jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timothy D. Loose | Patrick G. Sahrmann | Thomas S. Qu | Gregory A. Voth
Source: arXiv (Cornell University) (preprint, 2023-09-17)
Field: Materials Science — Materials Chemistry

Coarse-Graining with Equivariant Neural Networks: A Path Towards 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, the most significant of which is accuracy. Neural networks can inherently incorporate multi-body 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 the considerable amount of data needed 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 models 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 which incorporate equivariant convolutional operations can produce functional models using datasets 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) ; Machine learning ; Convolutional neural network ; Granularity ; Force field (fiction) ; Deep learning ; Path (computing) ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Block Copolymer Self-Assembly
Authors: Hyunwook Jung | Lena Sauerland | Sina Stocker | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces. Adsorption ; Surface (topology) ; Global optimization ; Potential energy surface ; Computer science ; Stability (learning theory) ; Catalysis ; Set (abstract data type) ; Chemistry ; Materials science ; Chemical physics ; Molecule ; Mathematics ; Machine learning ; Algorithm ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Topic Modeling
Authors: Yuanqi Du | Chenru Duan | Carla Gomes | John M. Gregoire | Yining Huang | Jianan Canal Li | Yingheng Wang | Tian Xie | Yanqiao Zhu
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery Feature (linguistics) ; Key (lock) ; Active learning (machine learning) ; Identification (biology) ; Deep learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Aaron Garrison | Javier Heras‐Domingo | John R. Kitchin | Gabriel dos Passos Gomes | Zachary W. Ulissi | Samuel M. Blau
Source: ChemRxiv (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_rev Dataset The discovery of novel, high-performing catalysts is essential to the economical development and deployment of many promising materials and fuels. Machine learning (ML) methods have proved useful in the acceleration of the catalyst discovery process, but often yield models restricted to specific chemical domains or types of structures. To obtain generalizable ML models, large and diverse datasets are needed, which tend to exist mostly for heterogeneous catalysis. The tmQM dataset, which contains 86,665 transition metal complexes and their properties calculated at the TPSSh/def2-SVP level of density functional theory, provided a promising dataset to train a generalizable ML model on homogeneous catalyst systems. We trained several ML models on tmQM, and found that these models consistently underpredicted the energies of a chemically distinct subset of the data. To address this, we present the tmQM_rev dataset, which filters out several structures in tmQM found to be missing hydrogens in their molecular geometries and recomputes the energies of all other structures in tmQM at the ωB97M-V/def2-SVPD level of density functional theory. ML models trained on tmQM_rev show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. With respect to test set MAE, EwT, and parity, the ML models tested on tmQM_rev were, from best to worst, GemNet-T > PaiNN ~ SpinConv > SchNet. For all models, performance improved when using only neutral structures instead of the entire dataset, which also has charged species. However, models trained on only neutral structures appeared to saturate, while those trained on the entire dataset did not. Learning curves indicate that the models capture the chemical diversity of the neutral species in tmQM_rev. More data improves the model only when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach where model weights were initialized from models trained on OC20 led to drastic improvements in model performance. These results indicate transferability between ML strategies of heterogeneous and homogeneous systems. Homogeneous ; Density functional theory ; Computer science ; Yield (engineering) ; Artificial neural network ; Catalysis ; Graph ; Transition metal ; Machine learning ; Biological system ; Chemistry ; Artificial intelligence ; Computational chemistry ; Statistical physics ; Theoretical computer science ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Si-Da Xue | Qi‐Jun Hong
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Materials Properties Prediction (MAPP): Empowering the prediction of material properties solely based on chemical formulas Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed Graph Neural Network (GNN) approach to construct models that predict four distinct material properties. Our graph model represents materials as element graphs, with chemical formula serving as the only input. This approach ensures permutation invariance, offering a robust solution to prior limitations. By employing bootstrap methods to train on this individual GNN, we further enhance the reliability and accuracy of our predictions. With multi-task learning, we harness the power of extensive datasets to boost the performance of smaller ones. We introduce the inaugural version of the Materials Properties Prediction (MAPP) framework, empowering the prediction of material properties solely based on chemical formulas. Computer science ; Machine learning ; Graph ; Reliability (semiconductor) ; Permutation (music) ; Task (project management) ; Artificial neural network ; Artificial intelligence ; Material properties ; Theoretical computer science ; Power (physics) ; Engineering ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ke Liu | Shangde Gao | Kaifan Yang | Yuqiang Han
Source:  (article, 2023-06-18)
Field: Materials Science — Materials Chemistry

PCVAE: A Physics-informed Neural Network for Determining the Symmetry and Geometry of Crystals The symmetry and geometry of a crystal fundamentally determine its various physical and chemical properties. However, crystal structure prediction, including space group determination and crystal structure optimization, remains an ongoing challenge because traditional DFT-based approaches are time- and computational-intensive even for one specific set of material, not to mention structure prediction of massive materials. This paper determines the geometric structure of massive crystals solely from chemical formulae from scratch. In addition, due to various phases or changing environmental conditions, different pressure and temperature for example, there could be multiple crystal structures corresponding to one chemical formula (MS4OF), which has been overlooked or poorly addressed in previous research. Hereby, we propose a Physics-informed Conditional Variational Auto Encoder (PCVAE) to encode possible symmetry and geometry distribution as well as various phases of a crystal with Gaussian distributions. PCVAE achieves a new state-of-the-art in crystal structure prediction. Extensive experiments demonstrate the strong predictive power of PCVAE. The code and datasets are available at https://github.com/zjuKeLiu/PCVAE. Symmetry (geometry) ; Gaussian ; Crystal (programming language) ; Computer science ; Crystal structure ; Crystal structure prediction ; Statistical physics ; Algorithm ; Geometry ; Physics ; Theoretical physics ; Mathematics ; Crystallography ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jan-Lucas Uslu | Taoufiq Ouaj | David Tebbe | A. N. Nekrasov | Jo Henri Bertram | Marc Schütte | Kenji Watanabe | Takashi Taniguchi | Bernd Beschoten | Lutz Waldecker | Christoph Stampfer
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

An open-source robust machine learning platform for real-time detection and classification of 2D material flakes The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of two-dimensional materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes' optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe$_2$, MoSe$_2$, CrI$_3$, 1T-TaS$_2$ and hexagonal BN. Our platform has been tested over a two-year period, during which more than $10^6$ images of multiple different materials were acquired by over 30 individual researchers. Computer science ; Wafer ; Substrate (aquarium) ; Exfoliation joint ; Artificial intelligence ; Materials science ; Hexagonal crystal system ; Task (project management) ; Inference ; Open source ; Pattern recognition (psychology) ; Graphene ; Nanotechnology ; Engineering ; Geology ; Software Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Mingze Zheng | Brandon C. Bukowski
Source: ChemRxiv (preprint, 2023-11-19)
Field: Chemistry — Inorganic Chemistry

Probing the role of acid site distribution on water structure in aluminosilicate zeolites: insights from molecular dynamics Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals de-rived from bio-based feedstocks. Zeolites are porous catalysts used extensively due to their shape-selective adsorption and confinement interactions; However the kinetics of zeolite-catalyzed reactions are significantly impacted by the presence of water, which may affect product selectivity and intrinsic rate constants depending on transition state polarity. In this study, we employed machine learning force fields (MLFFs) to accelerate ab initio molecular dynamics (AIMD) simulations and enhance the phase space exploration of water configurations in a model Brønsted acid zeolite, H-AFI. We interrogated the structure of adsorbed water based on the Si/Al ratio and acid site distribution to disentangle the impact of acid site density and distribution on water matrix organization as a function of water loading. We integrated adsorption thermodynamics, vibrational spectroscopy simulations, and local density maps to interrogate the spatial orientation of adsorbed water clusters and their degree of hydrogen bonding. Our analysis unveiled the intricate interplay between zeolite structure, Brønsted acid site location, and water where spatially disparate acid sites nucleate extended clusters that span siliceous regions of the zeolite. We found that the length scale of ordered water regions is directly related to the Si/Al ratio and spatial distribution of Al sites. These findings provide insights into the molecular-level structure of water in microporous aluminosilicate micropores and demonstrate how acid sites can be used to control water activity which has applications to heterogeneous catalysis and adsorptive separations. Zeolite ; Aluminosilicate ; Microporous material ; Molecular dynamics ; Chemical physics ; Chemistry ; Catalysis ; Adsorption ; Brønsted–Lowry acid–base theory ; Sodalite ; Nucleation ; Hydrogen bond ; Materials science ; Computational chemistry ; Chemical engineering ; Molecule ; Physical chemistry ; Organic chemistry Zeolite Catalysis and Synthesis ; Spectroscopy and Quantum Chemical Studies ; Clay minerals and soil interactions
Authors: Bing Yan | Kyunghyun Cho
Source: ChemRxiv (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

CatScore: A High-Efficiency Evaluation Metric for Asymmetric Catalyst Design Asymmetric catalysis plays a crucial role in advancing medicine and materials science. However, the prevailing experiment-driven methods for catalyst evaluation are both resource-heavy and time-consuming. To address this challenge, we present CatScore - a learning-centric metric designed for the automatic evaluation of catalyst design models at both instance and system levels. This approach harnesses the power of deep learning to predict product selectivity as a function of reactants and the proposed catalyst. The predicted selectivity serves as a quantitative score, enabling a swift and precise assessment of a catalyst’s activity. On an instance level, CatScore’s predictions correlate closely with experimental outcomes, demonstrating a Spearman’s ρ = 0.84, which surpasses the density functional theory (DFT) with ρ = 0.54 and round-trip accuracy metrics at ρ = 0.24. Importantly, when ranking catalyst candidates, CatScore achieves a mean reciprocal ranking significantly superior to traditional DFT methods, marking a considerable reduction in labor and time investments needed to find top-performing catalysts. Ranking (information retrieval) ; Metric (unit) ; Catalysis ; Computer science ; Selectivity ; Density functional theory ; Benchmark (surveying) ; Reciprocal ; Artificial intelligence ; Machine learning ; Chemistry ; Engineering ; Computational chemistry ; Operations management Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Kumar Ayush | Pooja Sahu | Sk. Musharaf Ali | Tarak K. Patra
Source: Physical Chemistry Chemical Physics (article, 2023-12-04)
Field: Materials Science — Ceramics and Composites

Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relationship of glasses due to their enormous compositions and chemical space. Here, we address this problem and develop a metamodel of the composition-atomistic structure relationship of a class of glassy materials <i>via</i> a machine learning (ML) approach. Within this ML framework, an unsupervised deep learning technique, <i>viz.</i>, a convolutional neural network (CNN) autoencoder, and a regression algorithm, <i>viz.</i> random forest (RF), are integrated into a fully automated pipeline to predict the spatial distribution of atoms in a glass. The RF regression model predicts the pair correlation function of a glass in a latent space. Subsequently, the decoder of the CNN converts the latent space representation to the actual pair correlation function of the given glass. The atomistic structures of silicate (SiO<sub>2</sub>) and sodium borosilicate (NBS) based glasses with varying compositions and dopants are collected from molecular dynamics (MD) simulations to establish and validate this ML pipeline. The model is found to predict the atom pair correlation functions for many unknown glasses very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of the composition-atomistic structure relationship of glasses and other materials. Borosilicate glass ; Silicate ; Silicate glass ; Correlation ; Materials science ; Mineralogy ; Chemistry ; Mathematics ; Metallurgy ; Organic chemistry Glass properties and applications ; Geochemistry and Geologic Mapping ; Mineralogy and Gemology Studies
Authors: Fatimah Othman Alqahtani | Nazish Parveen | Gausal A. Khan | Meerambika Behera | Sankha Chakrabortty | Suraj K. Tripathy
Source: Biotechnology and Genetic Engineering Reviews (article, 2023-05-25)
Field: Chemistry — Organic Chemistry

Synthesis, characterization and application of BR@Ag nanocomposite material for high degree reduction of p-nitro phenol under a suitable condition One of the most essential chemical processes that is utilized in the manufacturing of a great deal of contemporary goods is called heterogeneously catalyzed reactions, and it is also one of the most fascinating. Metallic nanostructures are heterogeneous catalysts for range reactions due to their huge surface area, large assembly of active surface sites, and quantum confinement effects. Unprotected metal nanoparticles suffer from irreversible agglomeration, catalyst poisoning, and limited life cycle. To circumvent these technical disadvantages, catalysts are frequently spread on chemically inert materials like as mesoporous Al2O3, ZrO2, and different types of ceramic material. In this research, plentiful bauxite residue is used to create a low-cost alternative catalytic material. We have hydrogenated p-Nitrophenol to p-Aminophenol on bauxite residue (BR) supported silver nanocomposites (Ag NCs). The phase and crystal structure, bond structure and morphological analysis of the developed material will be done XRD, FTIR, and SEM-EDX respectively. The ideal conditions were 150 ppm of catalyst, 0.1 mM of p-NP, and 10 minutes overall up-to 99% conversion of p-NP to p-AP. A multi-variable predictive model created using Response Surface Methodology (RSM) and a data-based Artificial Neural Network (ANN) model were found to be the best ways to predict the maximum conversion efficiency. ANN models predicted efficiency more accurately than RSM models, and the strong agreement between model predictions and experimental data was indicated by their low relative error (RE0.10), high regression coefficient (R2>0.97), and Willmott-d index (dwill-index > 0.95) values. Catalysis ; Materials science ; Bauxite ; Nanocomposite ; Chemical engineering ; Mesoporous material ; Crystal structure ; Phenol ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy Nanomaterials for catalytic reactions ; Copper-based nanomaterials and applications ; Ammonia Synthesis and Nitrogen Reduction
Authors: Kunfeng Chen | Qianyu Hu | Feng Liu | Dongfeng Xue
Source: Journal of Inorganic Materials (article, 2023-01-01)
Field: Materials Science — Biomaterials

Multi-scale Crystallization Materials: Advances in <i>in-situ</i> Characterization Techniques and Computational Simulations Large-sized crystalline materials are the basic raw materials in semiconductors, lasers, and communications.Preparation of large-scale, high-quality crystalline materials has become a bottleneck restricting the development of related industries.Breaking through the preparation theory and technology of large-sized crystal materials is the key to obtaining high-quality large-sized crystals.Preparation process of crystal materials often undergoes nucleation and growth stages, including multiple processes at spatiotemporal scale: from atom/molecules, through clusters and nuclei, to bulk crystals.To further explore and accurately understand the crystal growth mechanism, we need intensively study the multiscale process,multi-scale in situ characterization techniques, and computational simulation methods.Among them, the latest in situ characterization methods for crystal growth includes optical microscopy, electron microscopy, vibration spectra, synchrotron radiation, neutron technology, andespecially, machine learning method.Thus, the multi-scale computational simulation techniques for crystallization is introduced, for example, first principles calculation at atom/molecular scale, molecular dynamics simulation, Monte Carlo simulation, phase field simulation at mesoscopic scale, and finite element simulation at macroscopic scale.A single in situ characterization or simulation technique can only explore crystallization information over a specific time and space scale.To accurately and fully reflect the crystallization process, a combination of multi-scale methods is introduced.It can be speculated that the establishment of in situ characterization technology and computational simulation methods for the actual large-sized crystal growth environment will be the future development trend, which provides an important experimental and theoretical basis for developing crystallization theory and controlling crystal quality.Furthermore, it can be deduced that the combination of in situ characterization technology with machine learning and big data technology will be the trend for large-sized crystal growth. Materials science ; Characterization (materials science) ; In situ ; Crystallization ; Scale (ratio) ; Nanotechnology ; Chemical engineering ; Engineering ; Physics Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies