jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stiv Llenga | Ganna Gryn’ova
Source: ChemRxiv (preprint, 2023-05-29)
Field: Materials Science — Materials Chemistry

Matrix of Orthogonalized Atomic Orbital Coefficients Representation for Radicals and Ions Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems, and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC – PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems. Atomic orbital ; Molecular orbital ; Chemistry ; Molecule ; Molecular graph ; Quantum chemistry ; Slater-type orbital ; Hamiltonian matrix ; Computational chemistry ; Atom (system on chip) ; Molecular physics ; Chemical physics ; Physics ; Molecular orbital theory ; Electron ; Quantum mechanics ; Eigenvalues and eigenvectors ; Computer science ; Symmetric matrix Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Gabriel Krenzer | Johan Klarbring | Kasper Tolborg | Hugo Rossignol | Andrew R. McCluskey | Benjamin J. Morgan | Aron Walsh
Source: ChemRxiv (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Nature of the Superionic Phase Transition of Lithium Nitride from Machine Learning Force Fields Superionic conductors have great potential as solid-state electrolytes, but the physics of type-II superionic transitions remains elusive. In this study, we employed molecular dynamics simulations, using machine learning force fields, to investigate the type-II superionic phase transition in α-Li3N. We characterised Li3N above and below the superionic phase transition by calculating the heat capacity, Li+ ion self-diffusion coefficient, and Li defect concentrations as functions of temperature. Our findings indicate that both the Li+ self-diffusion coefficient and Li vacancy concentration follow distinct Arrhenius relationships in the normal and superionic regimes. The activation energies for self-diffusion and Li vacancy formation decrease by a similar proportion across the superionic phase transition. This result suggests that the superionic transition may be driven by a change in defect formation behaviour, rather than changes in Li transport mechanism. This insight may hold implications for other type-II superionic materials. Fast ion conductor ; Phase transition ; Vacancy defect ; Lithium (medication) ; Arrhenius equation ; Diffusion ; Ion ; Condensed matter physics ; Electrolyte ; Chemical physics ; Materials science ; Phase (matter) ; Self-diffusion ; Activation energy ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Source: C&EN Global Enterprise (article, 2023-03-13)
Field: Materials Science — Materials Chemistry

Reactions Letters to the editor What ChatGPT means for chemistry Recent inroads made in chemistry seemingly have little to do with historical techniques that advanced the central science. The discovery of a new organic reaction or mechanism, invention of click chemistry, development of powerful light sources for X-rays, advent of 3D nuclear magnetic resonance and cryo-electron microscopy techniques, hyphenation of analytical techniques, and ab initio computation to solve chemical and biochemical problems seem “so yesterday.” Today, these rigorous and pejoratively “incremental” techniques have been broadsided by artificial intelligence, machine learning, and programs such as AlphaFold, which has predicted over 200 million structures to date . AI’s rapid growth has predictably resulted in the creation of an interactive graphical platform called ChatGPT. Its ramifications for those in academia and the chemical craft cannot be ignored. ChatGPT’s “dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect Computer science Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Chemistry and Chemical Engineering
Authors: Marco Eckhoff | Markus Reiher
Source: arXiv (Cornell University) (preprint, 2023-03-10)
Field: Materials Science — Materials Chemistry

Lifelong Machine Learning Potentials Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain the high accuracy, while inflicting little computational demands. On the downside, they need to be trained for each individual system. In recent years, a vast number of MLPs has been trained from scratch because learning additional data typically requires to train again on all data to not forget previously acquired knowledge. Additionally, most common structural descriptors of MLPs cannot represent efficiently a large number of different chemical elements. In this work, we tackle these problems by introducing element-embracing atom-centered symmetry functions (eeACSFs) which combine structural properties and element information from the periodic table. These eeACSFs are a key for our development of a lifelong machine learning potential (lMLP). Uncertainty quantification can be exploited to transgress a fixed, pre-trained MLP to arrive at a continuously adapting lMLP, because a predefined level of accuracy can be ensured. To extend the applicability of an lMLP to new systems, we apply continual learning strategies to enable autonomous and on-the-fly training on a continuous stream of new data. For the training of deep neural networks, we propose the continual resilient (CoRe) optimizer and incremental learning strategies relying on rehearsal of data, regularization of parameters, and the architecture of the model. Computer science ; Artificial intelligence ; Lifelong learning ; Machine learning ; Key (lock) ; Regularization (linguistics) ; Artificial neural network ; Table (database) ; Scratch ; Online machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rui Liu | Feng Tan | Nan Bai | Bo Ma | Hongbo Xiang | Jiaxin Gao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Intelligent Identification of Rice Growth Period (Gp)Based on Raman Spectroscopy and Improved Cnn in Heilongjiang Province of China China ; Raman spectroscopy ; Period (music) ; Identification (biology) ; Artificial intelligence ; Computer science ; History ; Physics ; Optics ; Botany ; Biology ; Archaeology ; Acoustics Spectroscopy and Chemometric Analyses
Authors: Antoine Herbeaux | Claire Maurice | Aurélien Villani | Jean‐Michel Bergheau | Helmut Klöcker
Source: SPIRE - Sciences Po Institutional REpository (article, 2023-09-03)
Field: Materials Science — Materials Chemistry

Combining Phase Field Modeling and Deep Learning for Accurate Modeling of Crystal Orientation in Solidification Microstructure obtained by Wire Arc Additive Manufacturing International audience Microstructure ; Materials science ; Arc (geometry) ; Phase (matter) ; Orientation (vector space) ; Numerical modeling ; Field (mathematics) ; Crystal (programming language) ; Mechanical engineering ; Metallurgy ; Computer science ; Geometry ; Geology ; Engineering ; Mathematics ; Physics ; Geophysics Solidification and crystal growth phenomena ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Joshua A. Vita | Dallas R. Trinkle
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Spline-based neural network interatomic potentials: blending classical and machine learning models While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are strictly necessary for constructing high-quality IPs. In this work, we introduce a new MLIP framework which blends the simplicity of spline-based MEAM (s-MEAM) potentials with the flexibility of a neural network (NN) architecture. The proposed framework, which we call the spline-based neural network potential (s-NNP), is a simplified version of the traditional NNP that can be used to describe complex datasets in a computationally efficient manner. We demonstrate how this framework can be used to probe the boundary between classical and ML IPs, highlighting the benefits of key architectural changes. Furthermore, we show that using spline filters for encoding atomic environments results in a readily interpreted embedding layer which can be coupled with modifications to the NN to incorporate expected physical behaviors and improve overall interpretability. Finally, we test the flexibility of the spline filters, observing that they can be shared across multiple chemical systems in order to provide a convenient reference point from which to begin performing cross-system analyses. Interpretability ; Computer science ; Artificial neural network ; Spline (mechanical) ; Flexibility (engineering) ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Lenz Fiedler | Normand A. Modine | Kyle D. Miller | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

Machine learning the electronic structure of matter across temperatures We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions. Electronic structure ; Range (aeronautics) ; Ionic bonding ; Observable ; Density functional theory ; Electronic density ; Artificial neural network ; Computer science ; Materials science ; Statistical physics ; Machine learning ; Physics ; Condensed matter physics ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; nanoparticles nucleation surface interactions
Authors: Wan‐Jian Yin | Guanjian Cheng | Xin-Gao Gong
Source: Research Square (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

De novo inverse materials design by combining optimization algorithm, universal potential and universal property model Inverse ; Property (philosophy) ; Algorithm ; Optimization algorithm ; Computer science ; Mathematical optimization ; Theoretical computer science ; Mathematics ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Shape Memory Alloy Transformations
Authors: Rochan Bajpai | Atharva Shukla | Janish Kumar | Abhishek Tewari
Source: ChemRxiv (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models The efficient search for crystals with targeted properties is a significant challenge in materials discovery. The rapidly growing field of materials informatics has so far primarily focused on the application of AI/ML models to predict the properties of known crystals from their fundamental and derived properties as descriptors. In the last few years, deep learning-based approaches have spawned a slew of innovative data-driven materials research applications. Materials scientists have used these techniques for the reverse engineering of crystal structures for target applications. However, one of the challenges has been the representation of the crystal structures in the machine readable format. Proposed representations in the literature lack in generality and scalability. In this paper, we train a conditional variational autoencoder with a scalable and invertible representation along with the elemental properties of the constituents as descriptors to inverse-design new crystal structures with specified attributes. When targeting formation energy, we show that our model predicts structures that are not in the complete OQMD database. Finally, we use first-principles density functional theory calculations to validate our findings and show that the developed model is able to generate novel crystal structures for targeted property, i.e. formation energy in this case. Scalability ; Crystal structure prediction ; Autoencoder ; Computer science ; Representation (politics) ; Generality ; Artificial intelligence ; Crystal (programming language) ; Theoretical computer science ; Property (philosophy) ; Invertible matrix ; Deep learning ; Algorithm ; Crystal structure ; Mathematics ; Database ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Takuya TANIGUCHI
Source: Nihon Kessho Gakkaishi (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Application of Materials Informatics for Organic Crystals Organic crystals have presented flexible features in contrast with inorganic crystals, and are expected for the future applications of optoelectronics and active matters. Pharmaceuticals are also the utilization of the organic crystal family. This article presents the basis of the data science in organic crystals, and a few examples of application of my research. An attempt is to screen the structural phase transition of organic crystals, and another topic is to compare structural representation of molecular and crystal structures regressed by graph neural network. Crystal (programming language) ; Materials science ; Representation (politics) ; Crystal structure ; Graph ; Computer science ; Crystallography ; Chemistry ; Theoretical computer science ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Juan C. Zapata Trujillo | Maria M. Pettyjohn | L. K. McKemmish
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Chemistry — Spectroscopy

High-throughput Quantum Chemistry: Empowering the Search for Molecular Candidates behind Unknown Spectral Signatures in Exoplanetary Atmospheres The identification of molecules in exoplanetary atmospheres is only possible thanks to the availability of high-resolution molecular spectroscopic data. However, due to its intensive and time-consuming generation process, at present, only on order 100 molecules have high-resolution spectroscopic data available, limiting new molecular detections. Using routine quantum chemistry calculations (i.e., scaled harmonic frequency calculations using the B97-1/def2-TZVPD model chemistry with median errors of 10cm-1), here we present a complementary high-throughput approach to rapidly generate approximate vibrational spectral data for 2743 molecules made from the biologically most important elements C, H, N, O, P and S. Though these data are not accurate enough to enable definitive molecular detections and does not seek to replace the need for high-resolution data, it has powerful applications in identifying potential molecular candidates responsible for unknown spectral features. We explore this application for the 4.1 micron (2439cm-1) feature in the atmospheric spectrum of WASP-39b, listing potential alternative molecular species responsible for this spectral line, together with SO2. Further applications of this big data compilation also include identifying molecules with strong absorption features that are likely detectable at quite low abundances, and training set for machine learning predictions of vibrational frequencies. Characterising exoplanetary atmospheres through molecular spectroscopy is essential to understand the planet's physico-chemical processes and likelihood of hosting life. Our rapidly generated quantum chemistry big data set will play a crucial role in supporting this understanding by giving directions into possible initial identifications of the more unusual molecules to emerge. Quantum chemistry ; Molecule ; Quantum ; Chemistry ; Physics ; Computer science ; Chemical physics ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Molecular Spectroscopy and Structure
Authors: Xuan Zhang | Cheng Chen | Xiaoting Wang | Haitao Jiang | Wei Zhao | Xuefeng Cui
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

MetaGIN: A Lightweight Framework for Molecular Property Prediction Abstract Recent advancements in AI-based synthesis of small molecules have led to the creation of extensive databases, housing billions of small molecules. Given this vast scale, traditional quantum chemistry (QC) methods become inefficient for determining the chemical and physical properties of such an extensive array of molecules. As these properties are key to new drug research and development, deep learning techniques have recently been developed. Here, we present MetaGIN, a lightweight framework designed for molecular property prediction. While traditional GNN models with 1-hop edges (i.e., covalent bonds) are sufficient for abstract graph representation, they are inadequate for capturing 3D features. Our MetaGIN model shows that including 2-hop and 3-hop edges (representing bond and torsion angles, respectively) is crucial to fully comprehend the intricacies of 3D molecules. Moreover, MetaGIN is a streamlined model with fewer than 10 million parameters, making it ideal for fine-tuning on a single GPU. It also adopts the widely acknowledged MetaFormer framework, which has consistently shown high accuracy in many computer vision tasks. Through our experiments, MetaGIN achieves a mean absolute error (MAE) of 0.0851 with just 8.87M parameters on the PCQM4Mv2 dataset. Furthermore, MetaGIN outperforms leading techniques, showcasing superior performance across several datasets in the MoleculeNet benchmark. Computer science ; Benchmark (surveying) ; Quantum chemical ; Property (philosophy) ; Molecular graph ; Graph ; Theoretical computer science ; Covalent bond ; Molecule ; Algorithm ; Artificial intelligence ; Data mining ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Colton C. Seegmiller | Sterling G. Baird | Hasan M. Sayeed | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-30)
Field: Materials Science — Materials Chemistry

Discovering Chemically Novel, High-TemperatureSuperconductors One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions [1]. This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process. Superconductivity ; Cuprate ; Curse of dimensionality ; High-temperature superconductivity ; Cluster analysis ; Set (abstract data type) ; Computer science ; Chemical space ; Materials science ; Physics ; Machine learning ; Condensed matter physics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography