jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janosh Riebesell | Rhys E. A. Goodall | Philipp Benner | Chiang, Yuan | Deng, Bowen | Ceder, Gerbrand | Asta, Mark | Alpha A. Lee | Anubhav Jain | Kristin A. Persson
Source: arXiv (Cornell University) (preprint, 2023-08-28)
Field: Materials Science — Materials Chemistry

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery, an evaluation framework for ML energy models, applied as pre-filters for high-throughput searches of stable inorganic crystals. This framework addresses the disconnect between thermodynamic stability and formation energy, as well as retrospective vs. prospective benchmarking in materials discovery. We release a Python package to support model submissions and maintain an online leaderboard, offering insights into performance trade-offs. To identify the best-performing ML methodologies for materials discovery, we benchmarked various approaches, including random forests, graph neural networks (GNNs), one-shot predictors, iterative Bayesian optimizers, and universal interatomic potentials (UIP). Our initial results rank models by test set F1 scores for thermodynamic stability prediction: EquiformerV2 + DeNS &gt; Orb &gt; SevenNet &gt; MACE &gt; CHGNet &gt; M3GNet &gt; ALIGNN &gt; MEGNet &gt; CGCNN &gt; CGCNN+P &gt; Wrenformer &gt; BOWSR &gt; Voronoi fingerprint random forest. UIPs emerge as the top performers, achieving F1 scores of 0.57-0.82 and discovery acceleration factors (DAF) of up to 6x on the first 10k stable predictions compared to random selection. We also identify a misalignment between regression metrics and task-relevant classification metrics. Accurate regressors can yield high false-positive rates near the decision boundary at 0 eV/atom above the convex hull. Our results demonstrate UIPs' ability to optimize computational budget allocation for expanding materials databases. However, their limitations remain underexplored in traditional benchmarks. We advocate for task-based evaluation frameworks, as implemented here, to address these limitations and advance ML-guided materials discovery. Random forest ; Machine learning ; Computer science ; Stability (learning theory) ; Artificial intelligence ; Python (programming language) ; Crystal structure prediction ; Voronoi diagram ; Test set ; Convex hull ; Data mining ; Algorithm ; Mathematics ; Regular polygon ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mohammad Javad Hooshmand | Chowdhury Sakib-Uz-Zaman | Mohammad Abu Hasan Khondoker
Source: Materials (article, 2023-11-15)
Field: Materials Science — Polymers and Plastics

Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam Polymer foams are extensively utilized because of their superior mechanical and energy-absorbing capabilities; however, foam materials of consistent geometry are difficult to produce because of their random microstructure and stochastic nature. Alternatively, lattice structures provide greater design freedom to achieve desired material properties by replicating mesoscale unit cells. Such complex lattice structures can only be manufactured effectively by additive manufacturing or 3D printing. The mechanical properties of lattice parts are greatly influenced by the lattice parameters that define the lattice geometries. To study the effect of lattice parameters on the mechanical stiffness of lattice parts, 360 lattice parts were designed by varying five lattice parameters, namely, lattice type, cell length along the X, Y, and Z axes, and cell wall thickness. Computational analyses were performed by applying the same loading condition on these lattice parts and recording corresponding strain deformations. To effectively capture the correlation between these lattice parameters and parts' stiffness, five machine learning (ML) algorithms were compared. These are Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), all ML algorithms exhibited significantly low prediction errors during the training and testing phases; however, the Taylor diagram demonstrated that ANN surpassed other algorithms, with a correlation coefficient of 0.93. That finding was further supported by the relative error box plot and by comparing actual vs. predicted values plots. This study revealed the accurate prediction of the mechanical stiffness of lattice parts for the desired set of lattice parameters. Materials science ; Composite material ; Stiffness ; Lattice (music) ; Polymer ; Algorithm ; Computer science ; Mechanical engineering ; Engineering ; Physics ; Acoustics Polymer Foaming and Composites ; Polymer composites and self-healing ; Cellular and Composite Structures
Authors: Xue-Ting Fan | Xiaojian Wen | Yong‐Bin Zhuang | Jun Cheng
Source: Journal of Energy Chemistry (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics Molecular dynamics ; Interface (matter) ; Dynamics (music) ; Chemical physics ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Psychology ; Physical chemistry ; Computational chemistry ; Adsorption ; Pedagogy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electronic and Structural Properties of Oxides
Authors: Lijing Wang | Shan Gao | Wenting Li | Ao Zhu | Huan Li | Chunning Zhao | Haijun Zhang | Weihua Wang | Weichao Wang
Source: Journal of Power Sources (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Machine learning assisted screening of MXenes pseudocapacitive materials MXenes ; Pseudocapacitance ; Electronegativity ; Carbide ; Materials science ; Density functional theory ; Computer science ; Nanotechnology ; Chemistry ; Supercapacitor ; Physics ; Computational chemistry ; Physical chemistry ; Composite material ; Quantum mechanics ; Capacitance MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Songyuan Yao | Richard Van | Xiaoliang Pan | Ji Hwan Park | Yuezhi Mao | Jingzhi Pu | Ye Mei | Yihan Shao
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen <i>et al.</i>, <i>J. Chem. Phys.</i>, 2021, <b>155</b>, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol<sup>-1</sup> Å<sup>-1</sup> from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol<sup>-1</sup>. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for <i>ab initio</i>-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute. Molecular dynamics ; Solvent ; Aqueous solution ; Dipeptide ; Dynamics (music) ; Computer science ; Chemistry ; Computational chemistry ; Organic chemistry ; Amino acid ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Julius B. Kleine Büning | Stefan Grimme
Source: Journal of Chemical Theory and Computation (article, 2023-06-01)
Field: Chemistry — Spectroscopy

Computation of CCSD(T)-Quality NMR Chemical Shifts via Δ-Machine Learning from DFT NMR spectroscopy undoubtedly plays a central role in determining molecular structures across different chemical disciplines, and the accurate computational prediction of NMR parameters is highly desirable. In this work, a new Δ-machine learning approach is presented to correct DFT-computed NMR chemical shifts using input features from the calculation and in addition highly accurate reference data at the CCSD(T)/pcSseg-2 level of theory with a basis set extrapolation scheme. The model is trained on a data set containing 1000 optimized and geometrically distorted structures of small organic molecules comprising most elements of the first three periods and containing data for 7090 <sup>1</sup>H and 4230 <sup>13</sup>C NMR chemical shifts. Applied to the PBE0/pcSseg-2 method, the mean absolute deviation (MAD) on the internal NMR shift test set is reduced by 81% for <sup>1</sup>H and 92% for <sup>13</sup>C at virtually no additional computational cost. For 12 different DFT functional and basis set combinations, the MAD of the ML-corrected NMR shifts ranges from 0.021 to 0.039 ppm (<sup>1</sup>H) and from 0.38 to 1.07 ppm (<sup>13</sup>C). Importantly, the new method consistently outperforms the simple and widely used linear regression correction technique. This behavior is reproduced on three different external benchmark sets, confirming the generality and robustness of the correction scheme, which can easily be applied in DFT-based spectral simulations. Chemical shift ; Extrapolation ; Test set ; Basis set ; Computer science ; Robustness (evolution) ; Density functional theory ; Basis (linear algebra) ; Computation ; Algorithm ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Physical chemistry ; Statistics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Simone Manti | Mark Kamper Svendsen | Nikolaj R. Knøsgaard | Peder Lyngby | Kristian S. Thygesen
Source: npj Computational Materials (article, 2023-03-04)
Field: Materials Science — Materials Chemistry

Exploring and machine learning structural instabilities in 2D materials Abstract We address the problem of predicting the zero-temperature dynamical stability (DS) of a periodic crystal without computing its full phonon band structure. Here we report the evidence that DS can be inferred with good reliability from the phonon frequencies at the center and boundary of the Brillouin zone (BZ). This analysis represents a validation of the DS test employed by the Computational 2D Materials Database (C2DB). For 137 dynamically unstable 2D crystals, we displace the atoms along an unstable mode and relax the structure. This procedure yields a dynamically stable crystal in 49 cases. The elementary properties of these new structures are characterized using the C2DB workflow, and it is found that their properties can differ significantly from those of the original unstable crystals, e.g., band gaps are opened by 0.3 eV on average. All the crystal structures and properties are available in the C2DB. Finally, we train a classification model on the DS data for 3295 2D materials in the C2DB using a representation encoding the electronic structure of the crystal. We obtain an excellent receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.90, showing that the classification model can drastically reduce computational efforts in high-throughput studies. Brillouin zone ; Phonon ; Crystal (programming language) ; Stability (learning theory) ; Representation (politics) ; Crystal structure ; Workflow ; Computer science ; Boundary (topology) ; Algorithm ; Artificial intelligence ; Materials science ; Physics ; Mathematics ; Machine learning ; Condensed matter physics ; Mathematical analysis ; Crystallography ; Chemistry ; Database Thermal properties of materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Kevin Maik Jablonka | Philippe Schwaller | Andres Ortega‐Guerrero | Berend Smit
Source: ChemRxiv (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

Is GPT all you need for low-data discovery in chemistry? Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry lead to various sophisticated machine-learning approaches that incorporate chemical knowledge for each application and therefore require a lot of expertise to develop. Here, we show that large language models that have been trained on vast amounts of text extracted from the internet can easily be adapted to solve various tasks in chemistry and materials science by simply prompting them with chemical questions in natural language. We compared this approach with dedicated machine-learning models for many applications spanning properties of molecules and materials to the yield of chemical reactions. Surprisingly, we find this approach performs comparable to or even outperforms the conventional techniques, particularly in the low data limit. In addition, by simply inverting the questions, we can even perform inverse design successfully. The high performance, especially for small data sets, combined with the ease of use, can have a fundamental impact on how we leverage machine learning in the chemical and material sciences. Next to a literature search, querying a foundational model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundational models. Computer science ; Leverage (statistics) ; Artificial intelligence ; Data science ; Machine learning ; Chemical space ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Syed Shoaib Ahmad Shah | Hafiza Komal Zafar | Muhammad Sufyan Javed | Muhammad Aizaz Ud Din | Saleh S. Alarfaji | Georgia Balkourani | Manzar Sohail | Panagiotis Tsiakaras | Tayyaba Najam
Source: Coordination Chemistry Reviews (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Mxenes for Zn-based energy storage devices: Nano-engineering and machine learning MXenes ; Supercapacitor ; Energy storage ; Nanotechnology ; Chemistry ; Process engineering ; Electrode ; Materials science ; Capacitance ; Engineering ; Physics MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced battery technologies research
Authors: Bita Yarahmadi | Seyed Majid Hashemianzadeh | Seyed Mohammad-Reza Milani Hosseini
Source: Scientific Reports (article, 2023-07-26)
Field: Chemistry — Analytical Chemistry

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K<sub>MIP</sub>), and the distribution coefficient of the non-imprinted polymer (K<sub>NIP</sub>). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R<sup>2</sup> value (R<sup>2</sup> = 0.871), and the minimum mean absolute error (MAE = - 0.982), and mean square error were obtained (MSE = - 2.303). Computer science ; Support vector machine ; Algorithm ; Molecularly imprinted polymer ; Decision tree ; Random forest ; Feature selection ; Ensemble learning ; Machine learning ; Artificial intelligence ; Selectivity ; Chemistry Analytical chemistry methods development ; Microfluidic and Capillary Electrophoresis Applications ; Advanced Chemical Sensor Technologies
Authors: Junyang Hu | Zean Li | Jiaping Lin | Liangshun Zhang
Source: ACS Applied Materials & Interfaces (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks Establishing the structure-property relationship by machine learning (ML) models is extremely valuable for accelerating the molecular design of polymers. However, existing ML models for the polymers are subject to scarcity issues of training data and fewer variations of graph structures of molecules. In addition, limited works have explored the interpretability of ML models to infer the latent knowledge in the field of polymer science that could inspire ML-assisted molecular design. In this contribution, we integrate graph convolutional neural networks (GCNs) with data augmentation strategy to predict the glass transition temperature <i>T</i><sub>g</sub> of polymers. It is demonstrated that the data-augmented GCN model outperforms the conventional models and achieves a higher accuracy for the prediction of <i>T</i><sub>g</sub> despite a small amount of training data. Furthermore, taking advantage of molecular graph representations, the data-augmented GCN model has the capability to infer the importance of atoms or substructures from the understanding of <i>T</i><sub>g</sub>, which generally agrees with the experimental findings in the field of polymer science. The inferred knowledge of the GCN model is used to advise on the design of functional polymers with specific <i>T</i><sub>g</sub>. The data-augmented GCN model possesses prominent superiorities in the establishment of structure-property relationship and also provides an efficient way for accelerating the rational design of polymer molecules. Interpretability ; Computer science ; Polymer ; Graph ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Materials science ; Rational design ; Machine learning ; Theoretical computer science ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Jingbai Li | Steven A. Lopez
Source: Chemical Physics Reviews (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Machine learning accelerated photodynamics simulations Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QM calculations of excited-state potential energies surfaces that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QM calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QM calculations and ML techniques. We, then, discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism. QM/MM ; Isomerization ; Solvation ; Chemistry ; Molecular dynamics ; Cycloaddition ; Computational chemistry ; Quantum ; Adiabatic process ; Surface hopping ; Potential energy surface ; Reaction mechanism ; Molecule ; Chemical physics ; Computer science ; Physics ; Quantum mechanics ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Yujun Xie | Jingyang Wang | Benjamin H. Savitzky | Zheng Chen | Yu Wang | Sophia B. Betzler | Karen C. Bustillo | Kristin A. Persson | Yi Cui | Lin‐Wang Wang | Colin Ophus | Peter Ercius | Haimei Zheng
Source: Science Advances (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Spatially resolved structural order in low-temperature liquid electrolyte Determining the degree and the spatial extent of structural order in liquids is a grand challenge. Here, we are able to resolve the structural order in a model organic electrolyte of 1 M lithium hexafluorophosphate (LiPF<sub>6</sub>) dissolved in 1:1 (v/v) ethylene carbonate:diethylcarbonate by developing an integrated method of liquid-phase transmission electron microscopy (TEM), cryo-TEM operated at -30°C, four-dimensional scanning TEM, and data analysis based on deep learning. This study reveals the presence of short-range order (SRO) in the high-salt concentration domains of the liquid electrolyte from liquid phase separation at the low temperature. Molecular dynamics simulations suggest the SRO originates from the Li<sup>+</sup>-(PF<sub>6</sub><sup>-</sup>)<i><sub>n</sub></i> (<i>n</i> > 2) local structural order induced by high LiPF<sub>6</sub> salt concentration. Electrolyte ; Ethylene carbonate ; Hexafluorophosphate ; Salt (chemistry) ; Transmission electron microscopy ; Lithium (medication) ; Materials science ; Phase (matter) ; Chemical physics ; Ionic liquid ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Physical chemistry ; Electrode Solid-state spectroscopy and crystallography ; Chemical and Physical Properties in Aqueous Solutions ; Thermodynamic properties of mixtures