jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhan-Yun Zhang | Xin Liu | Lin Shen | Ling Chen | Wei‐Hai Fang
Source: The Journal of Physical Chemistry C (article, 2021-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals Nonlinear optical (NLO) crystals are the key materials in modern laser technology and science because of their intrinsic capability to convert the wavelength of the light source. The search for new NLO materials is still very active in both scientific and industrial communities. Machine learning (ML) becomes a powerful tool to explore new candidates of NLO materials and to reveal the underlying relationship between structures and properties. In this work, we have proposed multilevel features that are relevant to the atomic properties, the characters of fundamental structural groups, and the crystal structures to describe inorganic NLO crystals for machine learning. The first-level and second-level descriptors can be obtained based on chemical compositions of crystals without prior knowledge about crystal structures. Several ML classifiers have been optimized using a database that consists of hundreds of NLO crystals to identify the samples with desired birefringence (Δn) and second-order nonlinear coefficients (dij). In particular, almost all of the ML models that only involve the first-level and second-level features, called as the crystal-structure-free model, exhibit good classification performance. It is still far from perfect but suitable to act as a filter in the first step of high-throughput materials discovery. Using the optimized ML models, feature importance analyses and virtual screening processes have been performed to understand the relationship between the features and targeted properties and to extract the statistical pictures on elements and fundamental structural groups. Several unexplored crystals are also picked out as ML-proposed candidates, and three of them are suggested as new potential NLO materials based on further first-principle calculations. The present ML models are expected to accelerate the inverse design for new NLO crystals with desired properties. Birefringence ; Nonlinear optical ; Crystal (programming language) ; Computer science ; Nonlinear system ; Filter (signal processing) ; Machine learning ; Artificial intelligence ; Feature (linguistics) ; Materials science ; Algorithm ; Biological system ; Optics ; Physics Crystal Structures and Properties ; Nonlinear Optical Materials Research ; X-ray Diffraction in Crystallography
Authors: Anthony Y. Fong | Lenson A. Pellouchoud | Malcolm Davidson | Richard C. Walroth | Carena Church | Ekaterina Tcareva | Liheng Wu | Kyle Peterson | Bryce Meredig | Christopher J. Tassone
Source: The Journal of Chemical Physics (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Utilization of machine learning to accelerate colloidal synthesis and discovery Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability. Computer science ; Machine learning ; Process (computing) ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Johannes Allotey | Keith T. Butler | Jeyan Thiyagalingam
Source: ePubs (Science and Technology Facilities Council, Research Councils UK) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Entropy-based active learning of graph neural network surrogate models for materials properties Graph neural networks trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks once trained are able to make highly accurate predictions at a fraction of the cost of experiments or first-principles calculations of comparable accuracy. However, these networks typically rely on large databases of labeled experiments to train the model. In scenarios where data are scarce or expensive to obtain, this can be prohibitive. By building a neural network that provides confidence on the predicted properties, we are able to develop an active learning scheme that can reduce the amount of labeled data required by identifying the areas of chemical space where the model is most uncertain. We present a scheme for coupling a graph neural network with a Gaussian process to featurize solid-state materials and predict properties including a measure of confidence in the prediction. We then demonstrate that this scheme can be used in an active learning context to speed up the training of the model by selecting the optimal next experiment for obtaining a data label. Our active learning scheme can double the rate at which the performance of the model on a test dataset improves with additional data compared to choosing the next sample at random. This type of uncertainty quantification and active learning has the potential to open up new areas of materials science, where data are scarce and expensive to obtain, to the transformative power of graph neural networks. Artificial neural network ; Computer science ; Machine learning ; Artificial intelligence ; Gaussian process ; Graph ; Entropy (arrow of time) ; Gaussian ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Zhiwei Qiao | Lifeng Li | Shuhua Li | Hong Liang | Zhou Jian | Randall Q. Snurr
Source: AIChE Journal (article, 2021-06-17)
Field: Chemistry — Inorganic Chemistry

Molecular fingerprint and machine learning to accelerate design of <scp>high‐performance</scp> homochiral metal–organic frameworks Abstract Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for ( R,S )‐1,3‐dimethyl‐1,2‐propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO 2 ‐NHOH‐FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation. Steric effects ; Fingerprint (computing) ; Metal-organic framework ; Chemistry ; Similarity (geometry) ; Computer science ; Artificial intelligence ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Ryosuke Mizuguchi | Yasuhiko Igarashi | Hiroaki Imai | Yuya Oaki
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Lateral-size control of exfoliated transition-metal–oxide 2D materials by machine learning on small data A wide variety of nanosheets including monolayers and few-layers have attracted much interest as two-dimensional (2D) materials for the unique anisotropic structures and properties. On the other hand, one of the significant remaining and challenging issues is the lateral-size control. Since 2D materials are generally synthesized by the exfoliation of layered materials, the lateral size is not easily controlled in the breaking-down processes. The experimental factors have not been found for the control and prediction. In the present work, lateral sizes of the exfoliated transition-metal-oxide nanosheets were predicted and controlled by the assistance of machine learning. Layered composites of host inorganic layers and guest organic molecules were exfoliated into nanosheets in organic dispersion media. The lateral size of the nanosheets was estimated by dynamic light scattering (DLS), instead of microscopy methods, to achieve high-throughput analyses. Factors governing the lateral size are explored on the small experimental data by the assistance of sparse modeling, a method of machine learning. The eight physicochemical parameters of the organic guests and dispersion media were extracted by sparse modeling for the construction of the size-prediction model. The size-prediction model accelerated the selective syntheses of the nanosheets with large and small lateral sizes in a limited number of experiments. The results indicate that the prediction model is a guideline to find suitable exfoliation conditions for size control. Size-selective syntheses of a variety of 2D materials can be achieved by similar methods using sparse modeling on small experimental data. Moreover, sparse modeling is applicable to control the design and exploration of other materials and their processing based on small data. Oxide ; Materials science ; Transition metal ; Metal ; Nanotechnology ; Control (management) ; Metallurgy ; Computer science ; Artificial intelligence ; Chemistry ; Catalysis ; Organic chemistry Electronic and Structural Properties of Oxides ; Ga2O3 and related materials ; Transition Metal Oxide Nanomaterials
Authors: Yunmin Liang | Zhichun Liu | Wei Liu
Source: Energy and AI (article, 2021-02-06)
Field: Materials Science — Materials Chemistry

A co-training style semi-supervised artificial neural network modeling and its application in thermal conductivity prediction of polymeric composites filled with BN sheets Predicting the thermal conductivity of polymeric composites filled with BN sheets is helpful for fabricating thermal management material. In this study, a co-training style semi-supervised artificial neural network model (Co-ANN) was proposed to take advantage of unlabeled data to refine the prediction. The thermal conductivity of polymer matrix, the diameter, aspect ratio, and volume fraction of the BN sheets are considered as the input variables of the thermal conduction model. Two artificial neural network (ANN) learners with different architecture will label the unlabeled examples. Through estimating the labeling confidence from the mathematical influence and thermal conductive behavior, the most confidently labeled example will be used to augment the training dataset. The lower limit of the labeling confidence is introduced to reduce the data noise. After learning the augmented training information, a combination of two ANN regressors will construct the final Co-ANN thermal conduction model. Compared to other models, the newly developed Co-ANN thermal conduction model remarkably improves the thermal conductivity prediction and exhibits the best accuracy and generalization performance. The proposed method shows a vast potential in thermal conductive material design. Thermal conductivity ; Artificial neural network ; Materials science ; Thermal conduction ; Generalization ; Electrical conductor ; Thermal ; Artificial intelligence ; Composite material ; Matrix (chemical analysis) ; Machine learning ; Computer science ; Mathematics ; Thermodynamics Thermal properties of materials ; Heat Transfer and Optimization ; Composite Material Mechanics
Authors: Fumihiro Imoto | Masatoshi Imada | Atsushi Oshiyama
Source: Physical Review Research (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Order-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> orbital-free density-functional calculations with machine learning of functional derivatives for semiconductors and metals Orbital-free density-functional theory (OFDFT) offers a challenging way of electronic-structure calculations scaled as $\mathcal{O}(N)$ computation for system size $N$. We here develop a scheme of the OFDFT calculations based on the accurate and transferrable kinetic-energy density-functional (KEDF), which is created in an unprecedented way using appropriately constructed neural network (NN). We show that our OFDFT scheme reproduces the electron density obtained in the state-of-the-art DFT calculations and then provides accurate structural properties of 24 different systems, ranging from atoms, molecules, metals, semiconductors, and an ionic material. The accuracy and the transferability of our KEDF is achieved by our NN training system in which the kinetic-energy functional derivative (KEFD) at each real-space grid point is used. The choice of the KEFD as a set of training data is essentially important, because first it appears directly in the Euler equation, which one should solve, and second, its learning assists in reproducing the physical quantity expressed as the first derivative of the total energy. More generally, the present development of KEDF $T[\ensuremath{\rho}]$ is in the line of systematic expansion in terms of the functional derivatives ${\ensuremath{\delta}}^{{\ensuremath{\ell}}_{1}}T/\ensuremath{\delta}{\ensuremath{\rho}}^{{\ensuremath{\ell}}_{1}}$ through progressive increase of ${\ensuremath{\ell}}_{1}$. The present numerical success demonstrates the validity of this approach. The computational cost of the present OFDFT scheme indeed shows the $\mathcal{O}(N)$ scaling, as is evidenced by the computations of the semiconductor SiC used in power electronics. Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Kinetic energy ; Energy (signal processing) ; Linear scale ; Physics ; Algorithm ; Machine learning ; Mathematics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Tim Würger | Di Mei | Bahram Vaghefinazari | David A. Winkler | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Robert H. Meißner | Christian Feiler
Source: npj Materials Degradation (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

Exploring structure-property relationships in magnesium dissolution modulators Abstract Small organic molecules that modulate the degradation behavior of Mg constitute benign and useful materials to modify the service environment of light metal materials for specific applications. The vast chemical space of potentially effective compounds can be explored by machine learning-based quantitative structure-property relationship models, accelerating the discovery of potent dissolution modulators. Here, we demonstrate how unsupervised clustering of a large number of potential Mg dissolution modulators by structural similarities and sketch-maps can predict their experimental performance using a kernel ridge regression model. We compare the prediction accuracy of this approach to that of a prior artificial neural networks study. We confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates the automated discovery of chemicals with desired dissolution modulating properties from a commercial database. We subsequently prove this concept by blind validation of five chemicals. Dissolution ; Sketch ; Computer science ; Robustness (evolution) ; Chemical space ; Cluster analysis ; Workflow ; Artificial intelligence ; Property (philosophy) ; Kernel (algebra) ; Machine learning ; Biological system ; Data mining ; Database ; Chemistry ; Drug discovery ; Algorithm ; Mathematics Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; MXene and MAX Phase Materials
Authors: Nicholas A. Jose | Mikhail Kovalev | Eric Bradford | Artur M. Schweidtmann | Hua Chun Zeng | Alexei A. Lapkin
Source: Chemical Engineering Journal (article, 2021-07-17)
Field: Materials Science — Materials Chemistry

Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis Nanomaterials ; Throughput ; Yield (engineering) ; Benchmarking ; Nanotechnology ; Scalability ; Process engineering ; Computer science ; Biochemical engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Christopher M. Collins | Luke M. Daniels | Quinn Gibson | Michael W. Gaultois | Michael D. Moran | Richard Feetham | Michael J. Pitcher | Matthew S. Dyer | C. Delacotte | Marco Zanella | Claire A. Murray | Györgyi Glodán | Olivier Pérez | D. Pelloquin | Troy D. Manning | Jonathan Alaria | George R. Darling | John B. Claridge | Matthew J. Rosseinsky
Source: Angewandte Chemie International Edition (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning We report the aperiodic titanate Ba<sub>10</sub> Y<sub>6</sub> Ti<sub>4</sub> O<sub>27</sub> with a room-temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low-thermal-conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine-learning models. Thermal conductivity ; Artificial intelligence ; Oxide ; Materials science ; Machine learning ; Computer science ; Composite material ; Metallurgy Machine Learning in Materials Science ; Thermal properties of materials ; nanoparticles nucleation surface interactions
Authors: Vidyasagar Shetty | Shabari Shedthi B | J. Kumaraswamy
Source: Materials Today Proceedings (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques Machine learning ; Stability (learning theory) ; Artificial intelligence ; Convex hull ; Decision tree ; Support vector machine ; AdaBoost ; Computer science ; Artificial neural network ; Field (mathematics) ; Perovskite (structure) ; Materials science ; Mathematics ; Regular polygon ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Steven P. Bennett | Filip Szczypiński | Lukas Turcani | Michael E. Briggs | Rebecca L. Greenaway | Kim E. Jelfs
Source: Journal of Chemical Information and Modeling (article, 2021-08-13)
Field: Chemistry — Inorganic Chemistry

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as "easy-to-synthesize" or "difficult-to-synthesize" by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties. Computer science ; Workflow ; Limiting ; Bottleneck ; Computational model ; Machine learning ; Cheminformatics ; Realization (probability) ; Artificial intelligence ; Chemical space ; Biochemical engineering ; Nanotechnology ; Chemistry ; Materials science ; Drug discovery ; Engineering ; Computational chemistry ; Database ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Nataliya Lopanitsyna | Chiheb Ben Mahmoud | Michele Ceriotti
Source: Physical Review Materials (article, 2021-04-08)
Field: Materials Science — Materials Chemistry

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modeling of realistic thermodynamic conditions. Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations, that can therefore be combined with extensive thermodynamic sampling. We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point, and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost. We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modeling also, when needed, the impact of nuclear quantum fluctuations and electronic excitations. The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials. Materials science ; Statistical physics ; Observable ; Electronic structure ; Quantum ; Electron ; Sampling (signal processing) ; Material properties ; Computer science ; Physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques