jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mantas Vaškevičius | Jurgita Kapočiūtė-Dzikienė | Liudas Šlepikas
Source: Electronics (article, 2022-04-24)
Field: Materials Science — Materials Chemistry

Modeling of the Crystallization Conditions for Organic Synthesis Product Purification Using Deep Learning Crystallization is an important purification technique for solid products in a chemical laboratory. However, the correct selection of a solvent is important for the success of the procedure. In order to accelerate the solvent or solvent mixture search process, we offer an in silico alternative, i.e., a never previously demonstrated approach that can model the reaction mixture crystallization conditions which are invariant to the reaction type. The offered deep learning-based method is trained to directly predict the solvent labels used in the crystallization steps of the synthetic procedure. Our solvent label prediction task is a multi-label multi-class classification task during which the method must correctly choose one or several solvents from 13 possible examples. During the experimental investigation, we tested two multi-label classifiers (i.e., Feed-Forward and Long Short-Term Memory neural networks) applied on top of vectors. For the vectorization, we used two methods (i.e., extended-connectivity fingerprints and autoencoders) with various parameters. Our optimized technique was able to reach the accuracy of 0.870 ± 0.004 (which is 0.693 above the baseline) on the testing dataset. This allows us to assume that the proposed approach can help to accelerate manual R&amp;D processes in chemical laboratories. Computer science ; Crystallization ; Artificial intelligence ; Vectorization (mathematics) ; Artificial neural network ; Solvent ; Process (computing) ; Task (project management) ; Machine learning ; Pattern recognition (psychology) ; Biological system ; Process engineering ; Chemistry ; Engineering ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Daniel Probst
Source: arXiv (Cornell University) (preprint, 2022-10-01)
Field: Materials Science — Materials Chemistry

Social and environmental impact of recent developments in machine learning on biology and chemistry research Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications. However, these discussions and publications focus mainly on computer science-adjacent fields, including computer vision and natural language processing or basic ML research. Using bibliometric analysis of the complete and full-text analysis of the open-access literature, we show that the same observations can be made for applied machine learning in chemistry and biology. These developments can potentially affect basic and applied research, such as drug discovery and development, beyond the known issue of biased data sets. Diversity (politics) ; Artificial intelligence ; Resource (disambiguation) ; Focus (optics) ; Natural resource ; Computer science ; Data science ; Management science ; Chemistry ; Engineering ethics ; Social science ; Political science ; Ecology ; Biology ; Sociology ; Engineering ; Physics Machine Learning in Materials Science
Authors: Massimiliano Lupo Pasini | GS Jung | Stephan Irle
Source: ChemRxiv (preprint, 2022-07-28)
Field: Materials Science — Materials Chemistry

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases We developed a graph convolutional neural network (GCNN) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN model on a dataset for nickel-niobium (NiNb) that was generated for simplicity with the embedded atom model (EAM) empirical interatomic potential. The dataset has been generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structure. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as functions of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys. Cubic crystal system ; Materials science ; Crystal structure ; Alloy ; Solid solution ; Crystal (programming language) ; Niobium ; Modulus ; Atom (system on chip) ; Crystallography ; Computer science ; Composite material ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Chen Liang | Bowen Wang | Shaogang Hao | Guangyong Chen | Pheng‐Ann Heng | Xiaolong Zou
Source: arXiv (Cornell University) (preprint, 2022-09-15)
Field: Materials Science — Materials Chemistry

Multi-Task Mixture Density Graph Neural Networks for Predicting Catalyst Performance Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as input, few GNN models can predict the thermodynamic properties of relaxed configurations with an acceptable level of error. In this work, we develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task. Taking CO adsorption on Cu-based single-atom alloy catalysts as an illustration, we show that our method can reliably estimate CO adsorption energy with a mean absolute error of 0.087 eV from the initial CO adsorption structures without costly first-principles calculations. Further, compared to other state-of-the-art GNN methods, our model exhibits improved generalization ability when predicting catalytic performance of out-of-domain configurations, built with either unseen substrate surfaces or doping species. We show that the proposed MT GNN strategy can facilitate catalyst discovery. Generalization ; Catalysis ; Adsorption ; Atom (system on chip) ; Artificial neural network ; Graph ; Materials science ; Task (project management) ; Alloy ; Computer science ; Reduction (mathematics) ; Density functional theory ; Extension (predicate logic) ; Algorithm ; Biological system ; Theoretical computer science ; Chemistry ; Mathematics ; Computational chemistry ; Artificial intelligence ; Physical chemistry ; Metallurgy ; Engineering ; Mathematical analysis ; Parallel computing Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Juliet Floyd | Jennifer R. Lukes
Source: The Journal of Chemical Physics (article, 2022-05-12)
Field: Materials Science — Materials Chemistry

A neural network-assisted open boundary molecular dynamics simulation method A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system. Molecular dynamics ; Artificial neural network ; Boundary (topology) ; Periodic boundary conditions ; Statistical physics ; Kinetic energy ; Computer science ; Domain (mathematical analysis) ; Potential energy ; Boundary value problem ; Canonical ensemble ; Energy (signal processing) ; Physics ; Classical mechanics ; Mathematics ; Mathematical analysis ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Arbaaz Khan | Saikou Ceesay | Yuan-Po Teng | Ruoli Wang | Haipeng Yue | David A. Lowther
Source:  (article, 2022-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generalizable Deep Neural Network Based Multi-Material Hysteresis Modeling Over the past couple of decades, dependence on magnetic materials has increased tremendously. Since every kind of magnetic material behaves differently, it is very difficult to create a universal model of these behaviors. While there exist models and simulation tools that can represent the behavior of these materials, it may take days or even an entire week to compute when embedded in an analysis system, which is highly inefficient. With a good machine learning (ML) model, the computation time can be reduced by a significant amount with minimal error. To determine performance parameters such as the efficiency of an electrical machine, the hysteretic behavior of the material is crucial, and the representation used can impact the performance of a simulation tool. The goal of this work is to explore several, different deep learning (DL) network architectures which might both provide a generalized representation methodology for hysteresis problems and reduce the computational effort needed to include the impact of hysteresis in a finite element-based simulation over existing approaches. Hysteresis ; Representation (politics) ; Computer science ; Computation ; Artificial neural network ; Magnetic hysteresis ; Finite element method ; Work (physics) ; Artificial intelligence ; Control engineering ; Machine learning ; Computer engineering ; Theoretical computer science ; Algorithm ; Mechanical engineering ; Engineering ; Magnetic field Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2022-08-12)
Field: Materials Science — Materials Chemistry

Beyond potentials: integrated machine-learning models for materials Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit their predictive accuracy, and extend greatly the length and time scales that are accessible to explicit atomistic simulations. Inexpensive predictions of the energetics of individual configurations have facilitated greatly the calculation of the thermodynamics of materials, including finite-temperature effects and disorder. More recently, machine-learning models have been closing the gap with first-principles calculations in another area: the prediction of arbitrarily complicated functional properties, from vibrational and optical spectroscopies to electronic excitations. The implementation of integrated machine-learning models, that combine energetic and functional predictions with statistical and dynamical sampling of atomic-scale properties is bringing the promise of predictive, uncompromising simulations of existing and novel materials closer to its full realisation. Closing (real estate) ; Computer science ; Atomic units ; Scale (ratio) ; Statistical physics ; Realisation ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Siyu Tian | Aron Walsh | Zekun Ren | Qianxiao Li | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2022-06-10)
Field: Materials Science — Materials Chemistry

What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning? Conventional wisdom of materials modelling stipulates that both chemical composition and crystal structure are integral in the prediction of physical properties. However, recent developments challenge this by reporting accurate property-prediction machine learning (ML) frameworks using composition alone without knowledge of the local atomic environments or long-range order. To probe this behavior, we conduct a systematic comparison of supervised ML models built on composition only vs. composition plus structure features. Similar performance for property prediction is found using both models for compounds close to the thermodynamic convex hull. We hypothesize that composition embeds structural information of ground-state structures in support of composition-centric models for property prediction and inverse design of stable compounds. Composition (language) ; Convex hull ; Property (philosophy) ; Computer science ; Machine learning ; Range (aeronautics) ; Inverse ; Artificial intelligence ; Compositional data ; Regular polygon ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Tetsuya Yamada | Takanori Watanabe | Kazuaki Hatsusaka | Jianjun Yuan | Michihisa Koyama | Katsuya Teshima
Source: CrystEngComm (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Importance of raw material features for the prediction of flux growth of Al<sub>2</sub>O<sub>3</sub> crystals using machine learning We evaluated the role of raw-material features for machine-learning prediction of the flux crystal growth of Al 2 O 3 in MoO 3 based on 185 types of growth trials. Flux (metallurgy) ; Raw material ; Crystal growth ; Crystal (programming language) ; Materials science ; Flux method ; Biological system ; Crystallography ; Computer science ; Metallurgy ; Chemistry ; Single crystal Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Metallurgical Processes and Thermodynamics
Authors: Tien Quang Nguyen | Yūsuke Nanba | Michihisa KOYAMA
Source: Journal of Computer Chemistry Japan (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

On the Thermodynamic Stability of Alloys: Combination of Neural Network Potential and Wang-Landau Sampling Thermodynamic properties and atomic configuration of PdRu alloy were investigated using Wang-Landau Monte Carlo method in combination with a newly-developed universal neural network potential. By using this new potential, excess energy of PdRu alloy was calculated. It is found that PdRu alloy in FCC lattice is unstable in the full range of alloy composition. This agrees with previous study based on density functional theory. The combined method was able to determine the configurational density of states, from which thermodynamic properties of the alloy were derived. It is found that when temperature increases, the excess free energy of the alloy is reduced, increasing the possibility of alloy mixing. Depending on the composition, transition peaks appear at finite temperatures where there are changes of preferable atomic arrangement due to the effect of temperature via the configurational entropy. In addition, the analyses on short-range order parameter and bond fraction show that PdRu alloy prefers to be in segregated form, where Pd and Ru are immiscible at low temperature, consistent with the experimental observations. The random mixing of Pd and Ru atoms in the form of solid-solution can occur at high temperature. Alloy ; Thermodynamics ; Configuration entropy ; Materials science ; Gibbs free energy ; Lattice (music) ; Mixing (physics) ; Entropy (arrow of time) ; Condensed matter physics ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Chemical Thermodynamics and Molecular Structure
Authors: Ibrahim Dubdub
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Pyrolysis Study of Mixed Polymers for Non-Isothermal Tga: Artificial Neural Networks Application Artificial neural network ; Pyrolysis ; Isothermal process ; Polymer ; Materials science ; Chemical engineering ; Polymer science ; Computer science ; Artificial intelligence ; Composite material ; Thermodynamics ; Engineering ; Physics Petroleum Processing and Analysis ; Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes
Authors: Artur M. Schweidtmann | Jan G. Rittig | Jana M. Weber | Martin Grohe | Manuel Dahmen | Kai Leonhard | Alexander Mitsos
Source: arXiv (Cornell University) (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that poorly generalize. We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is demonstrated with molecular properties calculated from quantum mechanical computations. We also compare our results to the recent set2set pooling approach. We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent. Overall, we show that the use of physical pooling functions significantly enhances generalization. Pooling ; Generalization ; Computer science ; Molecular graph ; Artificial intelligence ; Machine learning ; Function (biology) ; Computation ; Graph ; Theoretical computer science ; Mathematics ; Algorithm ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Matthew Witman | Anuj Goyal | Tadashi Ogitsu | Anthony H. McDaniel | Stephan Lany
Source: ChemRxiv (preprint, 2022-05-24)
Field: Materials Science — Materials Chemistry

Graph neural network modeling of vacancy formation enthalpy for materials discovery and its application in solar thermochemical water splitting We present a graph neural network modeling approach that fully automates the prediction of the DFT-relaxed vacancy formation enthalpy of any crystallographic site from its DFT-relaxed host structure. Applicable to arbitrary structures with an accuracy limited principally by the amount/diversity of the data on which it is trained, this model accelerates the screening of vacancy defects by many orders of magnitude by replacing the (up to 100s of) DFT supercell relaxations required for each symmetrically unique crystal site. It can thus be used off-the-shelf to rapidly screen 10,000s of crystal structures (which can contain millions of unique defects) from existing databases of DFT-relaxed crystal structures. We demonstrate the model's practical utility by high-throughput screening metal oxides from the Materials Project to identify high potential candidates for solar thermochemical water splitting. Ultimately, this modeling approach provides a significant screening and discovery capability for any application in which vacancy defects are the primary driver of a material's utility. Vacancy defect ; Supercell ; Enthalpy ; Materials science ; Crystal structure ; Crystal (programming language) ; Artificial neural network ; Computer science ; Crystallography ; Chemistry ; Thermodynamics ; Physics ; Machine learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Hayden Scheiber | G. N. Patey
Source: The Journal of Chemical Physics (article, 2022-11-04)
Field: Materials Science — Materials Chemistry

Binary salt structure classification with convolutional neural networks: Application to crystal nucleation and melting point calculations Convolutional neural networks are constructed and validated for the crystal structure classification of simple binary salts such as the alkali halides. The inputs of the neural network classifiers are the local bond orientational order parameters of Steinhardt, Nelson, and Ronchetti [Phys. Rev. B 28, 784 (1983)], which are derived solely from the relative positions of atoms surrounding a central reference atom. This choice of input gives classifiers that are invariant to density, increasing their transferability. The neural networks are trained and validated on millions of data points generated from a large set of molecular dynamics (MD) simulations of model alkali halides in nine bulk phases (liquid, rock salt, wurtzite, CsCl, 5-5, sphalerite, NiAs, AntiNiAs, and β-BeO) across a range of temperatures. One-dimensional time convolution is employed to filter out short-lived structural fluctuations. The trained neural networks perform extremely well, with accuracy up to 99.99% on a balanced validation dataset constructed from millions of labeled bulk phase structures. A typical analysis using the neural networks, including neighbor list generation, order parameter calculation, and class inference, is computationally inexpensive compared to MD simulations. As a demonstration of their accuracy and utility, the neural network classifiers are employed to follow the nucleation and crystal growth of two model alkali halide systems, crystallizing into distinct structures from the melt. We further demonstrate the classifiers by implementing them in automated MD melting point calculations. Melting points for model alkali halides using the most commonly employed rigid-ion interaction potentials are reported and discussed. Convolutional neural network ; Artificial neural network ; Nucleation ; Chemistry ; Halide ; Binary number ; Alkali metal ; Artificial intelligence ; Computer science ; Mathematics ; Inorganic chemistry Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Spectroscopy and Quantum Chemical Studies