jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Colton C. Seegmiller | Sterling G. Baird | Hasan M. Sayeed | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-06-27)
Field: Materials Science — Materials Chemistry

Discovering Chemically Novel, High-TemperatureSuperconductors One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions [1]. This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process. Superconductivity ; Cuprate ; Curse of dimensionality ; Cluster analysis ; High-temperature superconductivity ; Set (abstract data type) ; Computer science ; Materials science ; Physics ; Machine learning ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Matteo Zanda
Source: Synfacts (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

SYNFORM ISSUE 2023/06 Artificial intelligence (AI) and machine learning (ML)which in my understanding (I am far from being a specialist in the field) is an application or subset of AI -are progressively extending their influence on the realm of chemistry, including that of organic synthes is.Some even think that AI will -at some point in a nottoo-far future -replace chemists entirely, not just in the execution of organic reactions but also in their design and planning.I will leave these predictionsand related analysis on whether this is good or bad, or simply inevitable -to others who have more talent in terms of crystal ball gazing, but certainly the impact of AI and ML in the organic chemistry literature is becoming increasingly evident.One such example is featured in the closing article of this issue of SYNFORM, where I. Larrosa and co-authors (UK) in a recent Nature article took advantage of the opportunities offered by an ML approach to analyze a set of kinetic data to uncover the underlying reaction mechanisms, which ultimately resulted in a better understanding -and optimizationof complex organic processes.This is a very exciting development -which perhaps owes something to certain chemometric methods that were trending a few de cades ago -because it is not difficult to conceive a future where similar ML or AI methods will be routine for analysing organic reactions before even trying any lab experiment.Although this piece of research is extremely visionary and exciting, there is more great science in this June issue of SYNFORM.Indeed, the first article covers a new application of photochemical catal ysis, which makes use of pyridinium salts for achiev ing the enantioselective alkylation of enolates and enamines, as reported by U. Tambar (USA).The second article features a recent and conceptually ground-breaking Science paper by M. Tobisu (Japan) Chemistry ; Earth science ; Astrobiology ; Environmental chemistry ; Geology Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Asymmetric Hydrogenation and Catalysis
Authors: Samantha Durdy | Michael W. Gaultois | Vladimir V. Gusev | Danushka Bollegala | Matthew J. Rosseinsky
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Materials Science — Materials Chemistry

Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context Clustering is a common task in machine learning, but clusters of unlabelled data can be hard to quantify. The application of clustering algorithms in chemistry is often dependant on material representation. Ascertaining the effects of different representations, clustering algorithms, or data transformations on the resulting clusters is difficult due to the dimensionality of these data. We present a thorough analysis of measures for isotropy of a cluster, including a novel implantation based on an existing derivation. Using fractional anisotropy, a common method used in medical imaging for comparison, we then expand these measures to examine the average isotropy of a set of clusters. A use case for such measures is demonstrated by quantifying the effects of kernel approximation functions on different representations of the Inorganic Crystal Structure Database. Broader applicability of these methods is demonstrated in analysing learnt embedding of the MNIST dataset. Random clusters are explored to examine the differences between isotropy measures presented, and to see how each method scales with the dimensionality. Python implementations of these measures are provided for use by the community. Cluster analysis ; MNIST database ; Curse of dimensionality ; Computer science ; Python (programming language) ; Isotropy ; Embedding ; Data mining ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Pattern recognition (psychology) ; Physics ; Artificial neural network X-ray Diffraction in Crystallography ; Machine Learning in Bioinformatics ; Gene expression and cancer classification
Authors: Matteo Zanda
Source: Synthesis (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

SYNFORM ISSUE 2023/6 Artificial intelligence (AI) and machine learning (ML)which in my understanding (I am far from being a specialist in the field) is an application or subset of AI -are progressively extending their influence on the realm of chemistry, including that of organic synthes is.Some even think that AI will -at some point in a nottoo-far future -replace chemists entirely, not just in the execution of organic reactions but also in their design and planning.I will leave these predictionsand related analysis on whether this is good or bad, or simply inevitable -to others who have more talent in terms of crystal ball gazing, but certainly the impact of AI and ML in the organic chemistry literature is becoming increasingly evident.One such example is featured in the closing article of this issue of SYNFORM, where I. Larrosa and co-authors (UK) in a recent Nature article took advantage of the opportunities offered by an ML approach to analyze a set of kinetic data to uncover the underlying reaction mechanisms, which ultimately resulted in a better understanding -and optimizationof complex organic processes.This is a very exciting development -which perhaps owes something to certain chemometric methods that were trending a few de cades ago -because it is not difficult to conceive a future where similar ML or AI methods will be routine for analysing organic reactions before even trying any lab experiment.Although this piece of research is extremely visionary and exciting, there is more great science in this June issue of SYNFORM.Indeed, the first article covers a new application of photochemical catal ysis, which makes use of pyridinium salts for achiev ing the enantioselective alkylation of enolates and enamines, as reported by U. Tambar (USA).The second article features a recent and conceptually ground-breaking Science paper by M. Tobisu (Japan) Chemistry ; Earth science ; Environmental chemistry ; Geology Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Asymmetric Hydrogenation and Catalysis
Authors: Xiang Li | Jia-Cheng Huang | Guang-Ze Zhang | Hao-En Li | Changsu Cao | Dingshun Lv | Han‐Shi Hu
Source: arXiv (Cornell University) (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

A Non-stochastic Optimization Algorithm for Neural-network Quantum States Neural-network quantum states (NQS) employ artificial neural networks to encode many-body wave functions in second quantization through variational Monte Carlo (VMC). They have recently been applied to accurately describe electronic wave functions of molecules and have shown the challenges in efficiency comparing with traditional quantum chemistry methods. Here we introduce a general non-stochastic optimization algorithm for NQS in chemical systems, which deterministically generates a selected set of important configurations simultaneously with energy evaluation of NQS. This method bypasses the need for Markov-chain Monte Carlo within the VMC framework, thereby accelerating the entire optimization process. Furthermore, this newly-developed non-stochastic optimization algorithm for NQS offers comparable or superior accuracy compared to its stochastic counterpart and ensures more stable convergence. The application of this model to test molecules exhibiting strong electron correlations provides further insight into the performance of NQS in chemical systems and opens avenues for future enhancements. Artificial neural network ; Computer science ; Stochastic neural network ; Quantum ; Convergence (economics) ; Quantum Monte Carlo ; Monte Carlo method ; Quantization (signal processing) ; Stochastic optimization ; Variational Monte Carlo ; Algorithm ; Mathematical optimization ; Recurrent neural network ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Yasemin Bayğu | İnci Çapan | Rıfat Çapan | matem Erdogan | Yaser Açıkbaş | Nilgün Kabay | Yaşar Gök | kemal buyukkabasakal
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Sensor Application and Mathematical Modeling of New Zn(Ii) Phthalocyanine Containing 26-Membered Tetraoxadithia Macrocycle Moieties Phthalocyanine ; Chemistry ; Crystallography ; Stereochemistry ; Materials science ; Organic chemistry Porphyrin and Phthalocyanine Chemistry
Authors: D. S. Dakshina | P. Jayapriya | R. Kala
Source: International Journal of Data Science and Artificial Intelligence (article, 2023-09-29)
Field: Materials Science — Polymers and Plastics

SAREE TEXTURE ANALYSIS AND CLSSIFICATION VIA DEEP LEARNING FRAMEWORK Indian women have worn sarees for centuries and have a significant market share in their clothing. Despite the ability to identify saree materials with tactile texture and the possibility to purchase online, it may not always be practicable to touch and feel a saree being worn. Moreover, the wide variety of saree materials available in comparison to other types of clothing, as well as the lack of experience in recognizing them, make this challenge even more challenging. The rapid advancement of smartphone technology makes it possible to capture images of cloth to determine the exact material of a saree and make an online purchase. To overcome the aforementioned challenges, a novel deep learning-based saree texture classification framework has been proposed for the rapid classification of saree tactile textures. Deep learning-based segmentation is used to detect visual texture and identify sarees from collected images. For better understanding the introduction of Mask Region-based Convolutional Neural Network (Mask RCNN) was used for generating the saree patches. To categorize saree textures, we propose a deep learning-based VGG-16 network. According to the findings, this pipeline performs better than the current deep learning methods and achieves 97.41% accuracy. Deep learning ; Convolutional neural network ; Pipeline (software) ; Categorization ; Texture (cosmology) ; Segmentation ; Deep neural networks Textile materials and evaluations ; Generative Adversarial Networks and Image Synthesis ; Face recognition and analysis
Authors: Zhantao Chen | Peng Cheng | Alexander N. Petsch | Sathya R. Chitturi | Alana Okullo | Sugata Chowdhury | Chun Hong Yoon | Joshua J. Turner
Source: arXiv (Cornell University) (preprint, 2023-06-03)
Field: Materials Science — Materials Chemistry

Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries. Computer science ; Artificial neural network ; Spin (aerodynamics) ; Bayesian probability ; Experimental data ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced MRI Techniques and Applications
Authors: Kausik Hira | Mohd Zaki | Dhruvil Sheth | Mausam Mausam | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure, and properties, which further depend on its processing and testing conditions. Recent developments in deep learning and natural language processing have enabled information extraction at scale from published literature such as peer-reviewed publications, books, and patents. However, this information is spread in multiple formats, such as tables, text, and images, and with little or no uniformity in reporting style giving rise to several machine learning challenges. Here, we discuss, quantify, and document these challenges in automated information extraction (IE) from materials science literature towards the creation of a large materials science knowledge base. Specifically, we focus on IE from text and tables and outline several challenges with examples. We hope the present work inspires researchers to address the challenges in a coherent fashion, providing a fillip to IE towards developing a materials knowledge base. Data science ; Computer science ; Information extraction ; Focus (optics) ; Knowledge base ; Function (biology) ; Knowledge extraction ; Scale (ratio) ; Information retrieval ; Earth science ; Artificial intelligence ; Geography ; Geology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Zeyuan Tang | Stefan T. Bromley | Bjørk Hammer
Source: arXiv (Cornell University) (preprint, 2023-05-25)
Field: Chemistry — Spectroscopy

A machine learning potential for simulating infrared spectra of nanosilicate clusters The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to collect training data for constructing a neural network based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics (MD) simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium (ISM) and in circumstellar environments. Anharmonicity ; Computer science ; Artificial neural network ; Sampling (signal processing) ; Molecular dynamics ; Infrared ; Training set ; Interatomic potential ; Set (abstract data type) ; Data set ; Statistical physics ; Spectral line ; Data point ; Computational physics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Optics ; Computer vision ; Quantum mechanics Spectroscopy and Laser Applications ; Geochemistry and Geologic Mapping ; Species Distribution and Climate Change
Authors: Elton Ogoshi | Henrique Bunselmeyer Ferreira | J. N. B. Rodrigues | Gustavo M. Dalpian
Source: arXiv (Cornell University) (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

Exploring chemical compound space with a graph-based recommender system With the availability of extensive databases of inorganic materials, data-driven approaches leveraging machine learning have gained prominence in materials science research. In this study, we propose an innovative adaptation of data-driven concepts to the mapping and exploration of chemical compound space. Recommender systems, widely utilized for suggesting items to users, employ techniques such as collaborative filtering, which rely on bipartite graphs composed of users, items, and their interactions. Building upon the Open Quantum Materials Database (OQMD), we constructed a bipartite graph where elements from the periodic table and sites within crystal structures are treated as separate entities. The relationships between them, defined by the presence of ions at specific sites and weighted according to the thermodynamic stability of the respective compounds, allowed us to generate an embedding space that contains vector representations for each ion and each site. Through the correlation of ion-site occupancy with their respective distances within the embedding space, we explored new ion-site occupancies, facilitating the discovery of novel stable compounds. Moreover, the graph's embedding space enabled a comprehensive examination of chemical similarities among elements, and a detailed analysis of local geometries of sites. To demonstrate the effectiveness and robustness of our method, we conducted a historical evaluation using different versions of the OQMD and recommended new compounds with Kagome lattices, showcasing the applicability of our approach to practical materials design. Bipartite graph ; Embedding ; Computer science ; Chemical space ; Recommender system ; Occupancy ; Graph ; Theoretical computer science ; Robustness (evolution) ; Space (punctuation) ; Information retrieval ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Complex Network Analysis Techniques ; History and advancements in chemistry
Authors: Krittapong Deshsorna | Luckhana Lawtrakul | Pawin Iamprasertkun
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

How False Data Affects Machine Learning Models in Electrochemistry? Recently, the selection of machine learning model based on only the data distribution without concerning the noise of the data. This study aims to distinguish, which models perform well under noisy data, and establish whether stacking machine learning models actually provide robustness to otherwise weak-to-noise models. The electrochemical data were tested with 12 standalone models and stacking model. This includes XGB, LGBM, RF, GB, ADA, NN, ELAS, LASS, RIDGE, SVM, KNN, DT, and the stacking model. It is found that linear models handle noise well with the average error of (slope) to 1.75 F g-1 up to error per 100% percent noise added; but it suffers from prediction accuracy due to having an average of 60.19 F g-1 estimated at minimal error at 0% noise added. Tree-based models fail in terms of noise handling (average slope is 55.24 F g-1 at 100% percent noise), but it can provide higher prediction accuracy (lowest error of 23.9 F g-1) than that of linear. To address the controversial between prediction accuracy and error handling, the stacking model was constructed, which is not only show high accuracy (intercept of 25.03 F g-1), but it also exhibits good noise handling (slope of 43.58 F g-1), making stacking models a relatively low risk and viable choice for beginner and experienced machine learning research in electrochemistry. Even though neural networks (NN) are gaining popularity in the electrochemistry field. However, this study presents that NN is not suitable for electrochemical data, and improper tuning resulting in a model that is susceptible to noise. Thus, STACK models should provide better benefits in that even with untuned base models, they can achieve an accurate and noise-tolerant model. Overall, this work provides insight into machine learning model selection for electrochemical data, which should aid the understanding of data science in chemistry context. Robustness (evolution) ; Stacking ; Noise (video) ; Computer science ; Machine learning ; Artificial intelligence ; Support vector machine ; Artificial neural network ; Algorithm Machine Learning in Materials Science ; Neural Networks and Applications