jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rebecca C. Walker | Andres P. Hyer | Haiquan Guo | James K. Ferri
Source: Chemistry of Materials (article, 2023-06-26)
Field: Chemistry — Spectroscopy

Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning Silica aerogels are mesoporous high surface area materials with extensive synthetic and processing conditions. To effectively synthesize aerogels, the impact of synthetic pathways on the resulting aerogel properties must be understood prior to experimental investigation. We develop an information architecture, the silica aerogel graph database (103), and a supervised machine learning neural network regression model to examine these relationships. The property graph database enables rapid queries and visualization of the impact of the synthesis and processing conditions on the final aerogel properties. The model maps from silica aerogel synthetic and processing conditions to predict the aerogel BET surface area with an average error of 109 ± 84 m2/g. Following a validation experiment, the model was shown to predict the aerogel surface area from new synthetic and processing conditions with an error of less than 5%. The experiment demonstrates the usefulness of the model in surface area prediction through the compatibility between computational and experimental results. Both in its current form and with further expansion, the developed graph database could reduce experimental dimensionality, time, and resources, enabling the successful synthesis of high surface area silica aerogels, which are advantageous for applications including thermal insulation, sorption media, and catalysis. Aerogel ; Materials science ; Mesoporous material ; Computer science ; Sorption ; Graph ; Specific surface area ; Accessible surface area ; Machine learning ; Nanotechnology ; Catalysis ; Theoretical computer science ; Organic chemistry ; Chemistry Aerogels and thermal insulation ; Mesoporous Materials and Catalysis
Authors: Omid Shayestehpour | Stefan Zahn
Source: Journal of Chemical Theory and Computation (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials In recent years, deep eutectic solvents emerged as highly tunable and ecofriendly alternatives to common organic solvents and liquid electrolytes. In the present work, the ability of machine learning (ML) interatomic potentials for molecular dynamics (MD) simulations of these liquids is explored, showcasing a trained neural network potential for a 1:2 ratio mixture of choline chloride and urea (reline). Using the ML potentials trained on density functional theory data, MD simulations for large systems of thousands of atoms and nanosecond-long time scales are feasible at a fraction of the computational cost of the target first-principles simulations. The obtained structural and dynamical properties of reline from MD simulations using our machine learning models are in good agreement with the first-principles MD simulations and experimental results. Running a single MD simulation is highlighted as a general shortcoming of typical first-principles studies if the dynamic properties are investigated. Furthermore, velocity cross-correlation functions are employed to study the collective dynamics of the molecular components in reline. Molecular dynamics ; Choline chloride ; Eutectic system ; Deep eutectic solvent ; Work (physics) ; Density functional theory ; Computer science ; Nanosecond ; Materials science ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Ionic liquids properties and applications ; Crystallography and molecular interactions
Authors: Ravindranadh Bobbili | B. Ramakrishna
Source: Materials Today Communications (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Prediction of phases in high entropy alloys using machine learning Overfitting ; Support vector machine ; Materials science ; Machine learning ; Atomic radius ; Artificial intelligence ; Ensemble learning ; High entropy alloys ; Entropy (arrow of time) ; Random forest ; Statistical physics ; Computer science ; Thermodynamics ; Artificial neural network ; Physics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Materials Characterization Techniques
Authors: Qinghua Wei | Bin Cao | Hao Yuan | Youyang Chen | Kangdong You | Shuting Yu | Tixin Yang | Ziqiang Dong | Tong‐Yi Zhang
Source: npj Computational Materials (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility Abstract The attainment of both high strength and high ductility is always the goal for structure materials, because the two properties generally are mutually competing, called strength-ductility trade-off. Nowadays, the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility. To enhance both strength and ductility, a joint feature is proposed here to be the product of strength multiplying ductility. The strategy of “divide and conquer” is developed to solve the contradictory problem, that material experimental data of mechanical behaviors are, in general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR). The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning (ML) models conquer the three sub-domains, achieving significantly improved prediction accuracy and generality. After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration. Finally, the experiment results confirm the ML predictions, exhibiting novel lead-free solder alloys with high strength high ductility. Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys. Ductility (Earth science) ; Computer science ; Divide and conquer algorithms ; Structural engineering ; Materials science ; Machine learning ; Artificial intelligence ; Engineering ; Algorithm ; Composite material Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Injection Molding Process and Properties
Authors: Mosayeb Naseri | Sergey Gusarov | Dennis R. Salahub
Source: The Journal of Physical Chemistry Letters (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning in Materials Prediction: A Case Study on ABO<sub>3</sub> Perovskite Structures Quantum machine learning (QML), ML on quantum computers, offers a promising approach for discovering and screening novel materials. This study introduces a hybrid classical-quantum ML method using a variational quantum classifier to identify simple perovskite structures within a data set of ABO<sub>3</sub> compounds. The model is trained using a data set of 397 known ABO<sub>3</sub> compounds, with 254 perovskites and 143 non-perovskite structures labeled as +1 and -1, respectively. By considering feature correlation and eliminating less important features, the QML system achieves an optimal accuracy of 88% for training data and 87% for unseen test data. These results demonstrate the potential of QML in materials science classification tasks, even with limited training data, leveraging the intrinsic properties of quantum computation to enhance the investigation of materials. In addition, perspectives on QML applications in materials science are discussed. Quantum ; Perovskite (structure) ; Computer science ; Quantum computer ; Computation ; Artificial intelligence ; Materials science ; Algorithm ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Machine Learning and ELM
Authors: Xing Han | Wenqiang Zhang | Zhijie Chen | Yan Liu | Yong Cui
Source: Materials Horizons (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

The future of metal–organic frameworks and covalent organic frameworks: rational synthesis and customized applications Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are designable and tunable functional crystalline porous materials that have been explored for applications such as catalysis, chemical sensing, water harvesting, gas storage, and separation. On the basis of reticular chemistry, the rational design and synthesis of MOFs and COFs allows us to have unprecedented control over their structural features and functionalities. Given the vast number of possible MOF and COF structures and the flexibility of modifying them, it remains challenging to navigate the infinite chemical space solely through a trial-and-error process. This Opinion Article provides a brief perspective of the current state and future prospects of MOFs and COFs. We envision that emerging technologies based on machine learning and robotics, such as high-throughput computational screening and fully automatic synthesis, can potentially address some challenges facing this field, accelerating the discovery of porous framework materials and the development of rational synthetic strategies for customized applications. Metal-organic framework ; Nanotechnology ; Organic synthesis ; Throughput ; Covalent bond ; Materials science ; Rational design ; Biochemical engineering ; Computer science ; Systems engineering ; Engineering ; Chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Chemical Synthesis and Reactions
Authors: Timothy D. Loose | Patrick G. Sahrmann | Thomas S. Qu | Gregory A. Voth
Source: The Journal of Physical Chemistry B (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields, even when accounting for specialized hardware, which accelerates the training and integration of such networks. The second and the focus of this paper is the need for a considerable amount of data to train such force fields. It is common to use 10s of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG model's usefulness in the first place. As we investigate in this work, this "data-hunger" trap from neural networks for predicting molecular energies and forces can be remediated in part by incorporating equivariant convolutional operations. We demonstrate that, for CG water, networks that incorporate equivariant convolutional operations can produce functional models using data sets as small as a single frame of reference data, while networks without these operations cannot. Computer science ; Pairwise comparison ; Artificial neural network ; Artificial intelligence ; Field (mathematics) ; Force field (fiction) ; Convolutional neural network ; Machine learning ; Deep learning ; Granularity ; Path (computing) ; Mathematics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Jiali Guo | Ming Sun | Xueyan Zhao | Chaojie Shi | Haoming Su | Yanzhi Guo | Xuemei Pu
Source: Journal of Chemical Information and Modeling (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph. Cocrystal ; Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Representation (politics) ; Graph ; Feature (linguistics) ; Data mining ; Robustness (evolution) ; Generality ; Theoretical computer science ; Pattern recognition (psychology) ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Woonbae Sohn | Miyoung Kim | Ho Won Jang
Source: Small Science (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Atomic‐Scale Insights into the 2D Materials from Aberration‐Corrected Scanning Transmission Electron Microscopy: Progress and Future 2D crystals are attractive due to their unique atomic, electronic structures, and physiochemical properties, which strongly rely on the synthesis conditions. The atomic structure and presence of defects in the crystal lattice, such as vacancies, dopants, grain boundaries, and edge terminations, significantly influence the properties of 2D materials. Due to its high spatial resolution, aberration-corrected scanning transmission electron microscopy (AC-STEM) has become a powerful tool to provide atomic-scale insights into the crystal structure, defects, heterointerfaces, ferroelectricity, and in situ observations of 2D materials. This review will cover the status of atomic-scale studies on various 2D materials, including graphene, boron nitride, transition metal dichaogenides, MXenes, and phosphorene using AC-STEM. The future perspective of AC-STEM for new findings in 2D materials using machine learning is further discussed. Scanning transmission electron microscopy ; Atomic units ; Materials science ; Phosphorene ; Graphene ; Dopant ; Grain boundary ; Boron nitride ; Nanotechnology ; Transmission electron microscopy ; Chemical physics ; Condensed matter physics ; Optoelectronics ; Chemistry ; Doping ; Physics ; Composite material 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Michelle Ernst | Jack D. Evans | Ganna Gryn’ova
Source: Chemical Physics Reviews (article, 2023-10-20)
Field: Chemistry — Inorganic Chemistry

Host–guest interactions in framework materials: Insight from modeling The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science. Computer science ; Field (mathematics) ; Host (biology) ; Key (lock) ; Computation ; Metal-organic framework ; Monte Carlo method ; Nanotechnology ; Biochemical engineering ; Computational science ; Materials science ; Adsorption ; Chemistry ; Engineering ; Algorithm Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Zeolite Catalysis and Synthesis
Authors: Wenhao Dong | Heqing Tian | Wenguang Zhang | Junjie Zhou | Xinchang Pang
Source: ACS Applied Materials & Interfaces (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Development of NaCl–MgCl<sub>2</sub>–CaCl<sub>2</sub> Ternary Salt for High-Temperature Thermal Energy Storage Using Machine Learning NaCl-MgCl<sub>2</sub>-CaCl<sub>2</sub> eutectic ternary chloride salts are potential heat transfer and storage materials for high-temperature thermal energy storage. In this study, first-principles molecular dynamics simulation results were used as a data set to develop an interatomic potential for ternary chloride salts using a neural network machine learning method. Deep potential molecular dynamics (DPMD) simulations were performed to predict the microstructure and thermophysical properties of the NaCl-MgCl<sub>2</sub>-CaCl<sub>2</sub> ternary salt. This work reveals that DPMD simulations can accurately calculate the microstructure and thermophysical properties of ternary chloride salts. The association strength of chloride ions and cations follows the order of Mg<sup>2+</sup> > Ca<sup>2+</sup> > Na<sup>+</sup>, and the coordination number decreases gradually with increasing temperature, indicating a progressively looser and more disordered molten structure. Furthermore, thermophysical properties, such as density, specific heat capacity, thermal conductivity, and viscosity, are in good agreement with the experimental measurements. Machine learning molecular dynamics will provide a feasible multivariate molten salt exploration method for the design of next-generation solar power plants and thermal energy storage systems. Ternary operation ; Materials science ; Eutectic system ; Molten salt ; Thermal energy storage ; Microstructure ; Thermodynamics ; Molecular dynamics ; Thermal conductivity ; Work (physics) ; Chloride ; Thermal fluids ; Viscosity ; Thermal ; Composite material ; Chemistry ; Metallurgy ; Thermal resistance ; Computer science ; Computational chemistry Thermal Expansion and Ionic Conductivity ; Phase Change Materials Research ; Adsorption and Cooling Systems
Authors: Ivan A. Kruglov | A. V. Yanilkin | Yana Propad | Arslan Mazitov | Pavel Rachitskii | Artem R. Oganov
Source: npj Computational Materials (article, 2023-10-21)
Field: Materials Science — Materials Chemistry

Crystal structure prediction at finite temperatures Abstract Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solved this problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensive calculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum of the enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchable to zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction at finite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with a forcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomic potential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initio accuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalent solid (WB), an Earth-forming silicate MgSiO 3 (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO 2 . Crystal structure prediction ; Interatomic potential ; Ab initio ; Crystal structure ; Phase diagram ; Materials science ; Thermodynamics ; Enthalpy ; Statistical physics ; Molecular dynamics ; Physics ; Chemistry ; Phase (matter) ; Computational chemistry ; Crystallography Machine Learning in Materials Science ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications