jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yujia Wang | Guoyan Li | Anand Natarajan | Sanjeev Mukerjee | Xiaoning Jin | Swastik Kar
Source: Advanced Materials Technologies (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Accelerating Optimal Synthesis of Atomically Thin MoS <sub>2</sub> : A Constrained Bayesian Optimization Guided Brachistochrone Approach Abstract A machine learning (ML) guided approach is presented for the accelerated optimization of chemical vapor deposition (CVD) synthesis of 2D materials toward the highest quality, starting from low‐quality or unsuccessful synthesis conditions. Using 26 sets of these synthesis conditions as the initial training dataset, our method systematically guides experimental synthesis towards optoelectronic‐grade monolayer MoS 2 flakes. A‐exciton linewidth (σ A ) as narrow as 38 meV could be achieved in 2D MoS 2 flakes after only an additional 35 trials (reflecting 15% of the full factorial design dataset for training purposes). In practical terms, this reflects a decrease of the possible experimental time to optimize the parameters from up to one year to about two months. This remarkable efficiency was achieved by formulating a constrained sequencing optimization problem solved via a combination of constraint learning and Bayesian Optimization with the narrowness of σ A as the single target metric. By employing graph‐based semi‐supervised learning with data acquired through a multi‐criteria sampling method, the constraint model effectively delineates and refines the feasible design space for monolayer flake production. Additionally, the Gaussian Process regression effectively captures the relationships between synthesis parameters and outcomes, offering high predictive capability along with a measure of prediction uncertainty. This method is scalable to a higher number of synthesis parameters and target metrics and is transferrable to other materials and types of reactors. This study envisions that this method will be fundamental for CVD and similar techniques in the future. Bayesian optimization ; Bayesian probability ; Computer science ; Mathematical optimization ; Materials science ; Mathematics ; Artificial intelligence 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Pankaj Chouhan | Sachin Shanbhag
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Surrogate Modeling of Sparse Functional Data Obtained from Molecular Models of Polymer Viscoelasticity Viscoelasticity ; Gaussian ; Surrogate data ; Relaxation (psychology) ; Linear model ; Computer science ; Biological system ; Synthetic data ; Polymer ; Dispersity ; Statistical physics ; Materials science ; Algorithm ; Nonlinear system ; Physics ; Thermodynamics ; Chemistry ; Machine learning ; Polymer chemistry ; Computational chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Massimiliano Lupo Pasini | Mariia Karabin | Markus Eisenbach
Source: ChemRxiv (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Transferable prediction of formation energy across lattices of increasing size In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum (NiPt) solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic (FCC) crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of geometry optimization, we first trained the GCNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of the GCNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes. Transferability ; Energy minimization ; Lattice (music) ; Atom (system on chip) ; Range (aeronautics) ; Crystal structure ; Scalability ; Materials science ; Computer science ; Molecular physics ; Statistical physics ; Chemical physics ; Chemistry ; Crystallography ; Physics ; Computational chemistry ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Shifeng Jin | Qi Li | Rui Jiao | Liming Wu | Tiannian Zhu | Wenbing Huang | Junyang Liu | Hongming Weng | Xiaolong Chen
Source: Research Square (preprint, 2024-10-28)
Field: Materials Science — Materials Chemistry

Powder Diffraction Crystal Structure Determination Using Generative Models Generative grammar ; Powder diffraction ; Diffraction ; Materials science ; Crystallography ; Crystal structure ; Computer science ; Chemistry ; Artificial intelligence ; Optics ; Physics X-ray Diffraction in Crystallography
Authors: Giovanni Vito Spinelli | Davide Evangelista | Liwei Hu | Fabiana Zama
Source: Neural Computing and Applications (article, 2024-12-18)
Field: Chemistry — Analytical Chemistry

Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems Relaxometry ; Computer science ; Artificial neural network ; Biological system ; Nuclear magnetic resonance ; Artificial intelligence ; Materials science ; Pattern recognition (psychology) ; Physics ; Magnetic resonance imaging ; Spin echo Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: Xingyi Zhou | Li Liu | Yipeng Fei | Jinbo Liu | Jueyong Ning | Haoxuan Xia | Peng Zhu | Ruiqi Shen
Source: Chemical Engineering Science (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Micro-scale crystallization thermodynamics study of typical energetic compounds integrating optofluidics and machine learning Optofluidics ; Crystallization ; Scale (ratio) ; Thermodynamics ; Statistical physics ; Materials science ; Physics ; Microfluidics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Qi Li | Rui Jiao | Liming Wu | Tiannian Zhu | Wenbing Huang | Shifeng Jin | Junyang Liu | Hongming Weng | Xiaolong Chen
Source: arXiv (Cornell University) (preprint, 2024-09-07)
Field: Materials Science — Materials Chemistry

Powder Diffraction Crystal Structure Determination Using Generative Models Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining inorganic crystal structures from powder X-ray diffraction (PXRD) data is traditionally a labor-intensive and time-consuming process that demands substantial expertise. In this work, we introduce PXRDGen, an end-to-end neural network that determines crystal structures by learning joint structural distributions from experimentally stable crystals and their PXRD, producing atomically accurate structures refined through PXRD data. PXRDGen integrates a pretrained XRD encoder, a diffusion/flow-based structure generator, and a Rietveld refinement module, enabling the solution of structures with unparalleled accuracy in a matter of seconds. Evaluation on MP-20 inorganic dataset reveals a remarkable matching rate of 82% (1 sample) and 96% (20 samples) for valid compounds, with Root Mean Square Error (RMSE) approaching the precision limits of Rietveld refinement. PXRDGen effectively tackles key challenges in XRD, such as the precise localization of light atoms, differentiation of neighboring elements, and resolution of overlapping peaks. Overall, PXRDGen marks a significant advancement in the automated determination of crystal structures from powder diffraction data. Generative grammar ; Powder diffraction ; Diffraction ; Materials science ; Crystallography ; Crystal structure ; Computer science ; Artificial intelligence ; Chemistry ; Optics ; Physics X-ray Diffraction in Crystallography
Authors: Yuehui Xian | Pengfei Dang | Yuan Tian | Xue Jiang | Yumei Zhou | Xiangdong Ding | Jun Sun | Turab Lookman | Dezhen Xue
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Compositional Design of Multicomponent Alloys Using Reinforcement Learning Reinforcement ; Reinforcement learning ; Materials science ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Adwait Vipra | Pradnyesh Utpat | Vishal Patil | Amit Joshi | Suraj Sawant
Source:  (article, 2024-06-08)
Field: Chemistry — Analytical Chemistry

Automatic Detection of Invasive Aquatic Weeds Using Remotely Sensed Multispectral Imagery Recent rise in the population of the invasive aquatic vegetation in the Bhima River and its tributaries near the Pune city of India, is one of the major alarming concerns for the local environment and corresponding ecosystem. The Invasive Aquatic Weeds have been proliferating highly due to the presence of untreated sewage and the high levels of water contamination arising from the hazardous liquids directly flowing in the Bhima River from the industries in the neighborhood of Pune city. An attempt has been made in this research to incorporate the Multispectral Imagery acquired from the Sentinel-2 Satellite belonging to the European Space Agency. An Optimum Index Factor is employed to calculate the subset of three bands with the highest amount of the spectral information from several other spectral bands under consideration. A unique band composite of the selected Optimum Index Factor Bands of Green, Red and Near-Infrared with the Normalized Difference Vegetation Index, has been incorporated to differentiate the Invasive Aquatic Weeds from other materials having unique spectral signatures in the regions of interest. The Machine Learning Architectures of the Artificial Neural Network and the XGBoost have been employed in the Automatic Detection of the Invasive Aquatic Weeds in the Bhima River and one of its tributaries, the Mula-Mutha River. The results of the experimentation concludes that the machine learning approach of the XGBoost outperforms the Artificial Neural Network, both approaches have produced high accuracies due to richness of the data obtained from the meticulous data extraction pipeline. Multispectral image ; Remote sensing ; Computer science ; Artificial intelligence ; Environmental science ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: L. Klochko | Mathieu d’Aquin | Atsushi Togo | Laurent Chaput
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

Transfer Learning for Deep Learning-based Prediction of Lattice Thermal Conductivity Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of these properties have been a barrier, leading to predictive models with limited precision or the ability to generalize. This is particularly true of lattice thermal conductivity (LTC): existing datasets of precise (ab initio, DFT-based) computed values are limited to a few dozen materials with little variability. Based on such datasets, we study the impact of transfer learning on both the precision and generalizability of a deep learning model (ParAIsite). We start from an existing model (MEGNet~\cite{Chen2019}) and show that improvements are obtained by fine-tuning a pre-trained version on different tasks. Interestingly, we also show that a much greater improvement is obtained when first fine-tuning it on a large datasets of low-quality approximations of LTC (based on the AGL model) and then applying a second phase of fine-tuning with our high-quality, smaller-scale datasets. The promising results obtained pave the way not only towards a greater ability to explore large databases in search of low thermal conductivity materials but also to methods enabling increasingly precise predictions in areas where quality data are rare. Generalizability theory ; Computer science ; Artificial intelligence ; Machine learning ; Thermal conductivity ; Transfer of learning ; Deep learning ; Lattice (music) ; Quality (philosophy) ; Ab initio ; Data mining ; Materials science ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Longhan Zhang | Temesgen Muruts Weldengus | Zhaoliang Lei | Haowen Wang | Yiteng Zhai | Jing Jiang | Ruixuan Song
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Navigating the Complexity of Hybrid Materials Without Structural Dependency: Perovgnn as a Map Dependency (UML) ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermal and Kinetic Analysis
Authors: P Guo | Hong Miao | Yanbo Zou | Shifeng Mao | Zejun Ding
Source: Measurement Science and Technology (article, 2024-02-20)
Field: Materials Science — Surfaces, Coatings and Films

Application of a machine learning method to model-based library approach for critical dimension measurements using CD-SEM Abstract A model-based library (MBL) method has already been established for the accurate measurement of the critical dimension (CD) of semiconductor linewidth using critical dimension scanning electron microscope (CD-SEM) images. In this work, the MBL method is further investigated by combining CD-SEM image simulation and a neural network algorithm. The secondary electron linescan profiles were first calculated using a Monte Carlo simulation method, enabling us to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g. top CD, sidewall angle and height) for Si and Au trapezoidal line structures. Machine learning methods have then been applied to predict the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviations of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. The findings show that the machine learning methods can be practically applied to the MBL method for reducing the library size, accelerating the construction of the MBL database and enriching the content of the available MBL database. Critical dimension ; Laser linewidth ; Scanning electron microscope ; Dimension (graph theory) ; Artificial neural network ; Monte Carlo method ; Computer science ; Line (geometry) ; Work (physics) ; Materials science ; Image (mathematics) ; Algorithm ; Artificial intelligence ; Machine learning ; Optics ; Physics ; Mathematics ; Geometry ; Thermodynamics ; Statistics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Semiconductor materials and devices
Authors: Arjun Neyyathala | Edvin Fako | Sandip De | Daria Gashnikova | Florian Maurer | Jan‐Dierk Grunwaldt | Stephan A. Schunk | Schirin Hanf
Source: ChemRxiv (preprint, 2024-03-13)
Field: Chemistry — Organic Chemistry

From poison to promoter: investigation of supported rhodium sulphides as heterogeneous hydroformylation catalysts Herein we report the use of supported nanoparticles of crystalline rhodium sulphides as active heterogeneous catalysts for the hydroformylation of alkenes showing an excellent selectivity towards the aldehyde products. It was found that supported RhxSy (x=17, y=15 or x=2, y=3 with 1 and 10 wt.% Rh) nanoparticles greatly outperform pure Rh nanoparticles in terms of activity, with Rh17S15/SiO2 being the superior catalyst candidate. The TOF for the hydroformylation of styrene, determined for the lead system Rh17S15/SiO2 (1 wt.% Rh), is with 2620 h-1 significantly higher than TOF values reported for rhodium phosphide-based systems. As the presence of S in the Rh sulphides introduces structural diversity in the bulk as well as the range of potential catalytic surfaces, we enumerate all possible terminations, use local environment descriptors combined with unsupervised Machine learning techniques and DFT to draw structure performance relationships. We find that the presence of S on the surface and in close proximity to the catalytic site had a profound impact on the chemical behavior of the catalyst, thus unlocking the tunability of the surface catalytic properties. This allows for the affinity towards the substrate to be modulated to a high degree ranging from S rich Rh-top like sites of low; to Rh rich, and in particular for Rh17S15, (Rh4) sites with adsorption energies rivaling those of pristine Rh and improved spatial resolution. Hydroformylation ; Rhodium ; Catalysis ; Chemistry ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; biodegradable polymer synthesis and properties
Authors: Longhan Zhang | Temesgen Muruts Weldengus | Meng Yu | Siyuan Xu | Zhaoliang Lei | Haowen Wang | Yiteng Zhai | Yang Yao | Ruixuan Song | Jing Jiang
Source: Acta Materialia (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map Materials science ; Dependency (UML) ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Zi Li | Wenbo Fu | Bochen Li | Yao Jia | Jiuchuang Yuan | Michael A. Bellucci | Guangxu Sun | Zhengtian Song | Liu Shi | Zhu Lang | Jian Ma | Shuhao Wen | Qun Zeng
Source: ChemRxiv (preprint, 2024-11-04)
Field: Materials Science — Materials Chemistry

Highly Efficient Crystallization Studies through Machine Learning and Automation Crystallization is an important process in a broad range of industries, though studies on this topic remain complicated. Recently, machine learning has been applied to resolve complex issues in chemistry and material science. Here we present a machine learning model integrated into a robotic platform to effectively propose and automatically perform crystallization experiments for organic small molecules. The model was pretrained on around 140000 simulated data generated by a kinetic model, and fine-tuned by over 7000 experimental data obtained on the automated workstation. The improved prediction accuracy and working efficiency of our integrated platform were presented in case studies compared with the traditional approach by humans. A feature contribution analysis demonstrates that this model provides a holistic data-based perspective of promoting and inhibiting influences to crystallization. This work thereby demonstrates the feasibility of applying machine learning techniques to solid-state studies to reduce cost, boost efficiency and deepen understanding. Automation ; Crystallization ; Computer science ; Artificial intelligence ; Engineering ; Mechanical engineering ; Chemical engineering Crystallization and Solubility Studies
Authors: Mengnan Cui | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2024-11-22)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Transfer Learning for Quantum Chemical Data Using A Robust Density Functional Tight Binding Baseline Machine learning has revolutionized the development of interatomic potentials over the past decade, offering unparalleled computational speed without compromising accuracy. However, the performance of these models is highly dependent on the quality and amount of training data. Consequently, the current scarcity of high-fidelity datasets (i.e. beyond semilocal density functional theory) represents a significant challenge for further improvement. To address this, this study investigates the performance of transfer learning (TL) across multiple fidelities for both molecules and materials. Crucially, we disentangle the effects of multiple fidelities and different configuration/chemical spaces for pre-training and fine-tuning, in order to gain a deeper understanding of TL for chemical applications. This reveals that negative transfer, driven by noise from low-fidelity methods such as a Density Functional Tight Binding (DFTB) baseline, can significantly impact fine-tuned models. Despite this, the multi-fidelity approach demonstrates superior performance compared to single-fidelity learning. Interestingly, it even outperforms TL based on foundation models in some cases, by leveraging an optimal overlap of pre-training and fine-tuning chemical spaces. Fidelity ; Transfer of learning ; Baseline (sea) ; Computer science ; High fidelity ; Noise (video) ; Ranging ; Density functional theory ; Training set ; Quality (philosophy) ; Artificial intelligence ; Machine learning ; Scarcity ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Biology ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: R. Hunter Wilson | Anoop R. Damodaran | Ambika Bhagi‐Damodaran
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-05)
Field: Chemistry — Inorganic Chemistry

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies. Lysine ; Heme ; Chemistry ; Rational design ; Computer science ; Biochemistry ; Stereochemistry ; Biology ; Enzyme ; Genetics ; Amino acid Metal-Catalyzed Oxygenation Mechanisms ; Protein Structure and Dynamics ; Electrocatalysts for Energy Conversion
Authors: Brooklyne Ariana Thompson | Shengguang Wang | Konstantinos A. Goulas | M. Ross Kunz | Rebecca Fushimi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Lowering the Barrier to Access Information-Rich Transient Kinetic Data for Machine Learning Methods Transient (computer programming) ; Computer science ; Kinetic energy ; Data science ; Physics ; Classical mechanics ; Programming language Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and Data Classification