jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenhao Zhu | Ziyao Li | Lingsheng Cai | Guojie Song
Source: arXiv (Cornell University) (preprint, 2021-12-26)
Field: Materials Science — Materials Chemistry

Stepping Back to SMILES Transformers for Fast Molecular Representation Inference In the intersection of molecular science and deep learning, tasks like virtual screening have driven the need for a high-throughput molecular representation generator on large chemical databases. However, as SMILES strings are the most common storage format for molecules, using deep graph models to extract molecular feature from raw SMILES data requires an SMILES-to-graph conversion, which significantly decelerates the whole process. Directly deriving molecular representations from SMILES is feasible, yet there exists a performance gap between the existing unpretrained SMILES-based models and graph-based models at large-scale benchmark results, while pretrain models are resource-demanding at training. To address this issue, we propose ST-KD, an end-to-end \textbf{S}MILES \textbf{T}ransformer for molecular representation learning boosted by \textbf{K}nowledge \textbf{D}istillation. In order to conduct knowledge transfer from graph Transformers to ST-KD, we have redesigned the attention layers and introduced a pre-transformation step to tokenize the SMILES strings and inject structure-based positional embeddings. Without expensive pretraining, ST-KD shows competitive results on latest standard molecular datasets PCQM4M-LSC and QM9, with $3\text{-}14\times$ inference speed compared with existing graph models. Inference ; Computer science ; Molecular graph ; Graph ; Transformer ; Artificial intelligence ; Representation (politics) ; Benchmark (surveying) ; Theoretical computer science ; Virtual screening ; Data mining ; Machine learning ; Chemistry ; Drug discovery ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Martin Uhrin
Source: Physical review. B./Physical review. B (preprint, 2021-10-26)
Field: Materials Science — Materials Chemistry

Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments In this work we apply methods for describing three-dimensional images to the problem of encoding atomic environments in a way that is invariant to rotations, translations, and permutations of the atoms and, crucially, can be decoded back into the original environment modulo global orientation without the need for training a model. From the point of view of decoding, the descriptor is optimally complete and can be extended to arbitrary order, allowing for a systematic convergence of the fidelity of the description. In experiments on molecules ranging from 3 to 29 atoms in size, we demonstrate that positions can be decoded with a 97% success rate and positions plus species with a 70% rate of success, rising to 95% if a second fingerprint is used. In all cases, consistent recovery is observed for molecules with 17 or fewer atoms. Additionally, we evaluate the descriptor's performance in predicting the energies and forces of bulk Ni, Cu, Li, Mo, Si, and Ge by means of a neural network model trained on DFT data. When comparing to six machine learning interaction potential methods that use various descriptors and regression schemes, our descriptor is found to be competitive, in several cases outperforming well established methods. The combined ability to both decode and make property predictions from a representation that does not need to be learned lays the foundations for a novel way of building generative models that are tasked with solving the inverse problem of predicting atomic arrangements that are statistically likely to have certain desired properties. Invertible matrix ; Decoding methods ; Computer science ; Invariant (physics) ; Fidelity ; Algorithm ; Encoding (memory) ; Generative grammar ; Mathematics ; Artificial intelligence ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mehmet Cagri Kaymak | Ali Rahnamoun | Kurt A. O’Hearn | Adri C. T. van Duin | Kenneth M. Merz | Hasan Metin Aktulga
Source:  (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields Molecular dynamics (MD) simulations ease the study of the chemistry of interest. While classical models that governs the interaction of the atoms lack reactivity, the quantum mechanics based methods increase the computational cost drastically. ReaxFF fills the gap between these two ends of the spectrum by allowing bond breaking and dynamic charges. To achieve realistic simulations using ReaxFF, the model parameters need to be carefully tuned based on the training data created using more accurate but expensive methods. The current optimization methods focus on black-box optimization methods such as genetic algorithms (GAs), Monte-Carlo methods and covariance matrix adaptation evolutionary strategy (CMA-ES). Due to the stochastic behavior of these methods, the training requires hundreds of thousands of error evaluations for complex training tasks and each error evaluation usually involves energy minimization of the many molecules in the training data. In this work, we propose a novel approach which takes advantage of the modern tools developed for machine learning to improve the training efficiency of the force field development for ReaxFF. By calculating the gradients of the loss function using JAX library developed by Google, we are able to use well studied local optimization methods such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) method. To further decrease the training time, we skip the energy minimization of the molecules during the local optimization since the parameters of the model are not likely to change drastically during a local search. JAX allows us to easily parallelize the error evolution by compiling the code for CPUs, GPUs or TPUs. With the help of the modern accelerators and the gradient information, we are able to decrease the training time from days to minutes. ReaxFF ; Computer science ; Force field (fiction) ; Molecular dynamics ; Minification ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Parallel Computing and Optimization Techniques
Authors: Pedro B. P. S. Reis | Marco Bertolini | Floriane Montanari | Walter Rocchia | Miguel Machuqueiro | Djork-Arné Clevert
Source: Research Square (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

pKAI: a fast and interpretable deep learning approach for accurate electrostatics-driven pKa predictions Abstract The p K a values of ionizable residues influence protein folding, stability and biological function. The p K a in bulk water is known for all residues, however, in a protein environment, it can significantly be affected by confinement and electrostatics. Existing computational methods to estimate p K a shifts rely on theoretical approximations and lengthy computations. Furthermore, the amount of experimentally determined p K a values is still very limited, hindering the development of faster machine learning-based methods. In this work, we use a data set of 6 million p K a shifts — determined by PypKa, a continuum electrostatics method — to train deep learning models that are shown to rival the physics-based predictor. On ~750 experimentally determined data points, our model displays the best accuracy and it is the only one that breaks the 1 p K unit RMSE barrier of this considerably difficult test set. Although trained using a very simplified view of the surroundings of the titratable group (namely, atom types and distances to other titratable groups within a given radius), the models are shown to assign proper electrostatic charges to chemical groups, to keep the known correlation between solvent exposure and p K a shift magnitude, and to grasp the importance of close interactions, including hydrogen bonds. Inference times allow speedups of more than 1000 times faster than physics-based methods, especially for large proteins. By combining speed, accuracy and a reasonable understanding of the theoretical basis for p K a shifts, our models provide a game-changing solution for fast estimations of macroscopic p K a from ensembles of microscopic (p K half ) values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations. Electrostatics ; Artificial intelligence ; Deep learning ; Computer science ; Machine learning ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source: ChemRxiv (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields forOrganic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Force field (fiction) ; Transferability ; Flexibility (engineering) ; Dihedral angle ; Molecular dynamics ; Cluster (spacecraft) ; Molecule ; Statistical physics ; Computer science ; Chemistry ; Biological system ; Computational chemistry ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: K Anthea Rana | John Andrei Acantilado | Jared Ethan Santos | Raymond R. Tan | Kathleen B. Aviso
Source: SHILAP Revista de lepidopterología (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides Despite being recognized as a key component towards reducing global GHG emissions, many challenges remain throughout the hydrogen value chain. Hydrogen storage is one critical aspect, since proper storage is essential for its wide scale deployment. One storage option is the chemisorption of hydrogen in metals and intermetallic alloys (i.e., metal hydrides). This approach can potentially address issues on safety, infrastructure, and cost which currently hinder the transition toward a hydrogen economy. The only suitable method for determining appropriate hydrogen storage materials is through experimentation which is resource intensive. There are many parameters that can affect the hydrogen storage capacity of a material; databases on previously investigated materials can be used to narrow down future investigation to the most promising candidates. Machine learning (ML) techniques can be employed to determine how different properties predict for hydrogen storage capacity. ML can use previously compiled data to generate a classification model that can be utilized for determining and predicting a material's viability for hydrogen storage. This paper uses the hyperbox ML technique to generate interpretable decision rules to predict if a metal hydride is a good candidate for hydrogen storage. A case study which specifically focuses on complex and magnesium hydrides is used to demonstrate this approach. The generated decision model had a false positive rate of 22.0 % and false negative rate of 36.8 %. Hydrogen storage ; Binary number ; Classifier (UML) ; Metal ; Materials science ; Hydrogen ; Computer science ; Artificial intelligence ; Chemistry ; Metallurgy ; Mathematics ; Organic chemistry Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Hybrid Renewable Energy Systems
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Huai Sun
Source: ChemRxiv (preprint, 2021-06-01)
Field: Materials Science — Materials Chemistry

A Comparative Study of Marginalized Graph Kernel and Message Passing Neural Network This work presents a state-of-the-art hybrid kernel for molecular property predictions. The hybrid kernel consists of a marginalized graph kernel that operates on molecular graphs and radial basis function kernels that operate on global molecular features. Direct message passing neural network (D-MPNN) with global molecular features is used as strong baselines. After using Bayesian optimization to find the optimal hyperparameters, we benchmark the models on 11 publicly available data sets. Our results show that the prediction of the graph kernel is correlated to the prediction of D-MPNN, which indicates that the molecular representation learned from D-MPNN is very close to the reproducing kernel Hilbert space generated by the hybrid kernel. These results may provide clues for research on the interpretability of graph neural networks. In addition, ensembling the graph kernel models with D-MPNN is the best. The advantage of D-MPNN lies in computational efficiency, and the advantage of the graph kernel model lies in the inherent uncertainty qualification of Gaussian process regression. All codes for graph kernel machines used in this work can be found at https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine. Computer science ; Graph kernel ; Kernel (algebra) ; Interpretability ; Kernel embedding of distributions ; Tree kernel ; Graph ; Kernel method ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Discrete mathematics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Adeleke Maradesa | Baptiste Py | Emanuele Quattrocchi | Francesco Ciucci
Source: ChemRxiv (preprint, 2021-12-06)
Field: Chemistry — Analytical Chemistry

The Probabilistic Deconvolution of the Distribution of Relaxation Times with Finite Gaussian Processes Electrochemical impedance spectroscopy (EIS) is a tool widely used to study the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as one of the main methods for the analysis of EIS spectra. Gaussian processes can be used to regress EIS data, quantify uncertainty, and deconvolve the DRT, but current implementations do not constrain the DRT to be positive and can only use the imaginary part of EIS spectra. Herein, we overcome both issues by using a finite Gaussian process approximation to develop a new framework called the finite Gaussian process distribution of relaxation times (fGP-DRT). The analysis on artificial EIS data shows that the fGP-DRT method consistently recovers exact DRT from noise-corrupted EIS spectra while accurately regressing experimental data. Furthermore, the fGP-DRT framework is used as a machine learning tool to provide probabilistic estimates of the impedance at unmeasured frequencies. The method is further validated against experimental data from fuel cells and batteries. In short, this work develops a novel probabilistic approach for the analysis of EIS data based on Gaussian process, opening a new stream of research for the deconvolution of DRT. Deconvolution ; Gaussian ; Probabilistic logic ; Noise (video) ; Computer science ; Gaussian noise ; Gaussian process ; Algorithm ; Electrical impedance ; Relaxation (psychology) ; Artificial intelligence ; Physics ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Fuel Cells and Related Materials ; Advanced Chemical Sensor Technologies
Authors: Xingyue Fan
Source: Materiali in tehnologije (article, 2021-04-13)
Field: Materials Science — Materials Chemistry

PREDICTION OF FORMATION ENERGY USING TWO-STAGE MACHINE LEARNING BASED ON CLUSTERING The formation energy (Hf) is one of the important properties associated with the thermodynamic stability of ABO3-type perovskite. In this work, two-stage machine learning based on hierarchical clustering and regression was designed for improving the prediction values of the density-functional theory (DFT) Hf of ABO3-type perovskites. A global dataset was clustered into Cluster 1 and Cluster 2 using the CHI (the Calinski-Harabasz index). To compare the prediction performances of Hf, DTR (decision tree regression), GBRT (gradient boosted regression trees), RFR (random forest regression) and ETR (extra tree regression) were applied to build models of Cluster 1, Cluster 2 and the global dataset, respectively. The results showed that all four different regression models of Cluster 1 had a higher R2, and lower MSE and MAE than those of the global dataset, while the models of Cluster 2 were poorer. Meanwhile, the GBRT model of Cluster 1 achieved a higher R2 of 0.917, and lower MSE and MAE of 0.033 eV/atom and 0.125 eV/atom. We further validated and compared the generalization ability of the models by predicting the Hf of ABO3-type perovskite previously unseen in the training set. The two-stage machine-learning models proposed here can provide useful guidance for accelerating the exploration of materials with desired properties. Cluster analysis ; Cluster (spacecraft) ; Generalization ; Regression ; Random forest ; Stability (learning theory) ; Hierarchical clustering ; Decision tree ; Regression analysis ; Artificial intelligence ; Materials science ; Linear regression ; Machine learning ; Energy (signal processing) ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Renyi Zhang | Yiran Bai | Meichen Guo
Source: Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Research on the preparation of C4 olefin by ethanol coupling based on regression analysis and XGBoost algorithm To address the challenges of preparing C4 olefins, this study processes the data by using regression analysis, calculating its Spearman correlation coefficient, analyzing the test results of a given catalyst combination at 350 degrees at different times in an experiment, as well as analyzing various catalyst combinations and temperature to determine the effects of ethanol conversion rate and C4 olefin selectivity. Moreover, based on the machine learning XGBoost algorithm, this study establishes a regression model between the catalyst combination and temperature and the yield of C4 olefins. At the same time, this study further explores the variable values when the C4 olefins yield is as high as possible. For instance, the catalyst carrier, Co loading, total Co/SiO2, Co/SiO2 and HAP loading ratio, ethanol concentration, temperature, and six other elements are considered as the independent variables. Then, C4 olefin yield is considered as the dependent variable. The XGBoost algorithm is then used for integrated training. Then, the six most important ranking features are selected. Finally, the trained model is reverse-optimized and, to further optimize the catalyst combination and temperature values when the C4 olefin yield is as high as possible, the grid search method is applied. Olefin fiber ; Yield (engineering) ; Regression analysis ; Catalysis ; Ranking (information retrieval) ; Algorithm ; Ethanol ; Correlation coefficient ; Regression ; Linear regression ; Variable (mathematics) ; Variables ; Computer science ; Chemistry ; Mathematics ; Machine learning ; Statistics ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Energy, Environment, Economic Growth ; Catalysis and Oxidation Reactions
Authors: Kevin M. Roccapriore | Sergei V. Kalinin | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement a deep kernel learning workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, Scanning Probe Microscopies, chemical, and optical imaging. Workflow ; Bayesian optimization ; Computer science ; Physics ; Artificial intelligence ; Plasmon ; Function (biology) ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Kokin Nakajin | Takuya Minami | Toshio Fujita | Masaaki Kawata | Katsumi Murofushi | Hiroshi Uchida | Kazuhiro Omori | Yoshishige Okuno
Source: MRS Advances (article, 2021-02-22)
Field: Materials Science — Materials Chemistry

Optimization of multiple physical properties by machine learning incorporating the concept of deviation value Materials science ; Standard deviation ; Artificial intelligence ; Value (mathematics) ; Relative standard deviation ; Mixing (physics) ; Set (abstract data type) ; Machine learning ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Memory and Neural Computing