jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Progna Banerjee | Alexander S. Filatov | Xiaobing Zuo | Benjamin T. Diroll | Elena V. Shevchenko
Source: ChemRxiv (preprint, 2023-10-05)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into Copper(I) and Copper (II) Cation Exchange Reactions in CdSe Nanoplatelets In this study, we investigated the synthesis of copper selenide nanoplatelets (NPLs) through a cation exchange reaction (CER) in 5 monolayers thick CdSe NPLs using Cu(I) and Cu(II) precursors. We discovered that the exposure of CdSe NPLs to Cu(I) precursor led to the transformation of NPLs into Cu2−xSe while maintaining their nanoplatelet morphology. The replacement of Cd(II) with Cu(I) prevailed over the formation of doped structures. In the case of Cu(II) precursor, we observed that Cu(II) was first reduced to Cu(I) before being intercalated into the host lattice, resulting in synthesis of Cu2−xSe, similar to CER with Cu(I) precursors but without preservation of the initial morphology of NPLs. Interestingly, the presence of oxygen was found to facilitate the cation exchange processes in CdSe NPLs, whereas a nitrogen atmosphere suppressed the CER. Despite the similar ionic sizes of Cu(I) and Cu(II), the substitution of Cd(II) with Cu(II) was found to be challenging, possibly due to the involvement of redox processes resulting in the significant deterioration of initial CdSe NPLs. We demonstrate that CER can achieve near-complete substitution of cadmium atoms with monovalent copper under room temperature. Understanding the processes involved in CERs is crucial for engineering more complex structures, such as high entropy nanoparticles involving cation exchange with different oxidation states and development of material synthesis using machine learning and artificial intelligence approaches. Copper ; Selenide ; Nanoparticle ; Chemistry ; Ion exchange ; Redox ; Inorganic chemistry ; Oxygen ; Ionic bonding ; Materials science ; Chemical engineering ; Selenium ; Ion ; Nanotechnology Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Jiawei Guo | Tyler Sours | Sam Holton | Chenghan Sun | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2023-05-31)
Field: Materials Science — Materials Chemistry

Quantifying the Limits of Methane Activation in Cu-exchanged Zeolites using Reactive and Interpretable Machine Learning based Potentials Copper-based zeolites have been widely explored as promising catalysts for the methane valorization reaction to form methanol. These studies are motivated by the hope of finding an elusive ‘Goldilocks’ topology or an active site that shows high methanol selectivity at reasonable methane conversions. As large-scale screening studies with density functional theory (DFT) remain challenging for zeolite catalysts, we now show that a reactive and interpretable machine learning-based potential (rMLP), developed using multistage active learning algorithm and a curriculum-based training (CBT) approach can be used to overcome this bottleneck. Our rMLP approach replaces expensive DFT-based NEB calculations without appreciable accuracy loss. We calculate methane activation barriers for all possible [CuOCu]2+ sites across 52 zeolites with an MAE of 0.07 eV versus DFT. By comparing with known experimental measurements, our approach establishes the limits of methane activation performance across 52 zeolite topologies. Finally, we show that our curriculum-based training (CBT) approach, which relies on several different types of calculations, gradually “teaches” the model about different relevant parts of the PES. This progressive training approach has important implications for the interpretability of emerging machine learning-based approaches. Interpretability ; Bottleneck ; Methane ; Zeolite ; Density functional theory ; Methanol ; Computer science ; Selectivity ; Catalysis ; Chemistry ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Computational chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Kishalay Das | Bidisha Samanta | Pawan Goyal | Seung‐Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: arXiv (Cornell University) (preprint, 2023-01-14)
Field: Materials Science — Materials Chemistry

CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials In recent years, graph neural network (GNN) based approaches have emerged as a powerful technique to encode complex topological structure of crystal materials in an enriched representation space. These models are often supervised in nature and using the property-specific training data, learn relationship between crystal structure and different properties like formation energy, bandgap, bulk modulus, etc. Most of these methods require a huge amount of property-tagged data to train the system which may not be available for different properties. However, there is an availability of a huge amount of crystal data with its chemical composition and structural bonds. To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unlabelled material data. Further, we extract distilled knowledge from CrysGNN and inject into different state of the art property predictors to enhance their property prediction accuracy. We conduct extensive experiments to show that with distilled knowledge from the pre-trained model, all the SOTA algorithms are able to outperform their own vanilla version with good margins. We also observe that the distillation process provides a significant improvement over the conventional approach of finetuning the pre-trained model. We have released the pre-trained model along with the large dataset of 800K crystal graph which we carefully curated; so that the pretrained model can be plugged into any existing and upcoming models to enhance their prediction accuracy. Leverage (statistics) ; Computer science ; ENCODE ; Property (philosophy) ; Graph ; Artificial intelligence ; Machine learning ; Distillation ; Crystal (programming language) ; Artificial neural network ; Data mining ; Algorithm ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks
Authors: John L. A. Gardner | Kathryn T. Baker | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2023-07-24)
Field: Materials Science — Materials Chemistry

Synthetic pre-training for neural-network interatomic potentials Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipelines is becoming an increasingly central challenge. Leveraging the idea of "synthetic" (artificial) data that is common in other areas of ML research, we here show that synthetic atomistic data, themselves obtained at scale with an existing ML potential, constitute a useful pre-training task for neural-network interatomic potential models. Once pre-trained with a large synthetic dataset, these models can be fine-tuned on a much smaller, quantum-mechanical one, improving numerical accuracy and stability in computational practice. We demonstrate feasibility for a series of equivariant graph-neural-network potentials for carbon, and we carry out initial experiments to test the limits of the approach. Computer science ; Artificial neural network ; Training set ; Interatomic potential ; Artificial intelligence ; Training (meteorology) ; Machine learning ; Stability (learning theory) ; Task (project management) ; Quantum ; Graph ; Molecular dynamics ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Roman Kempt | Agnieszka Kuc | Thomas Brumme | Thomas Heine
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Edge conductivity in PtSe$_2$ nanostructures PtSe$_2$ is a promising 2D material for nanoelectromechanical sensing and photodetection in the infrared regime. One of its most compelling features is the facile synthesis at temperatures below 500 °C, which is compatible with current back-end-of-line semiconductor processing. However, this process generates polycrystalline thin films with nanoflake-like domains of 5 to 100 nm size. To investigate the lateral quantum confinement effect in this size regime, we train a deep neural network to obtain an interatomic potential at DFT accuracy and use that to model ribbons, surfaces, nanoflakes, and nanoplatelets of PtSe$_2$ with lateral widths between 5 to 15 nm. We determine which edge terminations are the most stable and find evidence that the electrical conductivity is localized on the edges for lateral sizes below 10 nm. This suggests that the transport channels in thin films of PtSe$_2$ might be dominated by networks of edges, instead of transport through the layers themselves. Photodetection ; Materials science ; Conductivity ; Nanostructure ; Enhanced Data Rates for GSM Evolution ; Semiconductor ; Optoelectronics ; Crystallite ; Quantum dot ; Nanotechnology ; Condensed matter physics ; Chemistry ; Physics ; Photodetector ; Computer science Machine Learning in Materials Science
Authors: Arman Ter-Petrosyan | Jenna A. Bilbrey | Christina Doty | Bethany E. Matthews | Le Wang | Yingge Du | Eric Lang | Khalid Hattar | Steven R. Spurgeon
Source: arXiv (Cornell University) (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems. Images are oversegmented into overlapping chips, and similarity graphs are generated from embeddings extracted from a domain$\unicode{x2010}$pretrained convolutional neural network (CNN). The Louvain method for community detection is then applied to perform segmentation. The graph representation provides an intuitive way of presenting the relationship between chips and communities. We demonstrate our method to track irradiation$\unicode{x2010}$induced amorphous fronts in thin films used for catalysis and electronics. This method has potential for "on$\unicode{x2010}$the$\unicode{x2010}$fly" segmentation to guide emerging automated electron microscopes. Unicode ; Computer science ; Segmentation ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Electron microscope ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface Chemistry and Catalysis
Authors: Ji Qi | Zachary H. Aitken | Qing‐Xiang Pei | Anne Marie Z. Tan | Yunxing Zuo | Mark Hyunpong Jhon | S.S. Quek | Tongqi Wen | Zhaoxuan Wu | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

Machine Learning Moment Tensor Potential for Modelling Dislocation and Fracture in L1$_0$-TiAl and D0$_{19}$-Ti$_3$Al Alloys Dual-phase $γ$-TiAl and $α_2$-Ti$_{3}$Al alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughness at room temperature. Experimental studies show unusual plastic behaviour associated with ordinary and superdislocations, making it necessary to gain a detailed understanding on their core properties in individual phases and at the two-phase interfaces. Unfortunately, extended superdislocation cores are widely dissociated beyond the length scales practical for routine first-principles density-functional theory (DFT) calculations, while extant interatomic potentials are not quantitatively accurate to reveal mechanistic origins of the unusual core-related behaviour in either phases. Here, we develop a highly-accurate moment tensor potential (MTP) for the binary Ti-Al alloy system using a DFT dataset covering a broad range of intermetallic and solid solution structures. The optimized MTP is rigorously benchmarked against both previous and new DFT calculations, and unlike existing potentials, is shown to possess outstanding accuracy in nearly all tested mechanical properties, including lattice parameters, elastic constants, surface energies, and generalized stacking fault energies (GSFE) in both phases. The utility of the MTP is further demonstrated by producing dislocation core structures largely consistent with expectations from DFT-GSFE and experimental observations. The new MTP opens the path to realistic modelling and simulations of bulk lattice and defect properties relevant to the plastic deformation and fracture processes in $γ$-TiAl and $α_2$-Ti$_{3}$Al dual-phase alloys. Materials science ; Intermetallic ; Stacking fault ; Interatomic potential ; Ductility (Earth science) ; Density functional theory ; Ultimate tensile strength ; Titanium alloy ; Alloy ; Lattice (music) ; Condensed matter physics ; Dislocation ; Thermodynamics ; Molecular dynamics ; Creep ; Computational chemistry ; Metallurgy ; Composite material ; Physics ; Chemistry Hydrogen Storage and Materials ; Thermodynamic and Structural Properties of Metals and Alloys ; Chemical and Physical Properties of Materials
Authors: Abhishek Tewari | Rochan Bajpai | Atharva Shukla | Janish Kumar
Source: ChemRxiv (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models The efficient search for crystals with targeted properties is a significant challenge in materials discovery. The rapidly growing field of materials informatics has so far primarily focused on the application of AI/ML models to predict the properties of known crystals from their fundamental and derived properties as descriptors. In the last few years, deep learning-based approaches have spawned a slew of innovative data-driven materials research applications. Materials scientists have used these techniques for the reverse engineering of crystal structures for target applications. However, one of the challenges has been the representation of the crystal structures in the machine readable format. Proposed representations in the literature lack in generality and scalability. In this paper, we train a conditional variational autoencoder with a scalable and invertible representation along with the elemental properties of the constituents as descriptors to inverse-design new crystal structures with specified attributes. When targeting formation energy, we show that our model predicts structures that are not in the complete OQMD database. Finally, we use first-principles density functional theory calculations to validate our findings and show that the developed model is able to generate novel crystal structures for targeted property, i.e. formation energy in this case. Scalability ; Autoencoder ; Crystal structure prediction ; Computer science ; Representation (politics) ; Generality ; Artificial intelligence ; Property (philosophy) ; Crystal (programming language) ; Theoretical computer science ; Deep learning ; Crystal structure ; Database ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Qingyang Fan | G. Min | Li Liu | Yingbo Zhao | Xinhai Yu | Sining Yun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accelerate the Design of New Superhard Carbon Allotropes in the Pca21 Space Group: High-Throughput Screening and Machine Learning Strategies Throughput ; Space (punctuation) ; Group (periodic table) ; Carbon fibers ; Computer science ; Materials science ; Physics ; Operating system ; Algorithm ; Composite number ; Wireless Boron and Carbon Nanomaterials Research ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Longkun Xu | Wei Shao | Haishun Jin | Qiang Wang
Source: ChemRxiv (preprint, 2023-07-27)
Field: Materials Science — Materials Chemistry

Data Efficient and Stability Indicated Samplingfor Developing Reactive Machine LearningPotential to Achieve Ultra-long Simulation inLithium Metal Batteries Modelling the formation of solid-liquid interphase (SEI) is challenging as its strict requirement with 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 the two challenges together. We propose the stability-indicatedsampling (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. Compared with the recent proposed methods HAIR and DP-GEN, our approach gives significant improvement of sampling efficiency with less requirements with the initial training data, to realize > 10 ns MLPMD simulation using 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 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 battery (LMB) can not only reproduce some well-known SEI components including LiF, Li2O, LiOH, LiS and the incomplete N-S breaking in highconcentration systems, but also ionic aggregation structures of LiF, which is not shown in our AIMD training data but matches previous results of electrochemical impedance spectroscopy. Our work is expected to help accelerate future investigations, especially for studying long-time (≥ ns scale) reaction dynamics in interfacial problems. Stability (learning theory) ; Computer science ; Sampling (signal processing) ; Lithium (medication) ; Battery (electricity) ; SPARK (programming language) ; Work (physics) ; Field (mathematics) ; Machine learning ; Power (physics) ; Engineering ; Mathematics ; Mechanical engineering ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Maksim Zhdanov | Andrey Zhdanov
Source: arXiv (Cornell University) (preprint, 2023-04-28)
Field: Materials Science — Materials Chemistry

Machine learning-assisted close-set X-ray diffraction phase identification of transition metals Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to predict crystal structure phases from X-ray diffraction data of transition metals and their oxides. We evaluate the performance of our method and compare the variety of its settings. Our results demonstrate that the proposed machine learning framework achieves competitive performance. This demonstrates the potential for machine learning to significantly impact the field of X-ray diffraction and crystal structure determination. Open-source implementation: https://github.com/maxnygma/NeuralXRD. Diffraction ; Machine learning ; Computer science ; X-ray crystallography ; Crystal (programming language) ; Artificial intelligence ; Phase transition ; Identification (biology) ; Phase (matter) ; Field (mathematics) ; Set (abstract data type) ; Materials science ; Algorithm ; Optics ; Physics ; Condensed matter physics ; Mathematics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Iron and Steelmaking Processes
Authors: Krishnamohan Thekkepat | Sumanjit Das | Debi Prosad Dogra | Kapil Gupta | Seung‐Cheol Lee
Source: Journal of Physics Condensed Matter (article, 2023-09-02)
Field: Materials Science — Materials Chemistry

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of CE Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50% reduction in the training set size. The resultant sparse models can sample the configuration space at least 3 × faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd and (Ge,Sn)(S,Se,Te).The sparse CE models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling. Cluster expansion ; Algorithm ; Cluster (spacecraft) ; Block (permutation group theory) ; Set (abstract data type) ; Computer science ; Reduction (mathematics) ; Alloy ; Space (punctuation) ; Materials science ; Mathematics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Thermoelectric Materials and Devices
Authors: Shane Choo | Dohyun Park | Bernd Burgstaller
Source:  (article, 2023-03-15)
Field: Chemistry — Analytical Chemistry

Path-integrated concentration and multi-gas detection in FTIR spectroscopy with deep learning methods The accurate and efficient detection of molecular absorption signatures in FTIR output spectra is a challenging task for traditional filter and statistics-based methods; especially with the quantification of density and robustness to the presence of multiple molecules is concerned. Cross correlation, matched filter and support vector machine techniques generalise poorly to unseen variations of the input. In this work, we employ the powerful embedding capabilities of deep learning models to extract path-integrated concentrations of target gases from the complex spectra generated by HITRAN simulation in the mid-infrared spectrum. A quantitative study is done comparing the applicability of the common neural network types MLP, CNN, and LSTM. The results confirm that convolutional layers are substantially effective at capturing the “spatial” information present in characteristic absorption spectra. Furthermore, we show that such neural networks are robust to noise, temperature and concentration variations, and interference from the presence of other molecules. HITRAN ; Robustness (evolution) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Artificial neural network ; Filter (signal processing) ; Fourier transform ; Support vector machine ; Biological system ; Absorption spectroscopy ; Chemistry ; Mathematics ; Computer vision ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies