jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haripriyan Uthayakumar | Rahul Krishna K | Raj Jain | Rajnish Kumar | Tarak K. Patra
Source: ACS Engineering Au (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

QRChEM: A Deep Learning Framework for Materials Property Prediction and Design Using QR Codes Machine learning (ML) surrogate models are used for the rapid prediction of materials properties and are promising tools for accelerating new materials design and development. The performance and accuracy of these surrogate models appear to be intricately connected to the molecular representation that is employed. Developing efficient numerical representations of molecules is vital for the success of surrogate models in predicting materials' properties. Here, we propose a new machine-readable molecular representation, namely a molecular quick response (QR) code, for the deep learning of materials structure–property correlations. We built a convolutional deep neural network (CNN) model based on molecular QR codes, which is abbreviated as QRChEM. QRChEM was trained and validated using ∼21 000 data for four representative properties of small molecules, namely specific heat, enthalpy, zero-point vibrational energy, and HOMO–LUMO band gap. We show that QRChEM outperforms the commonly used Morgan fingerprint-based and one-hot encoding (OHE)-based deep learning frameworks. We further performed UMAP (uniform manifold approximation and projection) on the molecular QR codes to demonstrate the differentiability of the molecular topologies, which is vital for high-fidelity surrogate model development. Computer science ; Deep learning ; Artificial intelligence ; Convolutional neural network ; Differentiable function ; Representation (politics) ; Surrogate model ; Encoding (memory) ; Code (set theory) ; Projection (relational algebra) ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yunsie Chung | William H. Green
Source: ChemRxiv (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28,000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal/mol for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings. Solvation ; Chemistry ; Reaction rate constant ; Solvent ; COSMO-RS ; Reaction rate ; Solvent effects ; Enthalpy ; Thermodynamics ; Computational chemistry ; Kinetics ; Organic chemistry ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Atmika Bhardwaj | Jens‐Uwe Sommer | Marco Werner
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Nucleation patterns of polymer crystals analyzed by machine learning models We use machine learning algorithms to detect the crystalline phase in undercooled melts in molecular dynamics simulations. Our classification method is based on local conformation and environmental fingerprints of individual monomers. In particular, we employ self-supervised auto-encoders to compress the fingerprint information and a Gaussian mixture model to distinguish ordered states from disordered ones. The resulting identification of crystalline monomers agrees to a large extent with human-defined classifiers such as the stem-length-based classification scheme as developed in our previous work [C. Luo and J.-U. Sommer, Macromolecules 44 (2011), 1523], but does not require any foreknowledge about the structure of semi-crystalline polymers. Because of its local sensitivity, the method allows the resolution of detailed time patterns of crystalline order before an apparent signature of the transition is visible in thermodynamic properties such as for the specific volume. At a pre-transition point, we observe the highest crystallization efficiency using the fraction of monomers being conserved in the crystalline phase as compared to the number of monomers joining that phase. Monomer ; Crystallization ; Nucleation ; Phase transition ; Polymer ; Phase (matter) ; Materials science ; Gaussian ; Biological system ; Chemical physics ; Statistical physics ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science
Authors: Esther Heid | Charles J. McGill | Florence H. Vermeire | William H. Green
Source: ChemRxiv (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

Characterizing Uncertainty in Machine Learning for Chemistry Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on datasets of molecular properties, we show important trends in model performance associated with the level of noise in the dataset, size of the dataset, model architecture, molecule representation, ensemble size, and dataset splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance, and 4) evaluations of cross-validation models understate their performance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts. Robustness (evolution) ; Variance (accounting) ; Computer science ; Machine learning ; Uncertainty quantification ; Context (archaeology) ; Artificial intelligence ; Noise (video) ; Property (philosophy) ; Reliability (semiconductor) ; Representation (politics) ; Ensemble learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Basile Herzog | Alejandro Gallo | Felix Hummel | Michaël Badawi | Tomáš Bučko | Sébastien Lebègue∥ | Andreas Grüneis | Dario Rocca
Source: ChemRxiv (preprint, 2023-04-09)
Field: Materials Science — Materials Chemistry

Coupled cluster finite temperature simulations of periodic materials via machine learning Density functional theory is the workhorse of materials simulations. Unfortunately, the quality of results often varies depending on the specific choice of the exchange-correlation functional, and this significantly limits the predictive power of this approach. Coupled cluster theory, including single, double and perturbative triple particle-hole excitation operators, is widely considered as the `gold standard` of quantum chemistry as it can achieve chemical accuracy for non-strongly correlated applications. Because of the high computational cost, the application of coupled cluster theory in materials simulations is rare, and this is particularly true if finite-temperature properties are of interest for which molecular dynamics simulations have to be performed. By combining recent progress in machine learning models with low data requirements for energy surfaces and in the implementation of coupled cluster theory for periodic materials, we show that chemically accurate simulations of materials are practical and could soon become significantly widespread. As an example of this numerical approach, we consider the calculation of the enthalpy of adsorption of CO2 in a porous material. Coupled cluster ; Density functional theory ; Cluster (spacecraft) ; Statistical physics ; Molecular dynamics ; Computer science ; Materials science ; Algorithm ; Computational chemistry ; Physics ; Molecule ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sae Dieb | Yoshiaki Toda | Keitaro Sodeyama | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Machine learning-assisted determination of material chemical compositions: a study case on Ni-base superalloy The determination of chemical compositions of materials plays a paramount role in materials design and discovery.Optimization of such compositions can be a very expensive trial-and-error task, specially when the desired properties are very sensitive to the composition variations.As the number of elements and the variations of the possible composition values increase, the number of possible candidate materials increases exponentially.In this work, we present an efficient machine learning-assisted method to optimize the chemical compositions of materials for desired mechanical properties.The method utilizes a hybrid approach combining Monte Carlo tree search (MCTS) and an expansion policy neural network.The efficiency of this method was demonstrated by optimizing chemical compositions of a seven-element Ni-base superalloy (Al, Co, Cr, Mo, Nb, Ti, and Ni) to avoid the precipitation of the gamma-prime ( 0 ) phase during cooling in the 3D additive manufacturing process.We were able to find Ni-base superalloys that could not be found by trial-and-error search or by using human experience. Superalloy ; Materials science ; Chemical composition ; Base (topology) ; Monte Carlo method ; Precipitation ; Biological system ; Artificial neural network ; Process (computing) ; Process engineering ; Computer science ; Microstructure ; Machine learning ; Metallurgy ; Thermodynamics ; Mathematics ; Statistics ; Engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Crystallization and Solubility Studies
Authors: Taiping Hu | Teng Yang | Jianchuan Liu | Bin Deng | Zhengtao Huang | Xiaoxu Wang | Fuzhi Dai | Guobing Zhou | Fangjia Fu | Ping Tuo | Ben Bin Xu | Shenzhen Xu
Source: arXiv (Cornell University) (preprint, 2023-09-03)
Field: Materials Science — Materials Chemistry

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials. Ab initio ; Transition metal ; Spins ; Cathode ; Materials science ; Ternary operation ; Oxide ; Ab initio quantum chemistry methods ; Ion ; Electron ; Spin (aerodynamics) ; Condensed matter physics ; Chemistry ; Physics ; Computer science ; Physical chemistry ; Thermodynamics ; Quantum mechanics ; Metallurgy ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Caglar Tamur | Shaofan Li | Danielle Zeng
Source: PubMed (preprint, 2023-07-19)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks for Predicting Mechanical Properties of Crystalline Polyamide12 via Molecular Dynamics Simulations Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress-strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress-strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study. Artificial neural network ; Finite element method ; Materials science ; Polymer ; Biological system ; Molecular dynamics ; Anisotropy ; Multiscale modeling ; Computer science ; Work (physics) ; Stress–strain curve ; Process (computing) ; Microstructure ; Stress (linguistics) ; Deformation (meteorology) ; Artificial intelligence ; Mechanical engineering ; Composite material ; Structural engineering ; Physics ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Injection Molding Process and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Darinel Valencia-Márquez | Antonio Flores‐Tlacuahuac | Alan Aguirre‐Soto
Source: Computers & Chemical Engineering (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Computer aided molecular design coupled to deep learning techniques as a less-expensive approach to design organic photoredox catalysts Absorbance ; Chromophore ; HOMO/LUMO ; Absorption (acoustics) ; Chemistry ; Computer science ; Molecule ; Absorption spectroscopy ; Biological system ; Biochemical engineering ; Process engineering ; Materials science ; Photochemistry ; Organic chemistry ; Physics ; Engineering Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Yeonjoon Kim | Hojin Jung | Sabari Kumar | Robert S. Paton | Seonah Kim
Source: ChemRxiv (preprint, 2023-07-06)
Field: Materials Science — Materials Chemistry

Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model’s predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11,637 experimental solubilities to >900,000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal/mol for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. We anticipate this approach will be attractive to other areas of predictive chemistry where experimental, computational, and any other heterogeneous data sources should be combined. Solvation ; Artificial neural network ; Computer science ; Generalizability theory ; Solubility ; Machine learning ; Chemical database ; Data point ; Database ; Artificial intelligence ; Chemistry ; Solvent ; Mathematics ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Zhao‐Yang Wang | Yuhang Jing | Chuan Zhang | Yi Sun | Weiqi Li | Jianqun Yang | Xingji Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Machine-Learning Interatomic Potential to Understand the Anisotropic Fracture Behavior of Bazro3 Material Anisotropy ; Materials science ; Fracture (geology) ; Interatomic potential ; Condensed matter physics ; Nanotechnology ; Composite material ; Physics ; Molecular dynamics ; Chemistry ; Optics ; Computational chemistry High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Mineral Processing and Grinding
Authors: Xinyang Dong | Emanuel Gull | Lei Wang
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Equivariant neural network for Green's functions of molecules and materials The many-body Green's function provides access to electronic properties beyond density functional theory level in ab inito calculations. In this manuscript, we propose a deep learning framework for predicting the finite-temperature Green's function in atomic orbital space, aiming to achieve a balance between accuracy and efficiency. By predicting the self-energy matrices in Lehmann representation using an equivariant message passing neural network, our method respects its analytical property and the $E(3)$ equivariance. The Green's function is obtained from the predicted self-energy through Dyson equation with target total number of electrons. We present proof-of-concept benchmark results for both molecules and simple periodic systems, showing that our method is able to provide accurate estimate of physical observables such as energy and density of states based on the predicted Green's function. Equivariant map ; Observable ; Simple (philosophy) ; Function (biology) ; Representation (politics) ; Artificial neural network ; Benchmark (surveying) ; Density functional theory ; Atomic orbital ; Property (philosophy) ; Statistical physics ; Energy (signal processing) ; Green's function ; Computer science ; Physics ; Topology (electrical circuits) ; Mathematics ; Applied mathematics ; Computational chemistry ; Quantum mechanics ; Electron ; Pure mathematics ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; History and advancements in chemistry ; Advanced Chemical Physics Studies
Authors: Wei Zhang | Christof Schütte
Source: arXiv (Cornell University) (preprint, 2023-07-01)
Field: Materials Science — Materials Chemistry

Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics High-dimensional metastable molecular system can often be characterised by a few features of the system, i.e. collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning-based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning-based approaches for finding CVs, either by computing leading eigenfunctions of infinitesimal generator or transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples. Autoencoder ; Deep learning ; Computer science ; Artificial intelligence ; Transfer operator ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems