jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chuan-Nan Li | Han-Pu Liang | Yifeng Duan | Zijing Lin
Source: Physical Review Materials (article, 2023-03-28)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions Structural prediction for the discovery of novel materials is a long sought-after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require expensive density functional theory (DFT) calculations and follow unintelligent search paths. Here a machine-learning based crystal combinatorial optimization program (CCOP) with a fitting-search style is proposed to drastically improve the efficiency of structural search in SA. CCOP uses a graph neural network energy prediction model to reduce the DFT cost and a deep reinforcement learning algorithm to direct the search path. Tests on six multicomponent alloys show the energy prediction model is capable of extracting the bonding characteristics of the complex alloys to achieve interpretability. It also achieves high accuracy with a tiny training set (an increment of 30 samples per iteration) by active learning in less than five iterations. Comparison with a few conventional methods shows that CCOP finds the lowest-energy structures with the smallest number of search steps. CCOP cuts the computing cost of SA by two orders of magnitude, while providing better search results than SA. CCOP is promising for serving as a broadly applicable tool for the efficient crystal structure predictions. Simulated annealing ; Interpretability ; Annealing (glass) ; Materials science ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ziqiao Meng | Peilin Zhao | Yang Yu | Irwin King
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges Reaction and retrosynthesis prediction are two fundamental tasks in computational chemistry. In recent years, these two tasks have attracted great attentions from both machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these two problems and achieved initial success. In this survey, we conduct a comprehensive investigation on advanced deep learning-based reaction and retrosynthesis prediction models. We first summarize the design mechanism, strengths and weaknesses of the state-of-the-art approaches. Then we further discuss limitations of current solutions and open challenges in the problem itself. Last but not the least, we present some promising directions to facilitate future research. To our best knowledge, this paper is the first comprehensive and systematic survey on unified understanding of reaction and retrosynthesis prediction. Retrosynthetic analysis ; Strengths and weaknesses ; Computer science ; Artificial intelligence ; Deep learning ; Machine learning ; Management science ; Data science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Pascal Plettenberg | Bernd Bauerhenne | Martı́n E. Garcia
Source: Communications Materials (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Neural network interatomic potential for laser-excited materials Abstract Data-driven interatomic potentials based on machine-learning approaches have been increasingly used to perform large-scale, first-principles quality simulations of materials in the electronic ground state. However, they are not able to describe situations in which the electrons are excited, like in the case of material processing by means of femtosecond laser irradiation or ion bombardment. In this work, we propose a neural network interatomic potential with an explicit dependency on the electronic temperature. Taking silicon as an example, we demonstrate its capability of reproducing important physical properties with first-principles accuracy and use it to simulate laser-induced surface modifications on a thin film at time and length scales that are impossible to reach with first-principles approaches like density functional theory. The method is general and can be applied not only to other laser-excited materials but also to condensed and liquid matter under non-equilibrium situations in which electrons and ions exhibit different temperatures. Excited state ; Interatomic potential ; Laser ; Femtosecond ; Electron ; Atomic physics ; Ion ; Artificial neural network ; Materials science ; Atomic units ; Ground state ; Silicon ; Work (physics) ; Scale (ratio) ; Molecular dynamics ; Computer science ; Physics ; Optoelectronics ; Artificial intelligence ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Natalia Osiecka | Anna Drzewicz | Ewa Juszyńska‐Gałązka
Source: Liquid Crystals (article, 2023-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning studies for liquid crystal texture recognition Machine learning techniques such as local binary pattern algorithm and convolutional neural network were applied to study textures of liquid crystal compound (S)-4'-(1-methyloctyloxycarbonyl) biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]-benzoate (3F7HPhH7). This compound exhibits in its polymorphism several smectic phases (smectic A, ferroelectric smectic C and antiferroelectric smectic C) and two glass states of antiferroelectric smectic C phase, which textures are difficult to distinguish. Proof-of-concept evidence is provided, demonstrating the ability of machine learning algorithms to identify transition temperatures and the respective phases involved. The article describes the procedure for preparing a dataset of textures obtained from polarised microscopy for classification using convolutional neural networks, especially in cases where the image dataset is unbalanced. It utilises the feature of their self-similarity and image augmentation, particularly the cropping procedure. A Kolmogorov–Smirnov test was conducted to check if selected images do not carry the same information. Antiferroelectricity ; Artificial intelligence ; Convolutional neural network ; Liquid crystal ; Texture (cosmology) ; Materials science ; Ferroelectricity ; Pattern recognition (psychology) ; Binary number ; Biphenyl ; Artificial neural network ; Phase (matter) ; Image (mathematics) ; Computer science ; Crystallography ; Mathematics ; Organic chemistry ; Chemistry Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Ziziphus Jujuba Studies and Applications
Authors: Zhaoyang Wang | Yuhang Jing | Chuan Zhang | Yi Sun | Weiqi Li | Jianqun Yang | Xingji Li
Source: Solid State Ionics (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

A machine-learning interatomic potential to understand the anisotropic fracture behavior of BaZrO3 material Materials science ; Fracture toughness ; Anisotropy ; Fracture mechanics ; Crystal (programming language) ; Crystal structure ; Composite material ; Ultimate tensile strength ; Nanocrystalline material ; Nanotechnology ; Crystallography ; Optics ; Chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Youfu Ma | Xianwei Zhang | Lin Zhu | Xiaowei Feng | Jamal A. H. Kowah | Jun Jiang | Lisheng Wang | Lihe Jiang | Xu Liu
Source: Molecules (article, 2023-08-10)
Field: Chemistry — Inorganic Chemistry

Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P–H Reactions The paper discussed the use of machine learning (ML) and quantum chemistry calculations to predict the transition state and yield of copper-catalyzed P-H insertion reactions. By analyzing a dataset of 120 experimental data points, the transition state was determined using density functional theory (DFT). ML algorithms were then applied to analyze 16 descriptors derived from the quantum chemical transition state to predict the product yield. Among the algorithms studied, the Support Vector Machine (SVM) achieved the highest prediction accuracy of 97%, with over 80% correlation in Leave-One-Out Cross-Validation (LOOCV). Sensitivity analysis was performed on each descriptor, and a comprehensive investigation of the reaction mechanism was conducted to better understand the transition state characteristics. Finally, the ML model was used to predict reaction plans for experimental design, demonstrating strong predictive performance in subsequent experimental validation. Support vector machine ; Yield (engineering) ; Quantum chemical ; Quantum ; Density functional theory ; Product (mathematics) ; Chemistry ; Computer science ; State (computer science) ; Sensitivity (control systems) ; Quantum yield ; Machine learning ; Artificial intelligence ; Biological system ; Algorithm ; Mathematics ; Computational chemistry ; Thermodynamics ; Engineering ; Physics ; Molecule ; Quantum mechanics Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods
Authors: Tomonori Ida | Honoka Kojima | Yuta Hori
Source: Chemical Communications (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches A learning model is proposed that predicts both products and reaction pathways by combining machine learning and reaction network approaches. By training 50 fundamental organic reactions, the learning model predicted the products and pathways of 35 test reactions with a top-5 accuracy of 68.6%. The model identified the key fragment structures of the intermediates and could be classified as several basic reaction rules in the context of organic chemistry, such as the Markovnikov rule. Markovnikov's rule ; Context (archaeology) ; Computer science ; Chemistry ; Artificial intelligence ; Key (lock) ; Reaction conditions ; Biochemical engineering ; Machine learning ; Organic chemistry ; Engineering ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Christopher Gaul | Santiago Cuesta‐López
Source: physica status solidi (b) (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Machine Learning for Orbital Energies of Organic Molecules Upwards of 100 Atoms Organic semiconductors are promising materials for cheap, scalable, and sustainable electronics, light‐emitting diodes, and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the vast chemical compound space. For example, the ionization energy should fit to the optical spectrum of sunlight, and the energy levels must allow efficient charge transport. Herein, a machine learning model is developed for rapidly and accurately estimating the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies of a given molecular structure. It is built upon the SchNet model [Schütt et al. (2018)] and augmented with a “Set2Set” readout module [Vinyals et al. (2016)]. The Set2Set module has more expressive power than sum and average aggregation and is more suitable for the complex quantities under consideration. Most previous models are trained and evaluated on rather small molecules. Therefore, the second contribution is extending the scope of machine learning methods by adding also larger molecules from other sources and establishing a consistent train/validation/test split. As a third contribution, a multitask ansatz is made to resolve the problem of different sources coming at different levels of theory. All three contributions in conjunction bring the accuracy of the model close to chemical accuracy. HOMO/LUMO ; Organic solar cell ; Molecular orbital ; Scalability ; Photovoltaics ; Ionization ; Charge (physics) ; Ionization energy ; Photovoltaic system ; Molecule ; Chemical space ; Chemical physics ; Molecular electronics ; Physics ; Computer science ; Chemistry ; Quantum mechanics ; Electrical engineering ; Database ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Oladipo Folorunso | Peter Olukanmi | Thokozani Shongwe | Emmanuel Rotimi Sadiku | Yskandar Hamam
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite Polymer nanocomposites are emerging hybrid materials for the production of energy storage electrodes, biomedical sensors, and building construction materials. However, experimentation cost and time can be unfavorable to their performance investigation. Therefore, using a modeling approach to predict the electrical conductivity of polymer nanocomposite is an effective approach in mitigating experimentation cost and time. Since the polymer nanocomposites’ electrical conductivity depends on several factors, the engagement of efficient analytical models for predicting their properties, cannot be overemphasized. Herein, this study developed a series-parallel model, which incorporates the connection between the polymer and the nanofillers for the prediction of the electrical conductivity of graphene-polypyrrole (Gr-PPy) and reduced graphene oxide/polyvinyl alcohol/polypyrrole (RGO/PVA/PPy) nanocomposites. In addition to explicit modelling, an artificial intelligence approach (neural network) was also explored for the prediction tasks. The results of the models in an entity and when compared to an existing model, show flexibility and accuracy for the polymer nanocomposites electrical conductivity prediction. It can be inferred that the model can be suitable to predict the electrical conductivity of polymer nanocomposites. Series (stratigraphy) ; Artificial neural network ; Nanocomposite ; Conductivity ; Electrical resistivity and conductivity ; Polymer ; Computer science ; Materials science ; Artificial intelligence ; Composite material ; Electrical engineering ; Engineering ; Physics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Smart Materials for Construction
Authors: Max Hodapp
Source: Computational Materials Science (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Machine learning is funny but physics makes the money: How machine-learning potentials can advance computer-aided materials design in metallurgy Physical metallurgy ; Haystack ; Ductility (Earth science) ; Computer science ; Automation ; Materials science ; Nanotechnology ; Mechanical engineering ; Artificial intelligence ; Engineering ; Creep ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuqi Tang | Haiyuan Chen | Jianwei Wang | Xiaobin Niu
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-aided band gap prediction of semiconductors with low concentration doping The important physical quantities of materials, such as band gap, have been predicted efficiently with the help of machine learning (ML), in addition to the widely used experimental or computational methods. Under this scheme, by combining density functional theory (DFT) calculations and ML predictive models, the band gaps of doped semiconductors with normal doping concentrations are predicted successfully. Our present work provides a solution to the problem of how to obtain the band gaps of semiconductors doped with extremely low concentrations, which are important cases for some device designs. The structures were constructed by configuration screening with a symmetric criterion, and three-dimensional spatial structural variation was mapped to one-dimensional features, which are the key steps for the ML predictive model. The biggest error in the predicted band gaps of dilute nitride-doped GaAs by ML models is not more than 10%, compared with values obtained from DFT. Considering the limitation of material data, a few-shot learning method was further adopted to check the performance of the predictive models. The performance of the ML models was validated using data out of training and testing datasets. Our method will efficiently accelerate the prediction of physical properties of semiconductors with extremely low-concentration doping. Doping ; Semiconductor ; Band gap ; Density functional theory ; Materials science ; Computer science ; Characterization (materials science) ; Work (physics) ; Machine learning ; Computational physics ; Artificial intelligence ; Biological system ; Algorithm ; Optoelectronics ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; GaN-based semiconductor devices and materials ; Ga2O3 and related materials
Authors: Wenlong Wang | Qilei Liu | Yachao Dong | Jian Du | Qingwei Meng | Lei Zhang
Source: AIChE Journal (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

<scp>ConvPred</scp>: A deep learning‐based framework for predictions of potential organic reactions Abstract The traditional theory‐based technologies on reaction prediction are not efficient due to their heavy dependence on human expertise and experience. To this end, this article proposes a framework for predictions of potential organic reactions based on reaction templates and two‐dimensional convolutional neural network (2D CNN) model. The quantum mechanics‐based σ ‐profiles and the sub‐molecular structure‐based ECFP4 are used individually to encode chemical reactions. Using 605,753 patented reactions extracted from the USPTO 1976‐2016 database and their generated counterparts, the 2D CNN models are trained to evaluate the likelihood of molecular transformations by learning the feature differences between reactants and products. The classification accuracies of the σ ‐profiles‐based model and the ECFP4‐based model for the non‐trained reactions are 97.881 and 99.593%. Challenging reactions from literature involving identification of chemo‐, stereo‐, and regio‐selectivity are correctly predicted. Furthermore, a σ ‐profiles‐based visual reaction fingerprint is introduced to provide novel insights into the model interpretability. Interpretability ; Convolutional neural network ; ENCODE ; Computer science ; Artificial intelligence ; Deep learning ; Feature (linguistics) ; Chemistry ; Identification (biology) ; Machine learning ; Biological system ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Tamir S. Hasan | Laurent Capolungo | M.A. Zikry
Source: npj Materials Degradation (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

A machine learning microstructurally predictive framework for the failure of hydrided zirconium alloys Abstract Hydride precipitation within zirconium alloys affects ductility and fracture behavior. The complex distribution of hydrides and their interaction with defects, such as dislocations, have a significant role in crack nucleation and failure. Hence, there is substantial variability in the microstructural behavior of hydrided zirconium. A deterministic fracture model coupled to a dislocation-density based crystalline plasticity approach was used to predict failure. Deterministic simulations were used to develop a database of crack initiation for representative microstructural characteristics, such as texture, crystalline structure, hydride orientations and spacing, and hydride geometry. The machine learning (ML) analysis is based on Extreme Value Theory (EVT) and a Bayesian based Gaussian Process Regression (GPR). Fracture probability is significantly influenced by hydride orientation and dislocation-density interactions. Furthermore, surrogate reduced order models (ROM) models were used to predict the likelihood of failure. This approach provides a ML framework to predict failure at different physical scales. Materials science ; Nucleation ; Dislocation ; Zirconium alloy ; Ductility (Earth science) ; Zirconium ; Texture (cosmology) ; Fracture (geology) ; Precipitation ; Hydride ; Metallurgy ; Composite material ; Thermodynamics ; Artificial intelligence ; Computer science Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Filip Dinic | Oleksandr Voznyy
Source: Advanced Theory and Simulations (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing Abstract Modeling and predicting battery cathode material voltage requires accurate structural information regarding the binding sites of lithium within a target structure. Obtaining these optimized structures requires some form of structural optimization. The ensuing complexity impedes the rapid screening of new materials for their suitability in energy storage. Previous machine learning (ML) models use structures of both lithiated and nonlithiated forms for training; essentially, reproducing what is already known, but failing to generalize to structures whose lithiated form is not available. To avoid this limitation, an ML model capable of predicting the voltage associated with the material's lithiation without explicitly requiring the lithiated structure is trained. The model's predictive power is improved by adding newly calculated data points, with the most impactful being materials with unfavorable Li binding, which are lacking in the original dataset. Using this model, new cathode candidates among an order of magnitude more materials than in previous studies are screened and the most promising ones are validated with density functional theory calculations. Considering additional stability and conductivity constraints, 572 materials with voltages greater than 3.5 V are predicted. Unexpectedly, some of them are not based on conventional transition metals, highlighting the power of an unbiased search. Battery (electricity) ; Cathode ; Voltage ; Computer science ; Lithium (medication) ; Class (philosophy) ; Stability (learning theory) ; Power (physics) ; Materials science ; Algorithm ; Machine learning ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Engineering ; Electrical engineering ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research