jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tian Xie | Ha-Kyung Kwon | Daniel Schweigert | Sheng Gong | Arthur France‐Lanord | Arash Khajeh | Emily Crabb | Michael Puzon | Chris Fajardo | Will Powelson | Yang Shao‐Horn | Jeffrey C. Grossman
Source: APL Machine Learning (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

A cloud platform for sharing and automated analysis of raw data from high throughput polymer MD simulations Open material databases storing thousands of material structures and their properties have become the cornerstone of modern computational materials science. Yet, the raw simulation outputs are generally not shared due to their huge size. In this work, we describe a cloud-based platform to enable fast post-processing of the trajectories and to facilitate sharing of the raw data. As an initial demonstration, our database includes 6286 molecular dynamics trajectories for amorphous polymer electrolytes (5.7 terabytes of data). We create a public analysis library at https://github.com/TRI-AMDD/htp_md to extract ion transport properties from the raw data using expert-designed functions and machine learning models. The analysis is run automatically on the cloud, and the results are uploaded onto an open database. Our platform encourages users to contribute both new trajectory data and analysis functions via public interfaces. Finally, we create a front-end user interface at https://www.htpmd.matr.io/ for browsing and visualization of our data. We envision the platform to be a new way of sharing raw data and new insights for the materials science community. Terabyte ; Computer science ; Raw data ; Cloud computing ; Upload ; Interface (matter) ; Visualization ; Data sharing ; Data visualization ; Database ; World Wide Web ; Data mining ; Operating system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Graph Neural Networks
Authors: Arindam Debnath | Wesley F. Reinhart
Source: Computational Materials Science (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Investigating representation schemes for surrogate modeling of High Entropy Alloys Computer science ; Representation (politics) ; High entropy alloys ; Entropy (arrow of time) ; Machine learning ; Artificial intelligence ; Alloy ; Materials science ; Thermodynamics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: Chuan Zhou | Chen Chen | P. Hu | Haifeng Wang
Source: Journal of the American Chemical Society (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Topology-Determined Structural Genes Enable Data-Driven Discovery and Intelligent Design of Potential Metal Oxides for Inert C–H Bond Activation The identification of appropriate structural genes that influence the active-site configuration for a given reaction is critical for discovering potential catalysts with reduced reaction barriers. In this study, we introduce bulk-phase topology-derived tetrahedral descriptors as a means of expressing a catalyst's "material structural genes". We combine this approach with an interpretable machine learning model to accurately and efficiently predict the effective barrier associated with methane C-H bond cleavage across a wide range of metal oxides (MOs). These structural genes enable high-throughput catalyst screening for low-temperature methane activation and ultimately identify 13 candidate catalysts from a pool of 9095 MOs that are recommended for experimental synthesis. The topology-based method that we describe can also be extended to facilitate high-throughput catalyst screening and design for other dehydrogenation reactions. Dehydrogenation ; Catalysis ; Chemistry ; Topology (electrical circuits) ; Methane ; Inert ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Xinxin Niu | Yanfeng Dang | Yajing Sun | Wenping Hu
Source: Journal of Energy Chemistry (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

Judicious training pattern for superior molecular reorganization energy prediction model Mean squared error ; Computer science ; Feature (linguistics) ; Construct (python library) ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Machine learning ; Convolutional neural network ; Boom ; Correlation coefficient ; Pearson product-moment correlation coefficient ; Variance (accounting) ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Arsen Sultanov | Jean‐Claude Crivello | Tabea Rebafka | Nataliya Sokolovska
Source: Journal of Chemical Information and Modeling (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared with the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along with the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models based on descriptor-based metrics is provided. Computer science ; Crystal structure prediction ; Chemical space ; Crystal (programming language) ; Generative model ; Probabilistic logic ; Artificial intelligence ; Generative grammar ; Machine learning ; Algorithm ; Crystal structure ; Biological system ; Theoretical computer science ; Statistical physics ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Benedikt Winter | Philipp Rehner | Timm Esper | Johannes Schilling | André Bardow
Source: arXiv (Cornell University) (preprint, 2023-09-21)
Field: Materials Science — Materials Chemistry

Understanding the language of molecules: Predicting pure component parameters for the PC-SAFT equation of state from SMILES A major bottleneck in developing sustainable processes and materials is a lack of property data. Recently, machine learning approaches have vastly improved previous methods for predicting molecular properties. However, these machine learning models are often not able to handle thermodynamic constraints adequately. In this work, we present a machine learning model based on natural language processing to predict pure-component parameters for the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. The model is based on our previously proposed SMILES-to-Properties-Transformer (SPT). By incorporating PC-SAFT into the neural network architecture, the machine learning model is trained directly on experimental vapor pressure and liquid density data. Combining established physical modeling approaches with state-of-the-art machine learning methods enables high-accuracy predictions across a wide range of pressures and temperatures, while maintaining the physical meaning of PC-SAFT parameters. SPT-PCSAFT demonstrates exceptional prediction accuracy even for complex molecules with various functional groups, outperforming traditional group contribution methods by a factor of four in the mean average percentage deviation. Moreover, SPT-PCSAFT captures the behavior of stereoisomers without any special consideration. To facilitate the application of our model, we provide predicted PC-SAFT parameters of more than 13645 components, making PC-SAFT accessible to all researchers. Bottleneck ; Component (thermodynamics) ; Artificial intelligence ; Artificial neural network ; Computer science ; Machine learning ; Equation of state ; Extreme learning machine ; Statistical physics ; Thermodynamics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Susanne Linn Skjærvø | Andy S. Anker | Magnus Christian Wied | Emil T. S. Kjær | Mikkel Juelsholt | Troels Lindahl Christiansen | Kirsten M. Ø. Jensen
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Atomic structural changes in the formation of transition metal tungstates: the role of polyoxometalate structures in material crystallization Material nucleation processes are poorly understood; nevertheless, an atomistic understanding of material formation would aid in the design of material synthesis methods. Here, we apply <i>in situ</i> X-ray total scattering experiments with pair distribution function (PDF) analysis to study the hydrothermal synthesis of wolframite-type MWO<sub>4</sub> (M : Mn, Fe, Co, Ni). The data obtained allow the mapping of the material formation pathway in detail. We first show that upon mixing of the aqueous precursors, a crystalline precursor containing [W<sub>8</sub>O<sub>27</sub>]<sup>6-</sup> clusters forms for the MnWO<sub>4</sub> synthesis, while amorphous pastes form for the FeWO<sub>4</sub>, CoWO<sub>4</sub> and NiWO<sub>4</sub> syntheses. The structure of the amorphous precursors was studied in detail with PDF analysis. Using database structure mining and an automated modelling strategy by applying machine learning, we show that the amorphous precursor structure can be described through polyoxometalate chemistry. A skewed sandwich cluster containing Keggin fragments describes the PDF of the precursor structure well, and the analysis shows that the precursor for FeWO<sub>4</sub> is more ordered than that of CoWO<sub>4</sub> and NiWO<sub>4</sub>. Upon heating, the crystalline MnWO<sub>4</sub> precursor quickly converts directly to crystalline MnWO<sub>4</sub>, while the amorphous precursors transform into a disordered intermediate phase before the crystalline tungstates appear. Our data show that the more disordered the precursor is, the longer the reaction time required to form crystalline products, and disorder in the precursor phase appears to be a barrier for crystallization. More generally, we see that polyoxometalate chemistry is useful when describing the initial wet-chemical formation of mixed metal oxides. Polyoxometalate ; Crystallization ; Transition metal ; Materials science ; Crystallography ; Inorganic chemistry ; Chemical engineering ; Chemical physics ; Chemistry ; Nanotechnology ; Catalysis ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Mona Amrihesari | Amari Murry | Blair Brettmann
Source: Polymer (article, 2023-04-30)
Field: Materials Science — Materials Chemistry

Towards standardized polymer solubility measurements using a parallel crystallizer Recently developed machine learning approaches to understanding material properties provide new opportunities for studying and developing new knowledge about polymers. The vast application of polymers and their sensitivity to many parameters like molecular weight, temperature, morphology, etc. requires developing high throughput experimentation methods that lead to high quality data for machine learning predictions. Although polymer informatics has significantly improved in recent years using artificial intelligence and machine learning techniques, a significant hurdle in the polymer informatics area is the lack of complete and sufficiently detailed databases. One example where this is a challenge is in developing a dataset for polymer solubility and dissolution, where the classification of “soluble” or “insoluble” is sensitive to molecular weight, concentration, temperature, heating/cooling rate, and other factors that are not always reported or well-controlled. To overcome the challenges with the current dataset on polymer solubility, in this work, we will focus on applying turbidity measurements with precise temperature control using a Crystal16 parallel crystallizer. With this method, the experiments are possible to do in reproducible and time-effective ways with minimal human error while also collecting time-resolved dissolution data continuously throughout the experiment. In this work, we analyze the effect of experimental parameters on measured turbidity for representative polymer/solvent pairs and identify key parameters to control for producing accurate and high quality data for different types of polymers. This approach can lead to detailed results in a controllable manner and will allow researchers to develop information-rich data sets for predicting the polymer solubility using machine learning techniques. Polymer ; Solubility ; Computer science ; Dissolution ; Process engineering ; Work (physics) ; Turbidity ; Materials informatics ; Machine learning ; Materials science ; Artificial intelligence ; Chemistry ; Mechanical engineering ; Organic chemistry ; Engineering ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography
Authors: Henrik Schopmans | Patrick Reiser | Pascal Friederich
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms We used synthetically generated crystals to train ResNet-like models to enhance the prediction of space groups from ICSD powder X-ray diffractograms. The results show improved generalization to unseen structure types compared to previous approaches. Generalization ; X-ray ; Artificial neural network ; Materials science ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Crystallography ; Biological system ; Optics ; Mathematics ; Chemistry ; Physics ; Mathematical analysis ; Biology X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Thorren Kirschbaum | Börries von Seggern | Joachim Dzubiella | Annika Bande | Frank Noé
Source: Journal of Chemical Theory and Computation (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

Machine Learning Frontier Orbital Energies of Nanodiamonds Nanodiamonds have a wide range of applications including catalysis, sensing, tribology, and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new data set ND5k, consisting of 5089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. From this data set we derive a qualitative design suggestion for nanodiamonds in photocatalysis. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find the best performance using the equivariant message passing neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here. Density functional theory ; Computer science ; Leverage (statistics) ; Scalability ; Artificial neural network ; Extrapolation ; Test set ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques
Authors: Juejing Liu | Xiaodong Zhao | Ke Zhao | Vitaliy G. Goncharov | Jérôme Delhommelle | Jian Ping Lin | Xiaofeng Guo
Source: Scientific Reports (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium-aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion. Resonant ultrasound spectroscopy ; Fingerprint (computing) ; Ultrasound ; Elastic modulus ; Computer science ; Moduli ; Spectroscopy ; Acoustics ; Materials science ; Biomedical engineering ; Artificial intelligence ; Composite material ; Physics ; Medicine Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; Non-Destructive Testing Techniques
Authors: Tatsuya Sagawa | Ryosuke Kojima
Source: arXiv (Cornell University) (preprint, 2023-11-12)
Field: Materials Science — Materials Chemistry

ReactionT5: a large-scale pre-trained model towards application of limited reaction data Transformer-based deep neural networks have revolutionized the field of molecular-related prediction tasks by treating molecules as symbolic sequences. These models have been successfully applied in various organic chemical applications by pretraining them with extensive compound libraries and subsequently fine-tuning them with smaller in-house datasets for specific tasks. However, many conventional methods primarily focus on single molecules, with limited exploration of pretraining for reactions involving multiple molecules. In this paper, we propose ReactionT5, a novel model that leverages pretraining on the Open Reaction Database (ORD), a publicly available large-scale resource. We further fine-tune this model for yield prediction and product prediction tasks, demonstrating its impressive performance even with limited fine-tuning data compared to traditional models. The pre-trained ReactionT5 model is publicly accessible on the Hugging Face platform. Computer science ; Artificial intelligence ; Scale (ratio) ; Machine learning ; Transformer ; Field (mathematics) ; Resource (disambiguation) ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Peter Bjørn Jørgensen | Jonas Busk | Ole Winther | Mikkel N. Schmidt
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Coherent energy and force uncertainty in deep learning force fields In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quantify aleatoric uncertainty in the predicted energies, a widely used modeling approach involves predicting both a mean and variance for each energy value. However, this model is not differentiable under the usual white noise assumption, so energy uncertainty does not naturally translate to force uncertainty. In this work we propose a machine learning potential energy model in which energy and force aleatoric uncertainty are linked through a spatially correlated noise process. We demonstrate our approach on an equivariant messages passing neural network potential trained on energies and forces on two out-of-equilibrium molecular datasets. Furthermore, we also show how to obtain epistemic uncertainties in this setting based on a Bayesian interpretation of deep ensemble models. Uncertainty quantification ; Energy (signal processing) ; Potential energy ; Statistical physics ; Computer science ; Work (physics) ; Artificial intelligence ; Noise (video) ; Artificial neural network ; Function (biology) ; Physics ; Machine learning ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Krzysztof Maziarz | Austin Tripp | Guoqing Liu | Megan Stanley | Shufang Xie | Piotr Gaiński | Philipp Seidl | Marwin Segler
Source: arXiv (Cornell University) (preprint, 2023-10-30)
Field: Materials Science — Materials Chemistry

Re-evaluating Retrosynthesis Algorithms with Syntheseus Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques, and unnecessarily hamper progress. To remedy this, we present a synthesis planning library with an extensive benchmarking framework, called syntheseus, which promotes best practice by default, enabling consistent meaningful evaluation of single-step models and multi-step planning algorithms. We demonstrate the capabilities of syntheseus by re-evaluating several previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes in controlled evaluation experiments. We end with guidance for future works in this area, and call the community to engage in the discussion on how to improve benchmarks for synthesis planning. Retrosynthetic analysis ; Benchmarking ; Ranking (information retrieval) ; Imperfect ; Computer science ; Focus (optics) ; Best practice ; Artificial intelligence ; Machine learning ; Algorithm ; Political science ; Linguistics ; Management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions