly 100 relevant descriptors based on chemical composition and crystal structure. This gives results that are comparable to those of neural networks. Our findings suggests that magnetic properties are intrinsically more difficult to predict than other DFT-calculated properties. The comparison between the different machine learning approaches gives an estimate of the errors for our predictions on the ICSD database. Validating our final predictions by comparisons with available experimental data, we found 15 materials that are likely to have large magnetic moments and have not yet been studied experimentally. Materials science ; Magnetic moment ; Random forest ; Artificial neural network ; Ferromagnetism ; Magnetic monopole ; Convolutional neural network ; Artificial intelligence ; Random graph ; Graph ; Statistical physics ; Machine learning ; Computer science ; Condensed matter physics ; Theoretical computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Alexander Goscinski | Christian A. Jorgensen | Victor Paul Principe | Guillaume Fraux | Sergei Kliavinek | Benjamin A. Helfrecht | Rhushil Vasavada | Philip Loche | Michele Ceriotti | Rose K. Cersonsky
Source: Open Research Europe (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domain-specific functionalities. We present the python library scikit-matter that targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows. Workflow ; Computer science ; Usability ; Python (programming language) ; Suite ; Interoperability ; Software engineering ; Domain (mathematical analysis) ; Implementation ; Popularity ; Data science ; Database ; World Wide Web ; Programming language ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Beriham Basha | Tayyaba Mubashir | Mudassir Hussain Tahir | Jawayria Najeeb | Sumaira Naeem | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Inorganic Chemistry Communications (article, 2023-05-10)
Field: Materials Science — Polymers and Plastics

Designing of novel organic semiconductors materials for organic solar cells: A machine learning assisted proficient pipeline Organic solar cell ; HOMO/LUMO ; Pipeline (software) ; Computer science ; Photovoltaic system ; Characterization (materials science) ; Artificial intelligence ; Materials science ; Biological system ; Molecule ; Chemistry ; Nanotechnology ; Electrical engineering ; Engineering ; Organic chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Yulong Chen | Jia Feng | Xin Wang | Cheng Zhang | Dongfang Ke | Huiyan Zhu | Shuai Wang | Hongri Suo | Chongxuan Liu
Source: Environmental Science & Technology (article, 2023-07-02)
Field: Materials Science — Materials Chemistry

Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO<sub><i>x</i></sub> Selective Reduction Catalysts An iterative approach between machine learning (ML) and laboratory experiments was developed to accelerate the design and synthesis of environmental catalysts (ECs) using selective catalytic reduction (SCR) of nitrogen oxides (NO<sub><i>x</i></sub>) as an example. The main steps in the approach include training a ML model using the relevant data collected from the literature, screening candidate catalysts from the trained model, experimentally synthesizing and characterizing the candidates, updating the ML model by incorporating the new experimental results, and screening promising catalysts again with the updated model. This process is iterated with a goal to obtain an optimized catalyst. Using the iterative approach in this study, a novel SCR NO<sub><i>x</i></sub> catalyst with low cost, high activity, and a wide range of application temperatures was found and successfully synthesized after four iterations. The approach is general enough that it can be readily extended for screening and optimizing the design of other environmental catalysts and has strong implications for the discovery of other environmental materials. Catalysis ; NOx ; Iterated function ; Iterative and incremental development ; Computer science ; Reduction (mathematics) ; Process (computing) ; Selective catalytic reduction ; Process engineering ; Chemistry ; Engineering ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Naga Deepak Pagadala | Jyotika jaiswal | R. Radha
Source: Materials Today Communications (article, 2023-05-02)
Field: Materials Science — Biomaterials

Machine learning based corrosion prediction of as cast Mg-Sn alloys for biomedical applications Materials science ; Tafel equation ; Corrosion ; Alloy ; Biocompatible material ; Metallurgy ; Magnesium alloy ; Magnesium ; Machine learning ; Biomedical engineering ; Computer science Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties
Authors: Ruilin Xiao | Q. Wang | Jingyu Qin | J. F. Zhao | Ying Ruan | Haipeng Wang | Huiqi Li | B. Wei
Source: Journal of Applied Physics (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

A deep learning approach to predict thermophysical properties of metastable liquid Ti-Ni-Cr-Al alloy The physical properties of liquid alloy are crucial for many science fields. However, acquiring these properties remains challenging. By means of the deep neural network (DNN), here we presented a deep learning interatomic potential for the Ti–Ni–Cr–Al liquid system. Meanwhile, the thermophysical properties of the Ti–Ni–Cr–Al liquid alloy were experimentally measured by electrostatic levitation and electromagnetic levitation technologies. The DNN potential predicted this liquid system accurately in terms of both atomic structures and thermophysical properties, and the results were in agreement with the ab initio molecular dynamics calculation and the experimental values. A further study on local structure carried out by Voronoi polyhedron analysis showed that the cluster exhibited a tendency to transform into high-coordinated cluster with a decrease in the temperature, indicating the enhancement of local structure stability. This eventually contributed to the linear increase in the density and surface tension, and the exponential variation in the viscosity and the diffusion coefficient with the rise of undercooling. Supercooling ; Materials science ; Metastability ; Alloy ; Thermodynamics ; Cluster (spacecraft) ; Levitation ; Viscosity ; Condensed matter physics ; Metallurgy ; Chemistry ; Physics ; Composite material Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Agnese Marcato | Daniele Marchisio | Gianluca Boccardo
Source: The Canadian Journal of Chemical Engineering (article, 2023-01-18)
Field: Materials Science — Materials Chemistry

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering Abstract The use of machine learning in chemical engineering has the potential to greatly improve the design and analysis of complex systems. However, there are also risks associated with its adoption, such as the potential for bias in algorithms and the need for careful oversight to ensure the safety and reliability of machine learning‐powered systems. This paper explores the opportunities and risks of using machine learning in chemical engineering and provides a perspective on how it may be integrated into engineering practices in a responsible and effective manner. We generated the text of this abstract with GPT‐3, OpenAI's large‐scale language‐generation model. Upon generating the draft, we ensured that the language was to our liking, and we take ultimate responsibility for the content of this publication . Perspective (graphical) ; Reliability (semiconductor) ; Computer science ; Artificial intelligence ; Scale (ratio) ; Risk analysis (engineering) ; Business Machine Learning in Materials Science ; Reservoir Engineering and Simulation Methods ; Oil and Gas Production Techniques
Authors: Paul Katzberger | Sereina Riniker
Source: The Journal of Chemical Physics (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Implicit solvent approach based on generalized Born and transferable graph neural networks for molecular dynamics simulations Molecular dynamics simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the environment (solvent) has, therefore, a large impact. Implicit solvent representations are efficient but, in many cases, not accurate enough (especially for polar solvents, such as water). More accurate but also computationally more expensive is the explicit treatment of the solvent molecules. Recently, machine learning has been proposed to bridge the gap and simulate, in an implicit manner, explicit solvation effects. However, the current approaches rely on prior knowledge of the entire conformational space, limiting their application in practice. Here, we introduce a graph neural network based implicit solvent that is capable of describing explicit solvent effects for peptides with different compositions than those contained in the training set. Solvation ; Artificial neural network ; Molecular dynamics ; Computer science ; Limiting ; Solvent ; Solvent models ; Set (abstract data type) ; Chemical space ; Polar ; Graph ; Molecule ; Implicit solvation ; Space (punctuation) ; Computational chemistry ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Organic chemistry ; Physics ; Drug discovery Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Christian Venturella | Christopher Hillenbrand | Jiachen Li | Tianyu Zhu
Source: Journal of Chemical Theory and Computation (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Machine Learning Many-Body Green’s Functions for Molecular Excitation Spectra We present a machine learning (ML) framework for predicting Green's functions of molecular systems, from which photoemission spectra and quasiparticle energies at quantum many-body level can be obtained. Kernel ridge regression is adopted to predict self-energy matrix elements on compact imaginary frequency grids from static and dynamical mean-field electronic features, which gives direct access to real-frequency many-body Green's functions through analytic continuation and Dyson's equation. Feature and self-energy matrices are represented in a symmetry-adapted intrinsic atomic orbital plus projected atomic orbital basis to enforce rotational invariance. We demonstrate good transferability and high data efficiency of the proposed ML method across molecular sizes and chemical species by showing accurate predictions of density of states (DOS) and quasiparticle energies at the level of many-body perturbation theory (<i>GW</i>) or full configuration interaction. For the ML model trained on 48 out of 1995 molecules randomly sampled from the QM7 and QM9 data sets, we report the mean absolute errors of ML-predicted highest occupied and lowest unoccupied molecular orbital energies to be 0.13 and 0.10 eV, respectively, compared to <i>GW</i>@PBE0. We further showcase the capability of this method by applying the same ML model to predict DOS for significantly larger organic molecules with up to 44 heavy atoms. Quasiparticle ; Physics ; Perturbation theory (quantum mechanics) ; Excitation ; Quantum ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Alireza Geramizadegan | Leila Niknam | Elham Pournamdari
Source: Journal of Analytical Chemistry (article, 2023-05-01)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers for Selective Extraction and Determination of Toxic Herbicide Bentazon in Water Samples Using Liquid Chromatography and Assessment of Mean Square Error Using Artficial Neural Network Model In this article, a molecularly imprinted polymer-solid phase extraction (SPE)-liquid chromatography method was developed to isolate toxic bentazon in surface water. The molecularly imprinted polymer (MIP) consisted of methacrylic acid as a functional monomer, ethylene glycol dimethacrylate as a crosslinking monomer, and α,α′-azoisobutyronitrile as an initiator for polymer preparation. To evaluate the applicability of the imprinted polymer as a selective sorbent, general parameters, such as pH, amount of loading solvents, washing solution, eluent, and time, were optimized following a step-by-step approach. Under the optimum conditions, the linear range was between 0.05 and 1.0 µg/L. The standard deviation of 2.2% and the method detection limit of 0.05 µg/L were obtained. The recoveries up to approximately 97.0% from spiked surface water samples could be obtained. The observed outcomes confirmed the suitability of the artificial neural network model as a tool for mean square error of bentazon on MIP-SPE (0.018) and non-imprinted polymer-SPE (0.029) selectivity and permeability. The proposed molecularly imprinted polymer-solid phase extraction-liquid chromatography method could be applied to the direct determination of toxic bentazon in water samples. Molecularly imprinted polymer ; Solid phase extraction ; Chemistry ; Chromatography ; Ethylene glycol dimethacrylate ; Methacrylic acid ; Sorbent ; Detection limit ; Extraction (chemistry) ; Polymer ; Bentazon ; High-performance liquid chromatography ; Monomer ; Adsorption ; Selectivity ; Organic chemistry Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Hanbo Zheng | Weijie Lv | Yang Wang | Yongji Feng | Hang Yang
Source: Journal of Molecular Liquids (article, 2023-06-17)
Field: Chemistry — Analytical Chemistry

Molecular kinematic viscosity prediction of natural ester insulating oil based on sparse Machine learning models Molecular descriptor ; Viscosity ; Kinematics ; Biological system ; Quantitative structure–activity relationship ; Molecule ; Chemical space ; Chemistry ; Artificial intelligence ; Computer science ; Computational chemistry ; Materials science ; Machine learning ; Thermodynamics ; Organic chemistry ; Physics ; Drug discovery Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Power Transformer Diagnostics and Insulation
Authors: Xiangguo Li | Shuozhi Xu | Qian Zhang | Shenghua Liu | Jing Shuai
Source: Journal of Alloys and Compounds (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Complex strengthening mechanisms in nanocrystalline Ni-Mo alloys revealed by a machine-learning interatomic potential Nanocrystalline material ; Materials science ; Grain size ; Grain boundary strengthening ; Grain boundary ; Annealing (glass) ; Strengthening mechanisms of materials ; Nanocrystal ; Metallurgy ; Condensed matter physics ; Microstructure ; Nanotechnology ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Ion-surface interactions and analysis
Authors: Khashayar Ghorbani | Pedram Mirchi | Saeed Arabha | Ali Rajabpour | Sebastian Volz
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity and Young's modulus of XN<sub>4</sub> (X = Be, Mg and Pt) 2D materials using machine learning interatomic potentials The newly synthesized BeN<sub>4</sub> monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN<sub>4</sub>, MgN<sub>4</sub>, and PtN<sub>4</sub>, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed and utilized in classical molecular dynamics (MD) simulations. Mechanical properties are calculated by extracting the stress-strain curve and thermal properties by the non-equilibrium molecular dynamics (NEMD) method. The acquired results show the anisotropic Young's modulus and lattice thermal conductivity of these materials. Generally, the Young's modulus and thermal conductivity in the armchair direction are higher than in the zigzag direction. Also, the anisotropy of Young's modulus is almost constant at every temperature for BeN<sub>4</sub> and MgN<sub>4</sub>, while for PtN<sub>4</sub>, this parameter is decreased by increasing the temperature. The findings of this research are not only evidence of the application of machine learning in MD simulations, but also provide information on the basic anisotropic mechanical and thermal properties of these newly discovered 2D nanomaterials. Lattice (music) ; Interatomic potential ; Thermal conductivity ; Modulus ; Materials science ; Condensed matter physics ; Thermal ; Young's modulus ; Thermodynamics ; Chemistry ; Composite material ; Physics ; Molecular dynamics ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Suresh Bishnoi | Skyler Badge | Jayadeva | N. M. Anoop Krishnan
Source: Journal of Non-Crystalline Solids (article, 2023-07-04)
Field: Materials Science — Materials Chemistry

Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors Universality (dynamical systems) ; Computer science ; Property (philosophy) ; Artificial neural network ; Artificial intelligence ; Training set ; Machine learning ; Set (abstract data type) ; Biological system ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Pigment Synthesis and Properties
Authors: Dewu Lin | Shun Muroga | Hiroe Kimura | Hirokuni Jintoku | Takashi Tsuji | Kenji Hata | Guohai Chen | Don N. Futaba
Source: ACS Nano (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT) forests, as growing certain desired properties can often come at the expense of other desirable characteristics such as the case of crystallinity and growth efficiency. Simultaneously achieving mutually exclusive properties in the growth of SWCNT forests is a significant accomplishment, as it requires overcoming these trade-offs and balancing competing mechanisms. To address this, we trained a machine-learning regression model with a set of 585 "real" experimental synthesis data, which were taken using an automatic synthesis reactor. Subsequently, 16000 exploratory "virtual" experiments were performed by our trained model to examine potential routes toward addressing the current crystallinity-height trade-off limitation, and suggestions on growth conditions were predicted. Importantly, additional validation using "real" experimental syntheses showed good agreement with the predictions as well as a 48% increase in growth efficiency while maintaining the high crystallinity (G/D-ratio). This highlighted the effectiveness and accuracy of the predictive capability of our machine-learning model, which achieved improved results in less than 50 validation tests. Furthermore, the trained model revealed the surprising importance of the nature of the carbon feedstock, particularly the reactivity and concentration, as a route for overcoming the trade-off between the SWCNT crystallinity and growth efficiency. These results of the high-efficiency synthesis of highly crystalline SWCNT forests represent a significant advance in overcoming synthetic trade-off barriers for complex multivariable systems. Crystallinity ; Materials science ; Carbon nanotube ; Machine learning ; Artificial intelligence ; Nanotechnology ; Yield (engineering) ; Computer science ; Biochemical engineering ; Composite material ; Engineering Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications