jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carolina von Eßen | David Luedeker
Source: Drug Discovery Today (review, 2023-09-07)
Field: Materials Science — Materials Chemistry

In silico co-crystal design: Assessment of the latest advances Context (archaeology) ; Relevance (law) ; Active ingredient ; Drug discovery ; Pharmaceutical drug ; Computer science ; Pharmaceutical sciences ; Pharmaceutical technology ; Biochemical engineering ; Artificial intelligence ; Nanotechnology ; Data science ; Machine learning ; Drug ; Chemistry ; Medicine ; Materials science ; Pharmacology ; Engineering ; Biology Crystallization and Solubility Studies ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Timo Sommer | Roland Willa | Jörg Schmalian | Pascal Friederich
Source: Scientific Data (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

3DSC - a dataset of superconductors including crystal structures Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature T<sub>C</sub> of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by approximate three-dimensional crystal structures. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature T<sub>C</sub> of materials. Furthermore, we provide ideas and directions for further research to improve the 3DSC. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors. Superconductivity ; Computer science ; Machine learning ; Data mining ; Artificial intelligence ; Information retrieval ; Database ; Data science ; Physics ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Changliang Zhu | Xiangying 翔瀛 Shen 沈 | Guimei 桂妹 Zhu 朱 | Baowen 保文 Li 李
Source: Chinese Physics Letters (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Prediction of Thermal Conductance of Complex Networks with Deep Learning Predicting thermal conductance of complex networks poses a formidable challenge in the field of materials science and engineering. This challenge arises due to the intricate interplay between the parameters of network structure and thermal conductance, encompassing connectivity, network topology, network geometry, node inhomogeneity, and others. Our understanding of how these parameters specifically influence heat transfer performance remains limited. Deep learning offers a promising approach for addressing such complex problems. We find that the well-established convolutional neural network models AlexNet can predict the thermal conductance of complex network efficiently. Our approach further optimizes the calculation efficiency by reducing the image recognition in consideration that the thermal transfer is inherently encoded within the Laplacian matrix. Intriguingly, our findings reveal that adopting a simpler convolutional neural network architecture can achieve a comparable prediction accuracy while requiring less computational time. This result facilitates a more efficient solution for predicting the thermal conductance of complex networks and serves as a reference for machine learning algorithm in related domains. Computer science ; Convolutional neural network ; Thermal conductivity ; Artificial intelligence ; Deep learning ; Node (physics) ; Transfer of learning ; Field (mathematics) ; Complex network ; Conductance ; Artificial neural network ; Thermal ; Topology (electrical circuits) ; Machine learning ; Materials science ; Physics ; Mathematics ; Thermodynamics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Yuxinxin Chen | Yanchi Ou | Peikun Zheng | Yaohuang Huang | Fuchun Ge | Pavlo O. Dral
Source: The Journal of Chemical Physics (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method. Benchmark (surveying) ; Baseline (sea) ; Pericyclic reaction ; Quantum ; Computer science ; Range (aeronautics) ; Rotation (mathematics) ; Point (geometry) ; Artificial intelligence ; Mathematics ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Kaveh Safavigerdini | Koundinya Nouduri | Ramakrishna Surya | Andrew Reinhard | Zach Quinlan | Filiz Bunyak | Matthew R. Maschmann | Kannappan Palaniappan
Source:  (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

Predicting Mechanical Properties of Carbon Nanotube (CNT) Images Using Multi-Layer Synthetic Finite Element Model Simulations We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images which are generated by blending 2D synthetic images. The MLS images more closely resemble 3D synthetic and real scanning electron microscopy (SEM) images of CNTs but without the computational cost of performing expensive 3D simulations or experiments. Mechanical properties such as stiffness and buckling load for the MLS images are estimated using a physics-based model. The proposed deep learning architecture, CNTNeXt, builds upon our previous CNTNet neural network, using a ResNeXt feature representation followed by random forest regression estimator. Our machine learning approach for predicting CNT physical properties by utilizing a blended set of synthetic images is expected to outperform single synthetic image-based learning when it comes to predicting mechanical properties of real scanning electron microscopy images. This has the potential to accelerate understanding and control of CNT forest self-assembly for diverse applications. Carbon nanotube ; Artificial intelligence ; Artificial neural network ; Computer science ; Finite element method ; Materials science ; Pipeline (software) ; Deep learning ; Feature (linguistics) ; Random forest ; Pattern recognition (psychology) ; Nanotechnology ; Structural engineering ; Engineering Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Electron Microscopy Techniques and Applications
Authors: Fengru Zheng | Zhiwen Zhu | Jiayi Lu | Yuyi Yan | Hao Jiang | Qiang Sun
Source: Chemical Physics Letters (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Predicting the HOMO-LUMO gap of benzenoid polycyclic hydrocarbons via interpretable machine learning HOMO/LUMO ; Computer science ; Benzene ; Toolbox ; Property (philosophy) ; Chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Prahlad K. Routh | Nicholas Marcella | Anatoly I. Frenkel
Source: The Journal of Physical Chemistry C (article, 2023-03-20)
Field: Materials Science — Materials Chemistry

Speciation of Nanocatalysts Using X-ray Absorption Spectroscopy Assisted by Machine Learning The structure and morphology of supported nanoparticle catalysts play important roles in many industrial reactions. Recent progress has identified key aspects of structure–activity relationships at the nanoscale and novel methods to study the local environment of the active sites. X-ray absorption fine structure (XAFS) spectroscopy, despite being a leading technique for this purpose, is hampered significantly by its ensemble-averaging nature which often leads to a bias toward a single “representative” structure. Learning heterogeneous distributions of nanostructures at the inter- and intraparticle levels from the average XAFS spectrum is a formidable challenge that can be overcome in some cases described in this Perspective. We also discuss emerging machine learning techniques for extracting the information about the heterogeneity of metal species from XAFS data. X-ray absorption fine structure ; Nanomaterial-based catalyst ; Nanostructure ; Spectroscopy ; Nanoparticle ; X-ray absorption spectroscopy ; Absorption (acoustics) ; Nanoscopic scale ; Absorption spectroscopy ; Materials science ; Nanotechnology ; Chemical physics ; Computer science ; Biological system ; Chemistry ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Jun Li | Kun Luo | Qi An
Source: International Journal of Plasticity (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

Atomic structure, stability, and dissociation of dislocations in cadmium telluride • Dislocation core reconstructions are not favorable in CdTe , a typical II-VI semiconductor, due to the predominant ionic bonding characteristics. • The screw dislocation in cdte prefers to be in the shuffle set and starts to glide at relatively low temperatures due to the low peierls stress . • The 60° perfect dislocation in cdte is more stable in the shuffle set. As it moves to the glide set, the 60° perfect dislocation tends to dissociate into a pair of 30° and 90° partial dislocations connected by a stacking fault. • The undissociated screw and 60° shuffle dislocations, as well as dissociated 30° and 90° glide partial dislocations play an essential role in the mechanical deformation of cdte at ambient temperature. Dislocation plays a crucial role in many material properties of semiconductors, ranging from plastic deformation to electronic transport. But the dislocation structures and reactions remain controversial in many semiconductors. Here, we systematically examine the dislocation properties of cadmium telluride (CdTe), a prototype of II-VI compound semiconductors, using molecular statics and molecular dynamics simulations with a machine-learning force field. We find that dislocation cores in CdTe are not reconstructed along the dislocation line due to the significant ionic bonding characteristics. Moreover, the undissociated screw dislocation in the shuffle set is more stable than that in the glide set, and the glide dislocation tends to move to the shuffle set, followed by random movement rather than dissociation, arising from its low Peierls stress. For 60° perfect dislocation, it also prefers to locate in the shuffle set, but the 60° glide dislocation is dissociated into a pair of 30° and 90° partial dislocations connected by a stacking fault with a width of ∼9.03 nm at low temperatures through reconfiguring atomic bonds in the core. This work provides an atomic-level understanding of dislocation core properties in CdTe, laying a basis for interpreting the mechanical and electronic performance of CdTe and other II-VI semiconductors. Cadmium telluride photovoltaics ; Materials science ; Dislocation ; Semiconductor ; Partial dislocations ; Peierls stress ; Dissociation (chemistry) ; Condensed matter physics ; Crystallography ; Ionic bonding ; Stacking fault ; Crystal twinning ; Chemical physics ; Dislocation creep ; Nanotechnology ; Optoelectronics ; Chemistry ; Physical chemistry ; Physics ; Composite material ; Ion ; Microstructure Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Badril Azhar | Cries Avian | Ardila Hayu Tiwikrama
Source: Journal of Molecular Liquids (article, 2023-08-16)
Field: Chemistry — Inorganic Chemistry

Green synthesis optimization with artificial intelligence studies of copper–gallic acid metal–organic framework and its application in dye removal from wastewater Adsorption ; Crystallinity ; Yield (engineering) ; Materials science ; Fourier transform infrared spectroscopy ; Chemical engineering ; Copper ; Nuclear chemistry ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Anna Bachs-Herrera | Daniel York | Tristan Stephens-Jones | Ian Mabbett | Jingjie Yeo | Francisco J. Martín‐Martínez
Source: iScience (review, 2023-03-31)
Field: Materials Science — Biomaterials

Biomass carbon mining to develop nature-inspired materials for a circular economy A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature's efficiency and wasteless procedures are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials' mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material's development and process engineering. Biomass (ecology) ; Circular economy ; Process (computing) ; Carbon fibers ; Low-carbon economy ; Biochemical engineering ; Computer science ; Environmental science ; Engineering ; Ecology ; Climate change biodegradable polymer synthesis and properties ; Catalysis for Biomass Conversion ; Thermochemical Biomass Conversion Processes
Authors: P.J. Garcı́a Nieto | Esperanza García–Gonzalo | Luis Alfonso Menéndez García | Laura Álvarez de Prado | Antonio Bernardo-Sánchez
Source: Alexandria Engineering Journal (article, 2023-11-28)
Field: Materials Science — Materials Chemistry

Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression Using a random forest regression (RFR) machine learning technique, the critical temperature (Tc) of a superconductor was predicted in the context of Industry 4.0 in this study using features derived from the material's physico-chemical properties, containing atomic mass, electron affinity, atomic radius, valence, and thermal conductivity. The same experimental data were also fitted with multilayer perceptron (MLP) artificial neural networks (ANN), M5 model tree and multivariate linear regression (MLR) model for comparison. The current investigation's findings show that the proposed RFR–relied model can successfully forecast the critical temperature of a superconductor. Additionally, the Tc estimate was reached with a correlation coefficient of 0.9565 and a coefficient of determination 0.9146, when the observed dataset was used to test this unique technique. Additionally, the outcomes from the MLP, M5, and MLR models are obviously worse than those from the RFR–relied model. When it comes to fully comprehending the superconductivity, this investigation is noteworthy. Regarding forecasting effectiveness and feature reduction rate, the RFR approach has obvious advantages and generalizability, and it also demonstrates suitability for high-temperature superconductor Tc forecasting. In fact, it offers a practical and affordable approach to data-driven superconductor investigation. Artificial neural network ; Multivariate statistics ; Random forest ; Linear regression ; Artificial intelligence ; Bayesian multivariate linear regression ; Multilayer perceptron ; Machine learning ; Superconductivity ; Context (archaeology) ; Computer science ; Statistics ; Data mining ; Mathematics ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; X-ray Diffraction in Crystallography
Authors: Kenji Nawa | Tsuyoshi Suzuki | Keisuke Masuda | Shu Tanaka | Yoshio Miura
Source: Physical Review Applied (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Quantum Annealing Optimization Method for the Design of Barrier Materials in Magnetic Tunnel Junctions Materials informatics has boosted materials design, but the search for optimal atomic configurations in spintronic devices is challenging, due to many degrees of freedom and the need to design at the atomic level. Quantum annealing offers a breakthrough for such challenges in huge search spaces. The authors propose a combination of quantum annealing, machine learning, and first-principles calculations that is computationally cheaper than ordinary machine learning in designing atomically disordered spinel oxides (promising materials for magnetoresistive devices). Furthermore, the origins of physical properties of interest can be interpreted from the obtained Ising model Hamiltonian. Quantum annealing ; Spintronics ; Annealing (glass) ; Quantum tunnelling ; Quantum ; Simulated annealing ; Materials science ; Hamiltonian (control theory) ; Computer science ; Spinel ; Nanotechnology ; Condensed matter physics ; Quantum computer ; Physics ; Quantum mechanics ; Optoelectronics ; Ferromagnetism ; Mathematical optimization ; Algorithm ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Danish Khan | Stefan Heinen | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations The feature vector mapping used to represent chemical systems is a key factor governing the superior data efficiency of kernel based quantum machine learning (QML) models applicable throughout chemical compound space. Unfortunately, the most accurate representations require a high dimensional feature mapping, thereby imposing a considerable computational burden on model training and use. We introduce compact yet accurate, linear scaling QML representations based on atomic Gaussian many-body distribution functionals (MBDF) and their derivatives. Weighted density functions of MBDF values are used as global representations that are constant in size, i.e., invariant with respect to the number of atoms. We report predictive performance and training data efficiency that is competitive with state-of-the-art for two diverse datasets of organic molecules, QM9 and QMugs. Generalization capability has been investigated for atomization energies, highest occupied molecular orbital-lowest unoccupied molecular orbital eigenvalues and gap, internal energies at 0 K, zero point vibrational energies, dipole moment norm, static isotropic polarizability, and heat capacity as encoded in QM9. MBDF based QM9 performance lowers the optimal Pareto front spanned between sampling and training cost to compute node minutes, effectively sampling chemical compound space with chemical accuracy at a sampling rate of ∼48 molecules per core second. Kernel (algebra) ; Computer science ; Quantum ; Distribution (mathematics) ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Statistical physics ; Physics ; Discrete mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Jon Paul Janet | Lewis Mervin | Ola Engkvist
Source: Current Opinion in Structural Biology (review, 2023-03-24)
Field: Materials Science — Materials Chemistry

Artificial intelligence in molecular de novo design: Integration with experiment Computer science ; Artificial intelligence ; Generative grammar ; Automation ; Field (mathematics) ; Cover (algebra) ; Machine learning ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Andrew Ma | Yang Zhang | Thomas Christensen | Hoi Chun Po | Jing Li | Liang Fu | Marin Soljačić
Source: Nano Letters (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wave function. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term <i>topogivity</i>, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation. Heuristic ; Topology (electrical circuits) ; Computer science ; Symmetry (geometry) ; Simple (philosophy) ; Topological data analysis ; Artificial intelligence ; Algorithm ; Mathematics ; Geometry Machine Learning in Materials Science ; Topological Materials and Phenomena ; Advanced Condensed Matter Physics