jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Viki Kumar Prasad | Alberto Otero‐de‐la‐Roza | Gino A. DiLabio
Source: Machine Learning Science and Technology (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Bridging the gap between high-level quantum chemical methods and deep learning models Abstract Supervised deep learning (DL) models are becoming ubiquitous in computational chemistry because they can efficiently learn complex input-output relationships and predict chemical properties at a cost significantly lower than methods based on quantum mechanics. The central challenge in many DL applications is the need to invest considerable computational resources in generating large ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>1</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:math> ) training sets such that the resulting DL model can be generalized reliably to unseen systems. The lack of better alternatives has encouraged the use of low-cost and relatively inaccurate density-functional theory (DFT) methods to generate training data, leading to DL models that lack accuracy and reliability. In this article, we describe a robust and easily implemented approach based on property-specific atom-centered potentials (ACPs) that resolves this central challenge in DL model development. ACPs are one-electron potentials that are applied in combination with a computationally inexpensive but inaccurate quantum mechanical method (e.g. double- ζ DFT) and fitted against relatively few high-level data ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>1</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:math> – <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>1</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:math> ), possibly obtained from the literature. The resulting ACP-corrected methods retain the low cost of the double- ζ DFT approach, while generating high-level-quality data in unseen systems for the specific property for which they were designed. With this approach, we demonstrate that ACPs can be used as an intermediate method between high-level approaches and DL model development, enabling the calculation of large and accurate DL training sets for the chemical property of interest. We demonstrate the effectiveness of the proposed approach by predicting bond dissociation enthalpies, reaction barrier heights, and reaction energies with chemical accuracy at a computational cost lower than the DFT methods routinely used for DL training data set generation. Bridging (networking) ; Quantum chemical ; Computer science ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science
Authors: Yaxin Wang | Shuyuan Li | Kong Meng | Yunjiang Zhang | Zhaolin Fang | Shaorui Sun
Source: ACS Sustainable Chemistry & Engineering (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation Poly(ethylene terephthalate) (PET) is a prevalent single-use plastic, posing a significant threat to the environment and human health due to its nondegradable properties. To confront this pressing issue, there is an urgent need for a method of PET degradation that is not only efficient but also cost-effective. Recognizing the intricate interactions of parameters, we propose an innovative approach that integrates two-step machine learning (ML) techniques to achieve our objective while minimizing experimental costs. A dataset comprising 120 distinct PET degradation conditions was created using high-throughput experimentation (HTE). Initially, eight ML algorithms were employed to train and predict performance, with the decision tree model yielding the most favorable outcomes. Subsequently, the trained ML model was expanded to encompass an extensive array of hypothetical reaction conditions, facilitating the identification of degradation formulations that exhibit exceptional performance. Additionally, through the integration of feature importance analysis, we systematically reconstruct a relevant chemical space. Following regression training and prediction, we identified reaction conditions with significantly higher degradation rates at ambient temperature. The utilization of these conditions not only enhances the efficacy of research and development, leading to reduced experimental costs, but also provides valuable insights for future investigations into PET degradation. In contrast to conventional, resource-intensive trial-and-error approaches, our established platform facilitates the assessment of PET degradation rates across diverse reaction conditions, enabling a preliminary screening for process optimization. Consequently, this approach contributes to the mitigation of solid waste pollution and the advancement of sustainable economic development. Throughput ; Degradation (telecommunications) ; Computer science ; Process engineering ; Chemistry ; Environmental science ; Materials science ; Engineering Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Medical Imaging Techniques and Applications
Authors: Yu Cai | Chunyan Wang | Huanli Yuan | Yuan Guo | Jun‐Hyung Cho | Xianran Xing | Yu Jia
Source: Materials Horizons (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring negative thermal expansion materials with bulk framework structures and their relevant scaling relationships through multi-step machine learning Discovering new negative thermal expansion (NTE) materials is a great challenge in experiment. Meanwhile, the machine learning (ML) method can be another approach to explore NTE materials using the existing material databases. Herein, we adopt the multi-step ML method with efficient data augmentation and cross-validation to identify around 1000 materials, including oxides, fluorides, and cyanides, with bulk framework structures as new potential NTE candidate materials from ICSD and other databases. Their corresponding coefficients of negative thermal expansion (CNTE) and temperature ranges are also well predicted. Among them, about 57 materials are predicted to have an NTE probability of 100%. Some predicted NTE materials were tested by the first-principles calculations with quasi-harmonic approximation (QHA), which indicates that the ML results are in good agreement with the first principles calculation results. Based on the comprehensive analysis of the existing and predicted NTE materials, we established three universal relationships of CNTE with an average electronegativity, porosity, and temperature range. From these, we also identified some important critical values characterizing the NTE property, which can serve as an important criterion for designing new NTE materials. Scaling ; Phase diagram ; Negative thermal expansion ; Thermal ; Materials science ; Diagram ; Computer science ; Thermal expansion ; Nanotechnology ; Phase (matter) ; Statistical physics ; Thermodynamics ; Physics ; Mathematics ; Geometry ; Composite material ; Database ; Quantum mechanics Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials ; Magnesium Oxide Properties and Applications
Authors: Jiang Chen | Cai Y. | Thomas A. Hazlehurst | Thomas P. Ilett | Alexander S. M. Jackson | David Hogg | Kevin J. Roberts
Source: Crystal Growth & Design (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Automated Growth Rate Measurement of the Facet Surfaces of Single Crystals of the β-Form of <scp>l</scp>-Glutamic Acid Using Machine Learning Image Processing Precision measurement of the growth rate of individual single crystal facets (<i>hkl</i>) represents an important component in the design of industrial crystallization processes. Current approaches for crystal growth measurement using optical microscopy are labor intensive and prone to error. An automated process using state-of-the-art computer vision and machine learning to segment and measure the crystal images is presented. The accuracies and efficiencies of the new crystal sizing approach are evaluated against existing manual and semi-automatic methods, demonstrating equivalent accuracy but over a much shorter time, thereby enabling a more complete kinematic analysis of the overall crystallization process. This is applied to measure in situ the crystal growth rates and through this determining the associated kinetic mechanisms for the crystallization of β-form l-glutamic acid from the solution phase. Growth on the {101} capping faces is consistent with a Birth and Spread mechanism, in agreement with the literature, while the growth rate of the {021} prismatic faces, previously not available in the literature, is consistent with a Burton-Cabrera-Frank screw dislocation mechanism. At a typical supersaturation of σ = 0.78, the growth rate of the {101} capping faces (3.2 × 10<sup>-8</sup> m s<sup>-1</sup>) is found to be 17 times that of the {021} prismatic faces (1.9 × 10<sup>-9</sup> m s<sup>-1</sup>). Both capping and prismatic faces are found to have dead zones in their growth kinetic profiles, with the capping faces (<i>σ</i><sub>c</sub> = 0.23) being about half that of the prismatic faces (<i>σ</i><sub>c</sub> = 0.46). The importance of this overall approach as an integral component of the digital design of industrial crystallization processes is highlighted. Facet (psychology) ; Crystallography ; Image processing ; Glutamic acid ; Chemistry ; Computer science ; Materials science ; Image (mathematics) ; Artificial intelligence ; Biochemistry ; Amino acid ; Psychology Crystallization and Solubility Studies ; Enzyme Structure and Function
Authors: Zakaria Kharbouch | Mustapha Bouchaara | Fadila Elkouihen | Abderrahmane Habbal | Ahmed Ratnani | Abdessamad Faik
Source: Solid State Ionics (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes Ionic conductivity ; Conductivity ; Electrolyte ; Solid-state ; Fast ion conductor ; Ionic bonding ; Type (biology) ; Materials science ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Physical chemistry ; Ion ; Geology ; Electrode ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Zheng Long | Hongmei Lü | Zhimin Zhang
Source: ACS Omega (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate materials design through property prediction and large-scale virtual screening. In this study, a new method (Lieconv-Tg) has been developed to predict glass-transition temperature (Tg) values from repeating units of polymers based on Lieconv, which is equivariant with transformations from any specified Lie group. The introduction of equivariance allows the prediction of molecular properties from their 3D structures, independent of orientation and position. A total of 27,659 homopolymers with Tg values were collected from PolyInfo, and a standard data set containing 7166 polymers (named data set_Tg) was created for training a robust Lieconv-Tg model. Using the 3D coordinates as input, Lieconv-Tg performs better than Edge-Conditioned Convolution (ECC), and the mean absolute error (MAE) is significantly reduced by ∼6 from ∼30 to ∼24 on both the validation set and the test set, and the <i>R</i><sup>2</sup> value for both the validation set and the test set can reach 0.90. Lieconv-Tg is thus used to screen promising candidates from a benchmark database named PI1M with 995,800 generated polymers. However, there are some implausible repeating units in PI1M. To get more reasonable candidates from PI1M, a new filtering method has been accomplished by utilizing Morgan fingerprints at the polymerization points (MF@PP) of repeating units in data set_Tg. The combination of a standard data set, Lieconv-Tg, and a more reasonable screening strategy provides new directions in materials design for polymers. Glass transition ; Test set ; Set (abstract data type) ; Polymer ; Benchmark (surveying) ; Scale (ratio) ; Data set ; Chemical space ; Computer science ; Algorithm ; Materials science ; Artificial intelligence ; Chemistry ; Composite material ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ata Madanchi | Michael Kilgour | Frederik Zysk | Thomas D. Kühne | Lena Simine
Source: The Journal of Chemical Physics (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Simulations of disordered matter in 3D with the morphological autoregressive protocol (MAP) and convolutional neural networks Disordered molecular systems, such as amorphous catalysts, organic thin films, electrolyte solutions, and water, are at the cutting edge of computational exploration at present. Traditional simulations of such systems at length scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To address this problem, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP), which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that, as we previously demonstrated, produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multi-elemental 3D and demonstrate performance using water as our test system in two scenarios: (1) liquid water and (2) samples conditioned on the presence of pre-selected motifs. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulations, including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in quantum dynamics simulations. We expect our approach to perform equally well on other disordered molecular systems in which structural correlations decay sufficiently fast while offering unique advantages in situations when the disorder is quenched rather than equilibrated. Computer science ; Convolutional neural network ; Molecular dynamics ; Water model ; Quantum ; Artificial intelligence ; Scale (ratio) ; Materials science ; Biological system ; Statistical physics ; Computational science ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Friedrich Hastedt | Rowan M. Bailey | Klaus Hellgardt | Sophia N. Yaliraki | Ehecatl Antonio del Rio‐Chanona | Dongda Zhang
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis EvalRetro: Unifying the evaluation of machine learning frameworks to enhance understanding and transparency for retrosynthesis. Interpretability ; Retrosynthetic analysis ; Reliability (semiconductor) ; Computer science ; Machine learning ; Artificial intelligence ; Natural language processing ; Chemistry Machine Learning in Materials Science
Authors: Mostafa Faghih Shojaei | Jamie Holber | S. Das | Gregory H. Teichert | T. Mueller | L. Hung | V. Gavini | Krishna Garikipati
Source: Journal of the Mechanics and Physics of Solids (article, 2024-06-08)
Field: Materials Science — Materials Chemistry

Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in Li <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e1104"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> </mml:math> CoO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e1112"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> Bridging (networking) ; Statistical mechanics ; Computation ; Statistical physics ; Mathematics ; Computer science ; Theoretical physics ; Physics ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Rania Loukil | Wejden Gazehi | Mongi Besbes
Source: Nanocomposites (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study comes up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based on Long Short Term Memory (LSTM) neurons. Besides, a comparison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparisons are conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, are employed for simulations. The evaluation criteria encompass accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency. Recurrent neural network ; Artificial neural network ; Computer science ; Monte Carlo method ; Artificial intelligence ; Deep learning ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Daniel Persaud | Logan Ward | Jason Hattrick‐Simpers
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Reproducibility in materials informatics: lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’ Reproducing results from a foundational materials informatics tool (magpie) is difficult and in this study, a failure. This failure yields tangible suggestions to promote easy adoption and trust of materials informatics in the future. Informatics ; Materials informatics ; Computer science ; Health informatics ; Data science ; Engineering ; Engineering informatics ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced X-ray and CT Imaging
Authors: Ethan P. Shapera | Dejan-Krešimir Buč̌ar | Rohit P. Prasankumar | Christoph Heil
Source: npj Computational Materials (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction of organic salt structure properties Abstract We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to describe a crystal by more than an order of magnitude compared to the full crystal graph representation. We construct machine learning models using the crystal graph singular value representations in order to predict the volume, enthalpy per atom, and metal versus semiconductor/insulator phase of DFT-relaxed organic salt crystals based on randomly generated unrelaxed crystal structures. Initial base models are trained to relate 89,949 randomly generated structures of salts formed by varying ratios of 1,3,5-triazine and HCl with the corresponding volumes, enthalpies per atom, and phase of the DFT-relaxed structures. We further demonstrate that the base model is able to be extended to related chemical systems (isomers, pyridine, thiophene and piperidine) with the inclusion of 2000 to 10,000 crystal structures from the additional system. After training a single model with a large number of data points, extension can be done at significantly lower cost. The constructed machine learning models can be used to rapidly screen large sets of randomly generated organic salt crystal structures and efficiently downselect the structures most likely to be experimentally realizable. The models can be used as a stand-alone crystal structure predictor, but may serve CSP efforts best as a filtering step in more sophisticated workflows. Salt (chemistry) ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Jiang-Tao Yang | Qingyang Fan | Ming Ye | Heng Liu
Source: Diamond and Related Materials (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning Density functional theory ; Carbon fibers ; Materials science ; Nanotechnology ; Deep learning ; Computer science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Composite material Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research