jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paulo Neves | Kelly McClure | Jonas Verhoeven | Natalia Dyubankova | Ramil Nugmanov | Andrey Gedich | Sairam Menon | Jörg K. Wegner
Source: ChemRxiv (preprint, 2022-10-05)
Field: Materials Science — Materials Chemistry

Global Reactivity Models are Impactful in Industrial Synthesis Applications Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks with enormous unrealized potential. One such task is the reaction yield prediction. Every year more than one fifth of all synthesis attempts result in product yields which are either zero or too low. This equates to chemical and human resources being spent on activities which ultimately do not progress the programs, leading to a triple loss when accounting for the cost of opportunity in time wasted. In this work we pre-train a BERT model on more than 16 million reactions from 4 different data sources, and fine tune it to achieve an uncertainty calibrated global yield prediction model. This model is an improvement upon state of the art not just from the increase in pre-train data but also by introducing a new embedding layer which solves a few limitations of SMILES and enables integration of additional information such as equivalents and molecule role into the reaction encoding, the model is called BERT Enriched Embedding (BEE). The model is benchmarked on an open-source dataset against a published synthesis focused BERT method showing a 20-point improvement in r2 score. The model is fine-tuned and tested on an internal company data benchmark, and a prospective study shows that the application of the model can reduce the total number of negative reactions (yield under 5%) ran in Janssen by at least 34%. Lastly, we corroborate the previous results through experimental validation, by directly deploying the model in an on-going drug discovery project and showing that it can also be used as a reagent recommender due to its fast inference speed and reliable confidence estimation. Benchmark (surveying) ; Computer science ; Task (project management) ; Embedding ; Yield (engineering) ; Artificial intelligence ; Deep learning ; Encoding (memory) ; Machine learning ; Industrial engineering ; Data science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kai Zhu | Xin Xu | Bing Yan
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Eu(Iii) Functionalized Hofs Based on Machine-Learning-Assisted Fluorescence Sensing: Discrimination of Quinolones Via Pca and Bpnn Model Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning Lanthanide and Transition Metal Complexes ; Molecular Sensors and Ion Detection ; Radioactive element chemistry and processing
Authors: Surajit Nandi | Tejs Vegge | Arghya Bhowmik
Source: ChemRxiv (preprint, 2022-09-08)
Field: Materials Science — Materials Chemistry

Large dataset of molecular and reaction energies from multi-level quantum chemical methods Well curated extensive datasets have have helped spur intense molecular machine learning(ML) method development activities over last few years encouraging non-chemists to be part of the effort as well. QM9 dataset is the one of the benchmark database for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a widely variety of ML tasks with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A <-> B molecular conversions within the QM9 dataset and provide the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure and bond based reaction energy prediction tools based on ML. Benchmark (surveying) ; Molecule ; Computer science ; Quantum chemical ; Basis (linear algebra) ; Energy (signal processing) ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Viviana Dovale-Farelo | Pedram Tavadze | Logan Lang | A. Bautista Hernández | A. Romero
Source: Research Square (preprint, 2022-09-27)
Field: Materials Science — Materials Chemistry

The Hardness Calculator Abstract This investigation has studied the six most-used macroscopic models to compute hardness from the elastic constants, using an experimental database of 143 materials. "The Hardness Calculator" is proposed as a solution to estimate hardness in an easy, fast and confident manner. This study divides into two stages. The first approach, referred to as "The Classic Calculator", is a selection model based on simple properties of a solid such as crystal system, bandgap, and density. The second phase is machine learning (ML) based, and it is referred to as "The Machine Learning Calculator". We used two different methods to compute hardness in the ML approach. The first ML method is a classifier that targets the best model to calculate hardness using the mechanical properties of a solid (bulk modulus, shear modulus, Young's modulus, and Poisson's ratio) as input variables. The second ML method is a regressor that directly predicts the value of hardness using the same input variables as the classifier. Using the Materials Project's database, the classic and ML schemes were compared and tested to predict new hard and superhard materials. All methods are available in a free-access online application for users to discriminate between the different available results. Calculator ; Shear modulus ; Modulus ; Materials science ; Indentation hardness ; Computer science ; Young's modulus ; Elastic modulus ; Algorithm ; Composite material ; Microstructure Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Boron and Carbon Nanomaterials Research
Authors: Yeling Zhang | Huancong Shi | Zhi Chen | Dongfang Wu | Linhua Jiang
Source:  (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

An Innovative Multilevel Line Graph Attention Network for Predicting Molecular Properties Predicting molecular properties is a fundamental task of quantum chemistry and a prerequisite for subsequent research, such as compound discovery and drug design. Recent studies show that, graph neural networks are more effective for this task. By analyzing the composition of molecular energy, we designed a multilevel line graph method for graph data generation, which was experimentally verified to be more capable of representing molecules. Furthermore, we designed an innovative multi-task graph neural network based on graph attention network to predict molecular properties by learning the features embedded in multilevel line graph. Experiments show that our method has higher accuracy in predicting energy properties. Specifically, our method improves performance when trained with fewer samples, which has a great significance for practical applications with sparse samples. Computer science ; Graph ; Artificial neural network ; Graph theory ; Theoretical computer science ; Artificial intelligence ; Quantum chemical ; Power graph analysis ; Machine learning ; Data mining ; Molecule ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Pavlo Bilous | Adriana Pálffy | Florian Marquardt
Source: arXiv (Cornell University) (preprint, 2022-09-13)
Field: Materials Science — Materials Chemistry

Deep-learning approach for the atomic configuration interaction problem on large basis sets High-precision atomic structure calculations require accurate modelling of electronic correlations typically addressed via the configuration interaction (CI) problem on a multiconfiguration wave function expansion. The latter can easily become challenging or infeasibly large even for advanced supercomputers. Here we develop a deep-learning approach which allows to preselect the most relevant configurations out of large CI basis sets until the targeted energy precision is achieved. The large CI computation is thereby replaced by a series of smaller ones performed on an iteratively expanding basis subset managed by a neural network. While dense architectures as used in quantum chemistry fail, we show that a convolutional neural network naturally accounts for the physical structure of the basis set and allows for robust and accurate CI calculations. The method was benchmarked on basis sets of moderate size allowing for the direct CI calculation, and further demonstrated on prohibitively large sets where the direct computation is not possible. Basis (linear algebra) ; Computation ; Computer science ; Basis function ; Basis set ; Set (abstract data type) ; Convolutional neural network ; Deep learning ; Wave function ; Function (biology) ; Series (stratigraphy) ; Artificial neural network ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Abhishek Agarwal | Pradeep Rathore | Dharmendr Kumar | Harsha Nistala | S Sanjana | Vinay Jain | Beena Rai
Source:  (article, 2022-03-06)
Field: Materials Science — Materials Chemistry

Multitask Learning Framework for Screening of Corrosion Inhibitors for Mild Steel ABSTRACT Corrosion inhibitors are useful to mitigate corrosion of metal/alloy components. However, traditional corrosion inhibitors are toxic and need to be replaced by greener alternatives. Efficient screening models are required to find molecules with desired properties from millions of molecules available in public domain. To make these models, database of experimental inhibition efficiency of molecules is essential. In this work, we have developed a computational framework to accelerate the discovery of new corrosion inhibitors. We have used machine learning based algorithms to predict corrosion inhibition efficiency of organic molecules for steel in hydrochloric acid by using structural information of the molecules along with experimental conditions. Our multitask learning based neural network architecture was able to outperform traditional machine learning algorithms such as random forest, lasso and ridge regression. We have also created the largest dataset for predictive modelling of corrosion inhibitors for steel. Besides, we have also used the model to screen molecules from the ZINC15 dataset and found potential inhibitors with high inhibition efficiency. Corrosion ; Computer science ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Atsuto Seko
Source: Institutional Repositories DataBase (IRDB) (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

第一原理計算と機械学習による原子間相互作用のモデリングと構造探索 This paper demonstrates machine learning potential, providing an accurate description of the relationship between the energy and the crystal structure and its potential applications such as large-scale molecular dynamics simulations and global structure optimizations. This paper also describes machine-learning-based methods to perform crystal structure optimization from a vast number of candidate structures. Finally, the paper introduces a novel compact data structure of the zero-suppressed binary decision diagram (ZDD) to enumerate structures much more efficiently. Binary decision diagram ; Crystal structure prediction ; Data structure ; Binary number ; Diagram ; Energy (signal processing) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Physical and Chemical Molecular Interactions
Authors: Kamal Choudhary | Ramya Gurunathan | Brian DeCost | Adam J. Biacchi
Source: arXiv (Cornell University) (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

AtomVision: A machine vision library for atomistic images Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate, curate scanning tunneling microscopy (STM) and scanning transmission electron microscopy (STEM) datasets and apply machine learning techniques. To demonstrate the applicability of this library, we 1) generate and curate an atomistic image dataset of about 10000 materials, 2) develop and compare convolutional and graph neural network models to classify the Bravais lattices, 3) develop fully convolutional neural network using U-Net architecture to pixelwise classify atom vs background, 4) use generative adversarial network for super-resolution, 5) curate a natural language processing based image dataset using open-access arXiv dataset, and 6) integrate the computational framework with experimental microscopy tools. AtomVision library is available at https://github.com/usnistgov/atomvision. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Deep learning ; Machine learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Hai‐Chen Wang | Jonathan Schmidt | Miguel A. L. Marques | Ludger Wirtz | A. Romero
Source: arXiv (Cornell University) (preprint, 2022-12-07)
Field: Materials Science — Materials Chemistry

Symmetry-based computational search for novel binary and ternary 2D materials We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form A$_n$B$_m$ or A$_n$B$_m$C$_k$. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250~meV/atom above the convex hull of thermodynamic stability. Ternary operation ; Periodic table ; Convex hull ; Space group ; Binary number ; Group (periodic table) ; Space (punctuation) ; Crystal structure prediction ; Regular polygon ; Combinatorics ; Materials science ; Mathematics ; Physics ; Computer science ; Geometry ; Crystallography ; Chemistry ; Quantum mechanics ; Crystal structure ; Arithmetic Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Aodaohu | Gegentuya
Source: Journal of Molecular Structure (article, 2022-09-02)
Field: Chemistry — Inorganic Chemistry

Biological activity of Cu(II)-coordination polymer in functional dyspepsia and the design of novel pharmaceutical structure using supervised learning technic Chemistry ; Molecule ; Ghrelin ; Leptin ; Polymer ; Biphenyl ; Combinatorial chemistry ; Biochemistry ; Organic chemistry ; Receptor ; Internal medicine Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods ; MicroRNA in disease regulation
Authors: Takeru Miyagawa | Yugo Sakai | Akio Yonezu | Kazuki Mori | Nobuhiko Kato | Keiji Ishibashi
Source:  (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Investigation of Mechanical Properties of Combinatorial Ti-Cu Film Using MD Simulation With Neural Network Potential Abstract Combinatorial approach is a prominent method to synthesize samples with atomic composition gradients, which enables the high-throughput discovery of new materials. Titanium-copper (Ti-Cu) alloy is widely used in electronic devices because of its excellent mechanical properties such as stress relaxation resistance, bond formality, and workability. By synthesizing Ti-Cu thin film with combinatorial approach, the mechanical property may be improved, leading to a new application. Molecular Dynamics (MD) simulation is a powerful tool to predict mechanical property, but it requires interatomic potentials to depict the movements of atoms. Because of the complex structures of Ti-Cu thin film synthesized by combinatorial approach, the creation of interatomic potentials is a difficult and time-consuming process. Therefore, in this study, a neural network (NN) based method to create interatomic potentials is developed, which are referred to as neural network potentials (NNPs). It is found that NNP can accurately reproduce the energies and forces calculated by Ab initio molecular dynamics (AIMD) simulations. Finally, using MD simulations with developed NNP, the mechanism of mechanical properties is investigated from the perspective of atomic scales. Molecular dynamics ; Interatomic potential ; Materials science ; Artificial neural network ; Relaxation (psychology) ; Titanium ; Embedded atom model ; Chemical physics ; Topology (electrical circuits) ; Computer science ; Statistical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Machine learning ; Physics ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Pedro Baptista de Castro | Kensei Terashima | Miren Garbiñe Esparza Echevarría | Hiroyuki Takeya | Yoshihiko Takano
Source: Advanced Theory and Simulations (preprint, 2022-02-16)
Field: Materials Science — Materials Chemistry

XERUS: An Open‐Source Tool for Quick XRD Phase Identification and Refinement Automation Abstract Analysis of X‐ray diffraction patterns is one of the keystones of materials science and materials research. With the advancement of data‐driven methods for materials design, candidate materials can be quickly screened for the study of a desired physical property. Efficient methods to automatically analyze and identify phases present in a given pattern are paramount for the success of this new paradigm. To aid this process, the open‐source python package Xray Estimation and Refinement Using Similarity (XERUS) for semi‐automatic/automatic phase identification is presented. XERUS takes advantage of open crystal structure databases, not relying on proprietary databases, to obtain crystal structures on the fly, being then chemical space agnostic. By wrapping around GSASII scriptable, it can automatically simulate patterns and calculate similarity measures used for phase identification. This approach is simple and quick but also applicable to multiphase identification, by coupling the similarity calculations with quick refinements followed by an iterative peak removal process. XERUS is shown in action in four different experimental datasets, and also it is benchmarked against a recently proposed deep learning method for a mixture dataset covering the Li‐Mn‐O‐F chemical space. Python (programming language) ; Computer science ; On the fly ; Identification (biology) ; Similarity (geometry) ; Process (computing) ; Automation ; Open source ; Data mining ; Iterative refinement ; Software ; Algorithm ; Artificial intelligence ; Mechanical engineering ; Programming language ; Engineering X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Michelle L. Gill
Source: Magnetic Resonance in Chemistry (article, 2022-08-17)
Field: Materials Science — Materials Chemistry

The rise of the machines in chemistry The use of artificial intelligence and, more specifically, deep learning methods in chemistry is becoming increasingly common. Applications in informatics fields, such as cheminformatics and proteomics, structural biology, and spectroscopy, including NMR, are on the rise. Recent developments in model architectures, such as graph convolutional neural networks and transformers, have been enabled by advancements in computational hardware and software. However, model architectures with more predictive power often require larger amounts of training data, which can be challenging to acquire, but this requirement can be mitigated through techniques like pretraining and fine-tuning. In spite of these successes, challenges remain, such as normalization and scaling of data, availability of experimentally acquired data, and model explainability. Cheminformatics ; Chemistry ; Artificial intelligence ; Data science ; Normalization (sociology) ; Software ; Informatics ; Machine learning ; Computer science ; Nanotechnology ; Computational chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Max Pinheiro | Shuang Zhang | Pavlo O. Dral | Mario Barbatti
Source: ChemRxiv (preprint, 2022-09-18)
Field: Materials Science — Materials Chemistry

WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets Multidimensional surfaces of quantum chemical properties such as potential energies and dipole moments are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18~million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our data sets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing data sets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models. Dipole ; Interpolation (computer graphics) ; Curse of dimensionality ; Sampling (signal processing) ; Statistical physics ; Quantum ; Electronic structure ; Dimensionality reduction ; Distribution (mathematics) ; Computer science ; Space (punctuation) ; Physics ; Database ; Mathematics ; Quantum mechanics ; Classical mechanics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry