jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bor‐Yann Tseng | Chen‐Wei Conan Guo | Yu‐Chen Chien | Jyn‐Ping Wang | Chi‐Hua Yu
Source: Advanced Science (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Deep Learning Model to Predict Ice Crystal Growth The demand for highly specific and complex materials has made the development of controllable manufacturing processes crucial. Among the numerous manufacturing methods, casting is important because it is economical and highly flexible regarding the geometry of manufactured parts. Since solidification is an important stage in the casting process that influences the properties of the final product, the development of a controllable solidification process using modeling methods is necessary to create superior structural properties. However, traditional modeling methods are computationally expensive and require sophisticated mathematical schemes. Therefore, a deep learning model is proposed to predict the morphology of the dendritic crystal growth solidification process, along with a reinforcement learning model to control the solidification process. By training the deep learning model with data generated using the phase field method, the solidification process can be successfully predicted. The crystal growth structures are designed to be altered by adjusting the degree of supercooling in the deep learning model while implementing reinforcement learning to control the dendritic arteries. This research opens new avenues for applying artificial intelligence to the optimization of casting processes, with the potential to utilize it in the processing of advanced materials and to improve the target properties of material design. Process (computing) ; Computer science ; Reinforcement learning ; Casting ; Artificial intelligence ; Supercooling ; Materials science ; Mechanical engineering ; Process engineering ; Engineering ; Metallurgy Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: Yawen Wang | Thomas Wallmersperger | Adrian Ehrenhofer
Source: PAMM (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Application of back propagation neural networks and random forest algorithms in material research of hydrogels Abstract In the fourth paradigm of science, which is data‐driven discovery, the big data collected from the first three paradigms can be analyzed to gain information of the research object. Especially in the field of material science, various big data‐driven methods are applied in the tasks, such as material detection, material analysis and material design. In the current research, we focus on how data‐driven methods, e.g., machine learning algorithms, play a big role in deciphering processing‐properties‐performance (PPP) relationships in hydrogels. We present the procedure of (i) normalization of hydrogel properties, (ii) feature engineering of hydrogels, which is to summarize the decisive features in each PPP section of hydrogels, and (iii) database building by data extraction from scientific literature of hydrogels. Finally, we select the two most promising machine learning algorithms, back propagation neural network and random forest algorithm. The back propagation neural network can contribute to prediction of hydrogels properties and the random forest algorithm can be applied to obtain deeper understanding of hydrogels in the early stage of the research. Self-healing hydrogels ; Big data ; Computer science ; Artificial neural network ; Random forest ; Algorithm ; Backpropagation ; Normalization (sociology) ; Focus (optics) ; Artificial intelligence ; Machine learning ; Data mining ; Engineering Machine Learning in Materials Science ; Advanced Computing and Algorithms ; Hydrogels: synthesis, properties, applications
Authors: A. C. Arun Raj | Shubhabrata Datta
Source: Journal of Materials Engineering and Performance (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Designing Ti Alloy for Hard Tissue Implants: A Machine Learning Approach Materials science ; Titanium alloy ; Stress shielding ; Elastic modulus ; Alloy ; Titanium ; Biocompatibility ; Modulus ; Composite material ; Young's modulus ; Biomedical engineering ; Metallurgy ; Implant ; Surgery Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Bone Tissue Engineering Materials
Authors: Quin Hu | Andrew Johannesen | Daniel S. Graham | Jason D. Goodpaster
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking Neural network potentials achieve CASPT2 accuracy for reactive chemistry and molecular simulations. Using transfer learning, these potentials require minimal CASPT2 data on small systems to accurately predict bond dissociation in larger systems. Dissociation (chemistry) ; Artificial neural network ; Chemistry ; Potential energy ; Quality (philosophy) ; Biological system ; Computational chemistry ; Chemical physics ; Computer science ; Nanotechnology ; Artificial intelligence ; Physics ; Materials science ; Physical chemistry ; Atomic physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: I. A. Balyakin | R. E. Ryltsev | N. M. Chtchelkatchev
Source: Journal of Experimental and Theoretical Physics Letters (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems It has been studied whether machine learning interatomic potentials parameterized with only disordered configurations corresponding to liquid can describe the properties of crystalline phases and predict their structure. The study has been performed for a network-forming system SiO 2 , which has numerous polymorphic phases significantly different in structure and density. Using only high-temperature disordered configurations, a machine learning interatomic potential based on artificial neural networks (DeePMD model) has been parameterized. The potential reproduces well ab initio dependences of the energy on the volume and the vibrational density of states for all considered tetra- and octahedral crystalline phases of SiO 2 . Furthermore, the combination of the evolutionary algorithm and the developed DeePMD potential has made it possible to reproduce the really observed crystalline structures of SiO 2 . Such a good liquid–crystal portability of the machine learning interatomic potential opens prospects for the simulation of the structure and properties of new systems for which experimental information on crystalline phases is absent. Interatomic potential ; Parameterized complexity ; Artificial neural network ; Materials science ; Crystal structure prediction ; Software portability ; Crystal structure ; Ab initio ; Octahedron ; Molecular dynamics ; Chemical physics ; Statistical physics ; Machine learning ; Artificial intelligence ; Crystallography ; Algorithm ; Computer science ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Hang Liu | Liang Xu | Zongle Ma | Zhengquan Li | Haotian Li | Ying Zhang | Bo Zhang | Lingling Wang
Source: Materials Today Communications (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Accurate prediction of semiconductor bandgaps based on machine learning and prediction of bandgaps for two-dimensional heterojunctions Band gap ; Materials science ; Semiconductor ; Wide-bandgap semiconductor ; Hybrid functional ; Heterojunction ; Work (physics) ; Optoelectronics ; Machine learning ; Density functional theory ; Computer science ; Computational chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Dmitriy M. Makarov | Michail M. Lukanov | A. I. Rusanov | Н. Ж. Мамардашвили | Alexander A. Ksenofontov
Source: Journal of Computational Science (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes Yield (engineering) ; Computer science ; Process (computing) ; Pyrrole ; Condensation reaction ; Condensation ; Fingerprint (computing) ; Artificial intelligence ; Machine learning ; Reaction conditions ; Chemistry ; Biochemical engineering ; Organic chemistry ; Catalysis ; Materials science ; Engineering ; Programming language Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Advanced Chemical Sensor Technologies
Authors: Kai Ge | Yiping Huang | Yuanhui Ji
Source: Chinese Journal of Chemical Engineering (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism: Prediction for complex phase behaviors of pharmaceuticals and formulations Active ingredient ; Solubility ; Phase equilibrium ; Polymer ; Molecular descriptor ; Computer science ; Machine learning ; Phase (matter) ; Chemistry ; Artificial intelligence ; Biochemical engineering ; Organic chemistry ; Quantitative structure–activity relationship ; Engineering ; Bioinformatics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Massimiliano Lupo Pasini | Gang Seob Jung | Stephan Irle
Source: Computational Materials Science (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases Cubic crystal system ; Materials science ; Crystal structure ; Crystal (programming language) ; Alloy ; Niobium ; Solid solution ; Interatomic potential ; Atom (system on chip) ; Modulus ; Bulk modulus ; Thermodynamics ; Crystallography ; Molecular dynamics ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Composite material ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Daniel Schwalbe‐Koda | Daniel E. Widdowson | Tuan Anh Pham | Vitaliy Kurlin
Source: Digital Discovery (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances Crystallographic representations and machine learning predict inorganic synthesis conditions for arbitrary zeolites, as validated with literature-mined data. Crystallography ; Materials science ; Computer science ; Chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: N. Hung | Yinglong Miao
Source: The Journal of Physical Chemistry Letters (article, 2023-05-23)
Field: Materials Science — Materials Chemistry

Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network We have developed a new deep boosted molecular dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30 ns DBMD simulations captured up to 83-125 times more backbone dihedral transitions than 1 μs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300 ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on a deep learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/. Bayesian probability ; Deep neural networks ; Molecular dynamics ; Artificial neural network ; Probabilistic logic ; Artificial intelligence ; Gaussian ; Computer science ; Dynamics (music) ; Bayesian network ; Gaussian process ; Statistical physics ; Machine learning ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mengwei Gao | Bo Cai | Gaoyu Liu | Lili Xu | Shengli Zhang | Haibo Zeng
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors In order to accelerate the application of quaternary optoelectronic materials in the field of luminescence, it is crucial to develop new quaternary semiconductor materials with excellent properties. However, faced with vast alternative quaternary semiconductors, traditional trial-and-error methods tend to be laborious and inefficient. Here, we combined machine learning (ML) with density functional theory (DFT) calculation to predict the bandgaps of 2180 quaternary semiconductors, most of which were undeveloped but environmentally friendly. The evaluation coefficient (<i>R</i><sup>2</sup>) of the model using a random forest algorithm was up to 0.93 in ML. Four novel quaternary semiconductors with direct bandgaps: Ag<sub>2</sub>InGaS<sub>4</sub>, AgZn<sub>2</sub>InS<sub>4</sub>, Ag<sub>2</sub>ZnSnS<sub>4</sub>, and AgZn<sub>2</sub>GaS<sub>4</sub>, were selected from the ML model. Then their electronic structures and optical properties were further verified and studied by DFT calculations, which demonstrated that the four quaternary semiconductors had direct bandgaps, a small effective mass, and a large exciton binding energy and Stokes shift. Our calculation could significantly speed up the discovery of novel optoelectronic semiconductors and has a certain reference value for the study of luminescent materials and devices. Semiconductor ; Density functional theory ; Quaternary ; Materials science ; Nanotechnology ; Chemical physics ; Computational chemistry ; Chemistry ; Optoelectronics ; Geology Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides
Authors: Paul J. Dauenhauer
Source: Matter (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Up up down down left right left right B A Start for the catalytic hackers of programmable materials Catalysis ; Hacker ; Process (computing) ; Computer science ; Chemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Computer security Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Nam Q. Le | Michael Pekala | Alexander New | Edwin B. Gienger | Christine Chung | Timothy Montalbano | Elizabeth A. Pogue | Janna Domenico | Christopher D. Stiles
Source: The Journal of Physical Chemistry C (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Deep Learning Models to Identify Common Phases across Material Systems from X-ray Diffraction X-ray diffraction (XRD) is an indispensable tool for materials discovery and development, allowing researchers to confirm the synthesis of expected phases and determine the structures of new compounds in unexplored material systems. A common bottleneck is the identification of relevant phases from the XRD patterns, which even using current software requires human intervention and is thus not suitable for automated workflows. As a case study, we take the example of screening for A15-type phases based on XRD measurements of samples from systems spanning a broad space of 23 elements. Many A15 phases are type-II superconductors at comparatively high temperatures relative to other metallic alloys, motivating a hypothesis that seeking novel A15 phases will lead to a high proportion of novel superconductors. We report the results of convolutional neural networks trained to classify A15-like phases based on databases of synthetic data with simulated noise. Models exhibit high performance even when training, validation, and test sets are constructed such that they do not include examples from any material systems in common. Furthermore, performance is equally high on two data sets constructed from databases of crystal structures measured experimentally (Inorganic Crystal Structure Database, ICSD) and those computed using density functional theory (Materials Project). However, model performance decreased markedly when trained and tested across the two different sources, demonstrating a need for careful consideration when models are transitioned from large density functional theory (DFT)-computed databases to experimental data. A strategy of augmenting DFT-based patterns with experiment-based patterns is shown to be simple yet effective in improving performance across both MP- and ICSD-based data. Good performance is also observed on held-out XRD patterns measured experimentally on in-house samples. The approach demonstrated here can extend readily to other materials discovery settings where properties are strongly tied to particular phases that are distinguishable based on XRD patterns. Workflow ; Bottleneck ; Computer science ; Superconductivity ; Artificial intelligence ; Diffraction ; Identification (biology) ; Materials science ; Data mining ; Machine learning ; Physics ; Database ; Condensed matter physics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Dongming Wang | Yong Zhang | Yun-Fei Jia | Xian‐Cheng Zhang | Jianjun Yan | Wen-Xiang Shu | Shan‐Tung Tu
Source: Computational Materials Science (article, 2023-01-29)
Field: Materials Science — Materials Chemistry

Application of machine learning in the design and optimization of bimodal structural materials Ductility (Earth science) ; Finite element method ; Materials science ; Multi-objective optimization ; Computer science ; Microstructure ; Experimental data ; Personalization ; Elongation ; Structural engineering ; Mechanical engineering ; Machine learning ; Composite material ; Mathematics ; Ultimate tensile strength ; Engineering Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yuhao Liu | Xiaoxiao Huang | Xu Yan | Tao Zhang | Jiahao Sun | Yanan Liu
Source: ACS Applied Materials & Interfaces (article, 2023-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning Multicomponent materials are microwave-absorbing (MA) materials composed of a variety of absorbents that are capable of reaching the property inaccessible for a single component. Discovering mostly valuable properties, however, often relies on semi-experience, as conventional multicomponent MA materials’ design rules alone often fail in high-dimensional design spaces. Therefore, we propose performance optimization engineering to accelerate the design of multicomponent MA materials with desired performance in a practically infinite design space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with the expanded Maxwell–Garnett model, electromagnetic calculations, and experimental feedback; aiming at different desired performances, Ni surface@carbon fiber (NiF) materials and NiF-based multicomponent (NMC) materials with target MA performance were screened and identified out of nearly infinite possible designs. The designed NiF and NMC fulfilled the requirements for the X- and Ku-bands at thicknesses of only 2.0 and 1.78 mm, respectively. In addition, the targets regarding S, C, and all bands (2.0–18.0 GHz) were also achieved as expected. This performance optimization engineering opens up a unique and effective way to design microwave-absorbing materials for practical application. Materials science ; Material Design ; Microwave ; Variety (cybernetics) ; Computer science ; Engineering design process ; Mechanical engineering ; Space (punctuation) ; Property (philosophy) ; Process engineering ; Nanotechnology ; Composite material ; Artificial intelligence Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Magnetic Properties and Synthesis of Ferrites
Authors: Min Cheng | Zhiyuan Zhang | Shihui Wang | Kexin Bi | Kong‐Qiu Hu | Zhongde Dai | Yiyang Dai | Chong Liu | Li Zhou | Xu Ji | Wei‐Qun Shi
Source: Frontiers of Environmental Science & Engineering (article, 2023-07-15)
Field: Chemistry — Inorganic Chemistry

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning Metal-organic framework ; Ranking (information retrieval) ; Computer science ; Adsorption ; Grand canonical ensemble ; Iodine ; Scale (ratio) ; Monte Carlo method ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Radioactive element chemistry and processing
Authors: Longkun Xu | Wei Shao | Haishun Jin | Qiang Wang
Source: The Journal of Physical Chemistry C (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultralong Simulation in Lithium-Metal Batteries Modeling the formation of the solid–liquid interphase (SEI) is challenging due to its strict requirements of both simulation accuracy and length. Machine learning potential (MLP)-based molecular dynamics (MD) simulation is expected to play a role in this field, while currently its use is hindered by sampling efficiency and simulation stability. In this work, we tackle these two challenges together. We propose the stability-indicated sampling (SIS) algorithm for efficiently sampling training data using physical information (temperature). Unlike previous strategies, our method does not need prior knowledge of reaction networks or training multiple MLPs for uncertainty estimation. Our approach shows superior sampling efficiency without requirements of the diversity of initial training data, to realize >10 ns MLPMD simulation using an ab initio MD (AIMD) trajectory of just a few ps. We introduce the concept underlying instability consistency by showing the accuracy of reaction mechanisms, and the radial distribution function (RDF) can be improved by SIS-MLPMD, although their information is not explicitly used in our sampling decision. Furthermore, we show that long-time MLPMD simulation of lithium-metal batteries (LMB) can reproduce not only some well-known SEI components, including LiF, Li2O, LiOH, LiS, and incomplete N–S bond breaking in high-concentration systems, but also ionic aggregation structures of LiF, which is not shown in our AIMD training data but matches previous experimental results. Our work is expected to help accelerate future investigations, especially for efficient sampling fine-tuning data for pretrained MLPs and for studying long-time (≥ns scale) reaction dynamics in interfacial problems. Sampling (signal processing) ; Computer science ; Stability (learning theory) ; Molecular dynamics ; SPARK (programming language) ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research