jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hengrui Zhang | Wei Chen | Akshay Iyer | Daniel W. Apley
Source: Research Square (preprint, 2022-08-29)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design. Frequentist inference ; Machine learning ; Categorical variable ; Artificial intelligence ; Computer science ; Bayesian optimization ; Curse of dimensionality ; Gaussian process ; Bayesian probability ; Surrogate model ; Bayesian inference ; Gaussian Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: K. Song | Jun Li
Source: ChemRxiv (preprint, 2022-11-28)
Field: Materials Science — Materials Chemistry

Neural Network Based ∆-Machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction The recently proposed permutationally invariant polynomial-neural network (PIP-NN) based ∆-machine learning (∆-ML) approach (PIP-NN ∆-ML, J. Phys. Chem. Lett. 2022, 13, 4729) is a flexible, general, and highly cost-efficient method to develop full dimensional accurate potential energy surface (PES). Only a small portion of points, which can be actively selected from the low-level (often DFT) points, with high-level energies are needed to bring a low-level PES to a high-level of quality. The hydrogen abstraction reaction between methanol and hydroxyl radical, OH + CH3OH, has been studied by theories and experiments for a long time due to its great importance in combustion, atmospheric and interstellar chemistry. However, it is not trivial to develop the full dimensional accurate PES for it. In this work, the PIP-NN ∆-ML method is successfully applied to the title reaction. The DFT PES was fitted by using 140192 points. Only 5% of the DFT dataset was needed to be calculated at the level of UCCSD(T)-F12a/AVTZ, and to improve the DFT PES to the target high-level, UCCSD(T)-F12a/AVTZ. More than 92% of the original unaffordable calculation cost were saved. The kinetics, including rate coefficients and branching ratios, were then studied by performing quasi-classical trajectory calculations on this newly fitted PES for the title reaction. Potential energy surface ; Hydrogen atom abstraction ; Artificial neural network ; Computer science ; Chemistry ; Combustion ; Computational chemistry ; Hydrogen ; Physical chemistry ; Molecule ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Rishi Hazra | Prasenjit Hazra
Source: Journal of Emerging Investigators (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of molecular energy using Coulomb matrix and Graph Neural Network Molecular energy is used to estimate the overall favorability of the formation of a molecule, which can be measured either directly or indirectly. However, both methods require a chemical reaction. Molecular energy has also been investigated computationally. Yet, these calculations require a lot of expensive computer resources. The purpose of this project is to apply machine learning (ML) techniques to create models from known molecular properties. Once the model is created, the molecular properties of new molecules can be predicted quickly. Feedforward Neural Network (FNN) models were trained with two different representations of the molecules. In the first model, the molecules were represented by the Coulombic force between each atom; in the second model, the molecular structure, the bonding between atoms, and the electron configuration of each atom were provided as inputs. Prediction errors of the two models were compared statistically to determine the better representation of the molecule. The FNN network was able to predict the molecular energy with low errors in both models. Though, the model with the Coulomb matrix performed better for molecules that do not contain high electronegative atoms and performed poorly for molecules with three or more fluorine atoms. The effect of fluorine (or other electronegative atoms) on the Coulomb matrix needs to be further investigated. Molecule ; Molecular graph ; Matrix (chemical analysis) ; Artificial neural network ; Molecular dynamics ; Atom (system on chip) ; Coulomb ; Representation (politics) ; Computer science ; Work (physics) ; Chemical physics ; Computational chemistry ; Chemistry ; Materials science ; Atomic physics ; Statistical physics ; Graph ; Physics ; Artificial intelligence ; Electron ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Omar Allam | Hyun-Myung Woo | Graham Brantley | Robert Kuramshin | Zlatomir Stoichev | Byung-Jun Yoon | Seung Soon Jang
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Materials Chemistry

Uncovering Molecular Structure – Redox Potential Relationships for Organic Electrode Materials: A Hybrid DFT – Machine Learning Approach Density functional theory and machine learning are used to investigate the structure-electrochemical performance relationships of organic moieties for use in Li-ion batteries. Namely, DFT calculations are performed to predict the redox potential of several novel organic molecules with an accuracy within ~0.1 V of experimental measurements. However, despite its ability to provide valuable insight regarding the electrochemical properties of novel organic molecules, our high efficacy DFT modeling protocol demands significant computational time and is therefore impractical for the vast screening of novel material candidate. As a result, we explore machine learning as a strategy for the accelerated discovery of novel organic materials. More critically, we use machine learning as a method for assessing the various structure-electrochemical relationships which can provide a more general guideline for the design of organic electrode materials. We are employing different learning models, including artificial neural networks, gradient-boosting regression, and kernel methods (such as kernel ridge regression and Gaussian process regression), via three different pipelines with varying sophistication with the aim of generating an advanced ML scheme for the accurate prediction and analysis of electrochemical activity. In addition to incorporating structural fingerprints, we are exploring an active learning framework, namely using the efficient global optimization scheme, to explore the materials space strategically using publicly available datasets. Through this approach, we can discover new electrode materials that could 1) have a higher probability for achieving enhanced electrochemical properties and 2) increase our learning model’s performance by increasing our dataset’s representation of the material space. Additionally, we are implementing a high-throughput virtual screening (HTVS) pipeline which consists of several surrogate learning models with increasing levels of fidelity. The material candidates are pruned at each stage to discard samples that are unlikely to possess a desirable redox potential for cathodic application, and the remaining samples move on to the next model. In this way, only a subset of the original dataset which has a higher probability for lying within the desired redox potential range is pursued using the most computationally expensive model. Kriging ; Computer science ; Machine learning ; Chemical space ; Artificial intelligence ; Kernel (algebra) ; Density functional theory ; Materials science ; Chemistry ; Mathematics ; Computational chemistry ; Drug discovery Machine Learning in Materials Science ; Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Souvik Manna | Diptendu Roy | Sandeep Das | Biswarup Pathak
Source: ChemRxiv (preprint, 2022-01-10)
Field: Materials Science — Materials Chemistry

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important parameters for choosing suitable electrode materials for high energy storage metal ion battery. Exploration of suitable electrode materials for metal ion batteries other than Li ion batteries (LIBs) has been deficient, though there is a need to develop alternative battery technologies with higher energy storage characteristics and environmental safety. In this work, we have considered Li, Na and K-ion electrode materials and their available battery data from Materials Project database to predict specific capacity of prospective K-ion battery electrode materials. Suitable features have been considered and developed to train the various ML algorithms. Mean Absolute Percentage Error has been considered as the performance metrics for assessment of different ML algorithms and among them, kernel ridge regression has been adopted as the most useful to predict the capacity of unknown electrode materials for K-ion battery. Using the value of specific capacity, the number of intercalated K ions in the formula unit of the non-intercalated electrode material compounds have also been calculated. DFT calculations have also been performed to verify the results obtained through ML. Our result shows ML is an encouraging alternative to computationally demanding DFT process as it can screen electrode materials rapidly for battery. Battery (electricity) ; Electrode ; Computer science ; Ion ; Materials science ; Work (physics) ; Process (computing) ; Energy storage ; Mechanical engineering ; Chemistry ; Engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Hengrui Zhang | Wei Chen | Akshay Iyer | Daniel W. Apley
Source: arXiv (Cornell University) (preprint, 2022-07-11)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design. Frequentist inference ; Machine learning ; Artificial intelligence ; Categorical variable ; Computer science ; Bayesian optimization ; Curse of dimensionality ; Gaussian process ; Bayesian probability ; Surrogate model ; Bayesian inference ; Gaussian Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jay Morgan | Adeline Paiement | Christian Klinke
Source: arXiv (Cornell University) (preprint, 2022-08-25)
Field: Materials Science — Materials Chemistry

Domain-informed graph neural networks: a quantum chemistry case study We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN). We focus on graph neural networks (GNN), with a use case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledge into the design of the GNN to constrain and regularise its learning, towards higher accuracy and generalisation. First, knowledge on the existence of different types of relations (chemical bonds) between atoms is used to modulate the interaction of nodes in the GNN. Second, knowledge of the relevance of some physical quantities is used to constrain the learnt features towards a higher physical relevance using a simple multi-task paradigm. We demonstrate the general applicability of our knowledge integrations by applying them to two architectures that rely on different mechanisms to propagate information between nodes and to update node states. Relevance (law) ; Artificial neural network ; Computer science ; Graph ; Quantum chemical ; Domain (mathematical analysis) ; Domain knowledge ; Theoretical computer science ; Simple (philosophy) ; Focus (optics) ; Task (project management) ; Node (physics) ; Artificial intelligence ; Machine learning ; Molecule ; Chemistry ; Mathematics ; Engineering ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Mathias Schreiner | Arghya Bhowmik | Tejs Vegge | Peter Bjørn Jørgensen | Ole Winther
Source: arXiv (Cornell University) (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

NeuralNEB -- Neural Networks can find Reaction Paths Fast Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale exploration. Machine Learning (ML) models have turned out to be excellent emulators of small molecule DFT calculations and could possibly replace DFT in such tasks. For kinetics, success relies primarily on the models capability to accurately predict the Potential Energy Surface (PES) around transition-states and Minimal Energy Paths (MEPs). Previously this has not been possible due to scarcity of relevant data in the literature. In this paper we train state of the art equivariant Graph Neural Network (GNN)-based models on around 10.000 elementary reactions from the Transition1x dataset. We apply the models as potentials for the Nudged Elastic Band (NEB) algorithm and achieve a Mean Average Error (MAE) of 0.13+/-0.03 eV on barrier energies on unseen reactions. We compare the results against equivalent models trained on QM9 and ANI1x. We also compare with and outperform Density Functional based Tight Binding (DFTB) on both accuracy and computational resource. The implication is that ML models, given relevant data, are now at a level where they can be applied for downstream tasks in quantum chemistry transcending prediction of simple molecular features. Computer science ; Density functional theory ; Quantum ; Potential energy surface ; Artificial neural network ; Transition state ; Algorithm ; Statistical physics ; Artificial intelligence ; Molecule ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jeffrey Law | Shubham Pandey | Prashun Gorai | Peter C. St. John
Source: ChemRxiv (preprint, 2022-09-25)
Field: Materials Science — Materials Chemistry

Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully-relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128,000 DFT calculations comprising both fully-relaxed and volume-only relaxed structures. By minimizing the predicted upper-bound energy, we discover new stable structures with over 99% accuracy (versus DFT). We demonstrate the method by finding promising new candidates for solid-state battery (SSB) electrolytes that not only possess the required stability, but also additional functional properties such as large electrochemical stability windows and high conduction ion fraction. We expect this proposed framework to be directly applicable to a wide range of design challenges in materials science. Density functional theory ; Upper and lower bounds ; Stability (learning theory) ; Computer science ; Invariant (physics) ; Energy functional ; Energy minimization ; Minification ; A priori and a posteriori ; Graph theory ; Chemical stability ; Computational chemistry ; Mathematics ; Chemistry ; Physics ; Thermodynamics ; Machine learning ; Combinatorics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Sarah Bowman | Miranda L. Lynch
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

Advances using machine learning and computational tools for crystal growth and detection While there is much focus on how machine learning (ML) can assist analysis of structural datasets from diffraction and microscopy experiments, less recognized is the contribution of ML methods to upstream stages of the structure determination pipeline. A critical bottleneck in biomolecular crystallography is identifying conditions that will generate diffraction quality crystals. Imaging methods are key to the process of screening for successful crystallization hits and produce mineable data regarding the crystallization process. When crystallization screening is carried out in a high-throughput manner, the enormous number of images requires a large commitment of time and effort for locating successful crystallization conditions. To address this obstacle, machine learning approaches can be used to empower better detection of crystals. One successful example includes the MAchine Recognition of Crystallization Outcomes (MARCO) scoring algorithm, a deep learning convolutional neural network which was trained and tested on brightfield crystallization images in a collaborative effort between the National Crystallization Center at HWI, GoogleBrain, major pharmaceutical companies, and other crystallization facilities worldwide [1]. To further enhance crystal detection, we are working to integrate standard brightfield imaging with additional imaging modalities, including Ultraviolet Two-Photon Excited Fluorescence (UV-TPEF) and Second Harmonic Generation (SHG). We specifically are pursuing machine learning approaches to achieve combining the different information contributions of each imaging modality. Combining UV-TPEF and SHG enables detection of submicron crystals and crystals under precipitate, as well as distinguishes between protein and salt crystals. We have recently developed a Graphical User Interface called MARCO-Polo, which uses the MARCO algorithm on brightfield images coupled to a viewing platform for examining multiple imaging modalities, such as UV-TPEF and SHG Our current efforts are focused on ways to improve image processing and analysis for crystallization screening, with specific emphasis on machine learning approaches to combine the information from brightfield images and alterative imaging modalities to facilitate steps towards automated imaging to streamline crystal detection and acquisition. Computer science ; Materials science ; Crystal (programming language) ; Artificial intelligence ; Machine learning ; Programming language Crystallization and Solubility Studies
Authors: Ellie Hajizadeh | Zakiya Shireen | Hansani Weeratunge | Adrian Menzel | Andrew W. Phillips | Ronald G. Larson | Kate Smith‐Miles
Source: Research Square (preprint, 2022-06-13)
Field: Materials Science — Materials Chemistry

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer Granularity ; Computer science ; Polymer ; Artificial intelligence ; Mathematical optimization ; Materials science ; Mathematics ; Composite material ; Programming language Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization