jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Helge S. Stein
Source: Trends in Chemistry (article, 2022-05-31)
Field: Materials Science — Materials Chemistry

Advancing data-driven chemistry by beating benchmarks Constructive ; Computer science ; Property (philosophy) ; Chemistry ; Data science ; Nanotechnology ; Materials science ; Programming language ; Process (computing) ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lisa Je | George W. Huber | Reid C. Van Lehn | Ví­ctor M. Zavala
Source: Current Opinion in Chemical Engineering (article, 2022-02-02)
Field: Materials Science — Materials Chemistry

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis Leverage (statistics) ; Molecular dynamics ; Solvent ; Catalysis ; Computer science ; Workflow ; Biochemical engineering ; Chemistry ; Biological system ; Nanotechnology ; Materials science ; Computational chemistry ; Machine learning ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Eric Fangnon | Evgenii Malitckii | Renata Latypova | Pedro Vilaça
Source: International Journal of Hydrogen Energy (article, 2022-11-30)
Field: Materials Science — Metals and Alloys

Prediction of hydrogen concentration responsible for hydrogen-induced mechanical failure in martensitic high-strength steels Hydrogen, at critical concentrations, responsible for hydrogen-induced mechanical property degradation cannot yet be estimated beforehand and can only be measured experimentally upon fracture with specific specimen sizes. In this work, we develop two deep learning artificial neural network (ANN) models with the ability to predict hydrogen concentration responsible for early mechanical failure in martensitic ultra-high-strength steels. This family of steels is represented by four different steels encompassing different chemical compositions and heat treatments. The mechanical properties of these steels with varying size and morphology of prior austenitic grains in as-supplied state and after hydrogen-induced failure together with their corresponding hydrogen charging conditions were used as inputs. The feed forward back propagation models with network topologies of 12-7-5-3-2-1 (I) and 14-7-5-3-2-1 (II) were validated and tested with unfamiliar data inputs. The models I and II show good hydrogen concentration prediction capabilities with mean absolute errors of 0.28, and 0.33 wt.ppm at test datasets, respectively. A linear correlation of 80% and 77%, between the experimentally measured and ANN predicted hydrogen concentrations, was obtained for Model I and II respectively. This shows that for this family of steels, the estimation of hydrogen concentration versus property degradation is a feasible approach for material safety analysis. Hydrogen ; Materials science ; Martensite ; Austenite ; Work (physics) ; Degradation (telecommunications) ; Artificial neural network ; Metallurgy ; Thermodynamics ; Microstructure ; Computer science ; Chemistry ; Machine learning ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Hiroyasu Katsuno | Yuki Kimura | Tomoya Yamazaki | Ichigaku Takigawa
Source: Frontiers in Chemistry (article, 2022-01-24)
Field: Materials Science — Surfaces, Coatings and Films

Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning To support the detection, recording, and analysis of nucleation events during <i>in situ</i> observations, we developed an early detection system for nucleation events observed using a liquid-cell transmission electron microscope. Detectability was achieved using the machine learning equivalent of detection by humans watching a video numerous times. The detection system was applied to the nucleation of sodium chloride crystals from a saturated acetone solution of sodium chlorate. Nanoparticles with a radius of more greater than 150 nm were detected in a viewing area of 12 <i>μ</i>m × 12 <i>μ</i>m by the detection system. The analysis of the change in the size of the growing particles as a function of time suggested that the crystal phase of the particles with a radius smaller than 400 nm differed from that of the crystals larger than 400 nm. Moreover, the use of machine learning enabled the detection of numerous nanometer sized nuclei. The nucleation rate estimated from the machine-learning-based detection was of the same order as that estimated from the detection using manual procedures. Nucleation ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Cancan Huang | Brenda M. Rubenstein
Source: The Journal of Physical Chemistry A (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Machine Learning Diffusion Monte Carlo Forces Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C<sub>2</sub>, H<sub>2</sub>O, and CH<sub>3</sub>Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints. Monte Carlo method ; Diffusion Monte Carlo ; Molecular dynamics ; Statistical physics ; Density functional theory ; Work (physics) ; Ab initio ; Computation ; Computer science ; Potential energy ; Quantum Monte Carlo ; Energy minimization ; Computational chemistry ; Physics ; Monte Carlo molecular modeling ; Chemistry ; Algorithm ; Classical mechanics ; Mathematics ; Quantum mechanics ; Markov chain Monte Carlo Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Sergey Starikov | Daria Smirnova | Tapaswani Pradhan | И.В. Гордеев | Ralf Drautz | Matous Mrovec
Source: Physical Review Materials (article, 2022-04-26)
Field: Materials Science — Metals and Alloys

Angular-dependent interatomic potential for large-scale atomistic simulation of the Fe-Cr-H ternary system The recently developed angular-dependent potential for pure iron was advanced to the interatomic potential of the Fe-Cr-H ternary system. The new potential allows to simulate Fe-Cr alloys for a wide range of compositions and different concentrations of hydrogen. The angular-dependent format of the model and the development procedure based on the machine learning approach allow to achieve a favorable balance between the computation cost and the reliability of the created parametrization. As part of potential validation, we performed a large number of tests of both the binary metallic alloys and hydrogen interactions. The applicability of the potential is demonstrated by large-scale simulations of hydrogen diffusion in the vicinity of crystal defects. Materials science ; Ternary operation ; Parametrization (atmospheric modeling) ; Interatomic potential ; Diffusion ; Computation ; Hydrogen ; Carbide ; Range (aeronautics) ; Molecular dynamics ; Chemical physics ; Thermodynamics ; Metallurgy ; Computational chemistry ; Computer science ; Physics ; Algorithm ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Siqi Shi | Shiyu Sun | Shuchang Ma | Xinxin Zou | Quan QIAN | Yue Liu
Source: Journal of Inorganic Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Detection Method on Data Accuracy Incorporating Materials Domain Knowledge Due to the characteristics of small samples, high dimensions, and much noise, materials data often produce inconsistent results with those obtained from domain experts when used for machine learning modeling. Materials science ; Domain (mathematical analysis) ; Data mining ; Computer science Machine Learning in Materials Science ; Fault Detection and Control Systems ; Industrial Vision Systems and Defect Detection
Authors: Chenru Duan | Adriana Ladera | Julian C. -L. Liu | Michael G. Taylor | Isuru R. Ariyarathna | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2022-07-14)
Field: Materials Science — Materials Chemistry

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multireference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized octahedral mononuclear transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that the MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of the MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions. Ligand (biochemistry) ; Character (mathematics) ; Chemistry ; Computer science ; Computational chemistry ; Chemical physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: S. D. V. S. S. Varma Siruvuri | H. Verma | Brahmanandam Javvaji | P. R. Budarapu
Source: International Journal of Mechanics and Materials in Design (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Fracture strength of Graphene at high temperatures: data driven investigations supported by MD and analytical approaches Graphene ; Materials science ; Molecular dynamics ; Lattice (music) ; Solid mechanics ; Fracture (geology) ; Orientation (vector space) ; Fracture mechanics ; Artificial intelligence ; Composite material ; Machine learning ; Computer science ; Nanotechnology ; Geometry ; Mathematics ; Physics Graphene research and applications ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Kevin Ryczko | Jaron T. Krogel | Isaac Tamblyn
Source: Journal of Chemical Theory and Computation (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Machine Learning Diffusion Monte Carlo Energies We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn-Sham density functional theory (DFT) electron densities as input. The second uses kernel ridge regression (KRR) to predict atomic contributions to the DMC total energy using atomic environment vectors as input (we used atom-centered symmetry functions, atomic environment vectors from the ANI models, and smooth overlap of atomic positions). We first compare the methodologies on pristine graphene lattices, where we find that the KRR methodology performs best in comparison to gradient boosted decision trees, random forest, Gaussian process regression, and multilayer perceptrons. In addition, KRR outperforms VDNNs by an order of magnitude. Afterward, we study the generalizability of KRR to predict the energy barrier associated with a Stone-Wales defect. Lastly, we move from 2D to 3D materials and use KRR to predict total energies of liquid water. In all cases, we find that the KRR models are more accurate than Kohn-Sham DFT and all mean absolute errors are less than chemical accuracy. Monte Carlo method ; Computer science ; Statistical physics ; Diffusion ; Machine learning ; Physics ; Mathematics ; Thermodynamics ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Linwei Sai | Li Fu | Qiuying Du | Jijun Zhao
Source: Frontiers of Physics (article, 2022-11-05)
Field: Materials Science — Materials Chemistry

Graph attention network for global search of atomic clusters: A case study of Agn (n = 14−26) clusters Due to coexistence of huge number of structural isomers, global search for the ground-state structures of atomic clusters is a challenging issue. The difficulty also originates from the computational cost of ab initio methods for describing the potential energy surface. Recently, machine learning techniques have been widely utilized to accelerate materials discovery and molecular simulation. Compared to the commonly used artificial neural network, graph network is naturally suitable for clusters with flexible geometric environment of each atom. Herein we develop a cluster graph attention network (CGANet) by aggregating information of neighboring vertices and edges using attention mechanism, which can precisely predict the binding energy and force of silver clusters with root mean square error of 5.4 meV/atom and mean absolute error of 42.3 meV/Å, respectively. As a proof-of-concept, we have performed global optimization of medium-sized Agn clusters (n = 14−26) by combining CGANet and genetic algorithm. The reported ground-state structures for n = 14−21, have been successfully reproduced, while entirely new lowest-energy structures are obtained for n = 22−26. In addition to the description of potential energy surface, the CGANet is also applied to predict the electronic properties of clusters, such as HOMO energy and HOMO-LUMO gap. With accuracy comparable to ab initio methods and acceleration by at least two orders of magnitude, CGANet holds great promise in global search of lowest-energy structures of large clusters and inverse design of functional clusters. Ab initio ; Potential energy surface ; Cluster (spacecraft) ; Ground state ; Atom (system on chip) ; Computer science ; Graph ; Cluster analysis ; Artificial neural network ; Physics ; Theoretical computer science ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Z.A. Alrowaili | A. A. El-Hamalawy | Shaik Kareem Ahmmad | S.V.S. Bhadra Lasya | M.S. Al-Buriahi | Y. S. Rammah
Source: Optical Materials (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

A closer-look at lithium strontium boro-fluoride glasses doped with CeO2 and Yb2O3 ions: Synthesis, radiation shielding properties, and prediction of density using artificial intelligence techniques Lithium (medication) ; Strontium ; Artificial neural network ; Polynomial ; Materials science ; Doping ; Linear regression ; Analytical Chemistry (journal) ; Mineralogy ; Mathematics ; Machine learning ; Chemistry ; Statistics ; Computer science ; Mathematical analysis Radiation Shielding Materials Analysis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Wei Lu | Zhenze Yang | Markus J. Buehler
Source: Journal of Applied Physics (article, 2022-08-17)
Field: Materials Science — Biomaterials

Rapid mechanical property prediction and <i>de novo</i> design of three-dimensional spider webs through graph and GraphPerceiver neural networks Spider webs feature advanced structural performance due to the evolutionary success of over more than 3 × 109 years, including lightweight design and exceptional mechanical properties. Spider webs are appealing for bio-inspired design since web designs serve multiple functions including mechanical protection and prey catching. However, high computational cost and limited quantified web properties render extensive spider web studies challenging in part due to the high structural complexity and randomness of fiber arrangements in 3D webs. Here, we report a computational method to relate spider web graph microstructures to effective mechanical properties, focusing on strength and toughness, and upscaling from the microscopic to the mesoscale level. The new computational framework uses deep neural networks, trained on graph-structured Cyrtophora citricola spider web mechanical data, in order to capture complex cross-scale structural relationships. Three different models are developed and compared. First, two Graph Neural Network (GNN) models, a Graph Convolutional Network, and a Principal Neighborhood Aggregation method. Second, a GraphPerceiver transformer model that is fed similar input data as provided to the GNN approach but within a natural language modeling context using self-attention mechanisms. The GraphPerceiver model can achieve similar performance as the GNN model, offering added flexibility for building deep learning models of diverse hierarchical biological materials. As an application of the model, we propose a computational optimization tool for synthetic web design that is used to generate synthetic, de novo spider web architectures. Finally, multi-objective optimization enables us to discover web structures that meet specific mechanical properties as design objectives. Computer science ; Spider ; Artificial intelligence ; Theoretical computer science ; Biology ; Ecology Silk-based biomaterials and applications ; Surface Modification and Superhydrophobicity ; Insect and Arachnid Ecology and Behavior
Authors: Dengke Ma | Yuheng Xing | Lifa Zhang
Source: Journal of Physics Condensed Matter (review, 2022-11-22)
Field: Materials Science — Materials Chemistry

Reducing interfacial thermal resistance by interlayer Heat dissipation is crucial important for the performance and lifetime for highly integrated electronics, Li-ion battery-based devices and so on, which lies in the decrease of interfacial thermal resistance (ITR). To achieve this goal, introducing interlayer is the most widely used strategy in industry, which has attracted tremendous attention from researchers. In this review, we focus on bonding effect and bridging effect to illustrate how introduced interlayer decreases ITR. The behind mechanisms and theoretical understanding of these two effects are clearly illustrated. Simulative and experimental studies toward utilizing these two effects to decrease ITR of real materials and practical systems are reviewed. Specifically, the mechanisms and design rules for the newly emerged graded interlayers are discussed. The optimization of interlayers by machine learning algorithms are reviewed. Based on present researches, challenges and possible future directions about this topic are discussed. Bridging (networking) ; Thermal management of electronic devices and systems ; Electronics ; Computer science ; Thermal ; Nanotechnology ; Dissipation ; Focus (optics) ; Materials science ; Thermal resistance ; Mechanical engineering ; Engineering physics ; Biochemical engineering ; Engineering ; Physics ; Electrical engineering ; Thermodynamics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Electronic Packaging and Soldering Technologies
Authors: Yoni Choukroun | Lior Wolf
Source: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Geometric Transformer for End-to-End Molecule Properties Prediction Transformers have become methods of choice in many applications thanks to their ability to represent complex interactions between elements. However, extending the Transformer architecture to non-sequential data such as molecules and enabling its training on small datasets remains a challenge. In this work, we introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism. We further suggest an augmentation scheme for molecular data capable of avoiding the overfitting induced by the overparameterized architecture. The proposed framework outperforms the state-of-the-art methods while being based on pure machine learning solely, i.e. the method does not incorporate domain knowledge from quantum chemistry and does not use extended geometric inputs besides the pairwise atomic distances. Overfitting ; Computer science ; Transformer ; Pairwise comparison ; Encoder ; Architecture ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Electrical engineering ; Artificial neural network ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Subhrajit Ray
Source: Evergreen (article, 2022-03-01)
Field: Materials Science — Polymers and Plastics

Effect of Control Parameters on Erosion Wear Performance of Glass-Epoxy Composites Filled with Waste Marble Powder Marble waste is the principal solid waste generated during the processing of marbles in stone processing industries. Managing solid waste create a challenge for society. In the present study, composites are fabricated with different proportions (0, 5, 10, and 15 wt. %) of waste marble powder (WMP) along with epoxy and glass fiber by a simple polymer casting method. Erosion wear characterization of the composites have been conducted by following ASTM G76 standard. The experiment was statistically designed as per Taguchi's L16 model. The incorporation of WMP had shown an improvement in erosion resistance of composites. A theoretical model has been developed to predict the erosion wear rate of the composites. Subsequent to this, a neural network model is also developed to predict the erosion wear properties of glass-epoxy-marble (GEM) composites based on their experimental results obtained from the erosion test. The input data were trained in such a manner that the neural network model can precisely predict erosion wear properties of these composites. Moreover, the experimental results are compared with the predicted data to validate the neural network model. It is inferred, from the validation that the developed model can be utilized to predict the erosion wear rate of composites up to an accuracy of 95%. Epoxy ; Materials science ; Composite material ; Erosion ; Glass fiber ; Glass recycling ; Geology Flame retardant materials and properties ; Tribology and Wear Analysis