jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alessandra Toniato | Philippe Schwaller | Antonio Cardinale | Joppe Geluykens | Teodoro Laino
Source: ChemRxiv (preprint, 2020-06-01)
Field: Materials Science — Materials Chemistry

Unassisted Noise-Reduction of Chemical Reactions Data Sets Existing deep learning models applied to reaction prediction in organic chemistry are able to reach extremely high levels of accuracy (> 90% for NLP- based ones1). With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve the performance of artificial intelligence models in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries (noise) from chemical reaction collections. Results show that models trained on cleaned and balanced data sets improve the quality of the predictions without a decrease in performance. For the retrosynthetic models the round-trip accuracy is enhanced by 13% and the value of the cumulative Jensen Shannon metric is lowered down to 70% of its original value, while maintaining high values of coverage (97%) and constant class-diversity (1.6) at inference. Metric (unit) ; Computer science ; Inference ; Reduction (mathematics) ; Artificial intelligence ; Noise (video) ; Machine learning ; Noise reduction ; Quality (philosophy) ; Value (mathematics) ; Data mining ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Michael Fortunato | Connor W. Coley | Brian M. Barnes | Klavs F. Jensen
Source: ChemRxiv (preprint, 2020-02-07)
Field: Materials Science — Materials Chemistry

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access datasets of organic reactions with artificially calculated template applicability and pretraining a template relevance neural network on this augmented applicability dataset, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small dataset of well curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating these strategies can be very useful for small datasets. Template ; Computer science ; Retrosynthetic analysis ; Relevance (law) ; Artificial neural network ; Machine learning ; Set (abstract data type) ; Artificial intelligence ; Task (project management) ; Software ; Training set ; Data mining ; Pattern recognition (psychology) ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: David K. Ho | Albert Shkolnik | Neil Ferraro | Benjamin Rizkin | Ryan L. Hartman
Source: Computers & Chemical Engineering (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Using word embeddings in abstracts to accelerate metallocene catalysis polymerization research Metallocene ; Categorization ; Vocabulary ; Word (group theory) ; Computer science ; Word2vec ; Catalysis ; Polymerization ; Artificial neural network ; Artificial intelligence ; Chemistry ; Organic chemistry ; Polymer ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Alain C. Vaucher | Philippe Schwaller | Teodoro Laino
Source: ChemRxiv (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Completion of Partial Reaction Equations We present a deep-learning model for inferring missing molecules in reaction equations. Such an algorithm features multiple interesting behaviors. First, it can infer the necessary reagents and solvents in chemical transformations specified only in terms of main compounds, as often resulting from retrosynthetic analyses. The completion with necessary reagents ensures that reaction equations are compatible with deep-learning models relying on a complete reaction specification. Second, it can cure existing datasets by detecting missing compounds, such as reagents that are essential for given classes of reactions. Finally, this model is a generalization of models for forward reaction prediction and retrosynthetic analysis, as both can be formulated in terms of incomplete reaction equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address all three points.Workshop paper at the Machine Learning for Molecules Workshop at NeurIPS 2020. Retrosynthetic analysis ; Computer science ; Generalization ; Reagent ; Artificial intelligence ; Transformer ; Chemistry ; Mathematics ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Anthony Wang | Steven Kauwe | Ryan Murdock | Taylor D. Sparks
Source: ChemRxiv (preprint, 2020-02-19)
Field: Materials Science — Materials Chemistry

Compositionally-Restricted Attention-Based Network for Materials Property Prediction In this paper, we evaluate an attention-based neural network architecture for the prediction of inorganic materials properties given access to nothing but each materials' chemical composition. We demonstrate that this novel application of self-attention for material property predictions strikingly outperforms both statistical and ensemble machine learning methods, as well as a fully-connected neural network.This Compositionally-Restricted Attention-Based network, referred to as CrabNet, is associated with improved test metrics across six of seven different tested materials properties from the AFLOW database. Moreover, we show that CrabNet outperforms other methods in the absence of chemical information, even when the statistical and ensemble learning techniques are given domain-specific chemical knowledge about the materials. Given its impressive improvement in predictive accuracy compared to previous methods, as well as its minimal hardware requirements for training and prediction, we feel confident that CrabNet, and the ideas explored within, will be central for future materials informatics research. Computer science ; Property (philosophy) ; Artificial neural network ; Machine learning ; Artificial intelligence ; Domain (mathematical analysis) ; Ensemble learning ; Statistical learning ; Materials informatics ; Data mining ; Mathematics ; Health informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: Anthony Tabet | Thomas Gebhart | Guanglu Wu | Charlie Readman | Merrick Pierson Smela | Vijay Kumar Rana | Cole Baker | Harry Bulstrode | Polina Anikeeva | David H. Rowitch | Oren A. Scherman
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit[7]uril Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly. Support vector machine ; Host (biology) ; Constant (computer programming) ; Computer science ; Machine learning ; Algorithm ; Artificial intelligence ; Mathematics ; Biology ; Genetics Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications ; Boron Compounds in Chemistry
Authors: Sungwon Kim | Juhwan Noh | Geun Ho Gu | Alán Aspuru‐Guzik | Yousung Jung
Source: arXiv (Cornell University) (preprint, 2020-04-03)
Field: Materials Science — Materials Chemistry

Generative Adversarial Networks for Crystal Structure Prediction The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, machine learning generative models can offer promising opportunities since they allow for the continuous navigation of chemical space via low dimensional latent spaces. In this work, we employ a crystal representation that is inversion-free with a low memory requirement based on unit cell information and fractional atomic coordinates, and build the generative adversarial network (GAN) for crystal structures. The proposed model is then applied to the Mg-Mn-O ternary inorganic materials system to generate novel structures with application as potential water-splitting photoanodes, and combined with the evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The generative-HTVS system that we built predicts 23 new crystal structures with a reasonable predicted stability and bandgap. These findings suggest that the proposed generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed. Generative grammar ; Computer science ; Chemical space ; Representation (politics) ; Ternary operation ; Crystal (programming language) ; Stability (learning theory) ; Generative adversarial network ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Deep learning ; Drug discovery ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Xiang Gao | Farhad Ramezanghorbani | Olexandr Isayev | Justin S. Smith | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2020-05-03)
Field: Materials Science — Materials Chemistry

TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials This paper presents TorchANI, a PyTorch based software for training/inferenceof ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces andother physical properties of molecular systems. ANI is an accurate neural networkpotential originally implemented using C++/CUDA in a program called NeuroChem.Compared with NeuroChem, TorchANI has a design emphasis on being light weight,user friendly, cross platform, and easy to read and modify for fast prototyping, whileallowing acceptable sacrifice on running performance. Because the computation ofatomic environmental vectors (AEVs) and atomic neural networks are all implementedusing PyTorch operators, TorchANI is able to use PyTorch’s autograd engine to automatically compute analytical forces and Hessian matrices, as well as do force trainingwithout additional codes required. Computer science ; Hessian matrix ; Deep learning ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Computation ; Software ; Computer architecture ; Computational science ; Computer engineering ; Programming language Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Physics and Applications
Authors: Thorben Petersen | Thorsten Klüner
Source: The Journal of Physical Chemistry C (article, 2020-04-29)
Field: Materials Science — Materials Chemistry

Photodesorption of H<sub>2</sub>O from Anatase-TiO<sub>2</sub>(101): A Combined Quantum Chemical and Quantum Dynamical Study A theoretical study to clarify the photodesorption process of water on anatase-TiO2(101) was pursued in this study using a combined approach based on quantum chemical calculations and quantum dynamical simulations assisted by machine learning of artificial neural networks. First, an embedded cluster model was used to assess three-dimensional potential energy surfaces of ground and excited state of molecular water adsorption. The excited state addressed herein consists of a photogenerated hole which oxidizes the adsorbed water molecule. Next, the approximately 23 000 data points for each surface were fitted using artificial neural networks to generate a dense grid of the data. These fits are a prerequisite for our subsequent quantum dynamical simulations based on the propagation of wavepackets. Eventually, the photodesorption process of water on anatase-TiO2(101) was found to consist of a multidimensional mechanism involving a lateral translation of the water molecule toward the bridging oxygen row. Upon relaxation to the ground state, the maximum possible photodesorption probability of 85% was calculated through analyzing specific resonance lifetimes of the charge-transfer state. By comparison with experimental velocity distributions, long resonance lifetimes of over 60 fs can be ascribed to the studied system. Moreover, temperature effects by studying the population of vibronically excited states were included, which turned out to play a minor role in the photodesorption process. Excited state ; Anatase ; Quantum ; Population ; Chemical physics ; Molecule ; Chemistry ; Potential energy surface ; Atomic physics ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Water Quality Monitoring and Analysis ; Spectroscopy and Quantum Chemical Studies
Authors: Luca Messina | Alessio Quaglino | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | Christophe Domain | N. Castin | G. Bonny | Rolf Krause
Source: Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (article, 2020-10-12)
Field: Materials Science — Materials Chemistry

A DFT-driven multifidelity framework for constructing efficient energy models for atomic-scale simulations Fidelity ; Computer science ; Artificial neural network ; High fidelity ; Kinetic Monte Carlo ; Reliability (semiconductor) ; Monte Carlo method ; Experimental data ; Machine learning ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Somesh Mohapatra | Tzuhsiung Yang | Rafael Gómez‐Bombarelli
Source: Nature Machine Intelligence (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks Reusability ; Organic molecules ; Artificial neural network ; Computer science ; Environmental science ; Artificial intelligence ; Molecule ; Chemistry ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Kevin Maik Jablonka | Daniele Ongari | Seyed Mohamad Moosavi | Berend Smit
Source: ChemRxiv (preprint, 2020-01-17)
Field: Materials Science — Materials Chemistry

Using Collective Knowledge to Assign Oxidation States Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (> 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists. Heuristics ; Intuition ; Computer science ; Protonation ; Artificial intelligence ; Chemistry ; Organic chemistry ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Igor A. Abrikosov | Э. Е. Сон | B. O. Mukhamedov | Alexandra Khvan
Source: High Temperature (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Design of Materials for Nuclear Energy Applications: First-Principles Calculations and Artificial Intelligence Methods The review contains a description of modern methods and results of designing new materials for nuclear energy applications. The first part presented in this review contains a description of ab initio methods for calculating of materials properties. It describes rigorous and approximate quantum methods (density functional method) for the predicting the properties of materials employing modern supercomputers, and popular software packages for numerical simulation, as well as databases of first-principles calculations that exist internationally. With the accumulation of a large number of laborious quantum calculations, artificial intelligence methods are becoming more and more effective. In particular, machine learning methods are considered, and results of their use for the design of materials are presented in the review. The results of quantum mechanics modeling of binary alloys of iron, chromium and nickel are presented. The second part of the review will be devoted to quantum modeling of liquid and plasma states of matter in reactors, as well as experimental studies of materials for nuclear power. The third part of the review will be devoted to non-equilibrium plasma formed in reactors as a result of secondary processes during stopping of fast particles. Computer science ; Quantum ; Ab initio ; Stopping power ; Statistical physics ; Nuclear power ; Physics ; Quantum mechanics ; Nuclear physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Tori Cox | Volodymyr Gvozdetskyi | Mark Bertolami | Shannon Lee | Kristian Shipley | Oleg I. Lebedev | Julia V. Zaikina
Source: Angewandte Chemie (article, 2020-09-16)
Field: Materials Science — Materials Chemistry

Clathrate XI K<sub>58</sub>Zn<sub>122</sub>Sb<sub>207</sub>: A New Branch on the Clathrate Family Tree Abstract The compositional screening of K‐Zn‐Sb ternary system aided by machine learning, rapid exploratory synthesis using KH salt‐like precursor and in situ powder X‐ray diffraction yielded a novel clathrate type XI K 58 Zn 122 Sb 207 . This clathrate consists of a 3D Zn‐Sb framework hosting K + ions inside polyhedral cages, some of which are reminiscent of known clathrate types while others are unique to this structure type. The complex non‐centrosymmetric structure in the tetragonal space group was solved by means of single crystal X‐ray diffraction as a 6‐component twin due to pseudocubic symmetry and further confirmed by high‐resolution synchrotron powder X‐ray diffraction and state‐of‐the‐art scanning transmission electron microscopy. The electron‐precise composition of this clathrate yields narrow‐gap p ‐type semiconductor with extraordinarily low thermal conductivity due to displacement or “rattling” of K cations inside oversized cages and as well as to twinning, stacking faults and antiphase boundary defects. Clathrate hydrate ; Crystal twinning ; Crystallography ; Tetragonal crystal system ; Powder diffraction ; Chemistry ; Bismuth ; X-ray crystallography ; Electron diffraction ; Crystal structure ; Diffraction ; Materials science ; Hydrate ; Physics ; Optics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Eliya Nachmani | Lior Wolf
Source: arXiv (Cornell University) (preprint, 2020-02-01)
Field: Materials Science — Materials Chemistry

Molecule Property Prediction and Classification with Graph Hypernetworks Graph neural networks are currently leading the performance charts in learning-based molecule property prediction and classification. Computational chemistry has, therefore, become the a prominent testbed for generic graph neural networks, as well as for specialized message passing methods. In this work, we demonstrate that the replacement of the underlying networks with hypernetworks leads to a boost in performance, obtaining state of the art results in various benchmarks. A major difficulty in the application of hypernetworks is their lack of stability. We tackle this by combining the current message and the first message. A recent work has tackled the training instability of hypernetworks in the context of error correcting codes, by replacing the activation function of the message passing network with a low-order Taylor approximation of it. We demonstrate that our generic solution can replace this domain-specific solution. Computer science ; Message passing ; Testbed ; Theoretical computer science ; Property (philosophy) ; Graph ; Artificial neural network ; Context (archaeology) ; Artificial intelligence ; Machine learning ; Distributed computing ; Computer network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics