jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenru Duan | B. K. Chu Daniel | Aditya Nandy | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-01-11)
Field: Materials Science — Materials Chemistry

Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS is carried out with approximate density functional theory (DFT) using a single functional. Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates MR effect on chemical property prediction is not well established. We evaluate MR diagnostics of over 10,000 transition metal complexes (TMCs) and compare to those in organic molecules. We reveal that only some MR diagnostics are transferable across these materials spaces. By studying the influence of MR character on chemical properties (i.e., MR effect) that involves multiple potential energy surfaces (i.e., adiabatic spin splitting, $ΔE_\mathrm{H-L}$, and ionization potential, IP), we observe that cancellation in MR effect outweighs accumulation. Differences in MR character are more important than the total degree of MR character in predicting MR effect in property prediction. Motivated by this observation, we build transfer learning models to directly predict CCSD(T)-level adiabatic $ΔE_\mathrm{H-L}$ and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving chemical accuracy (i.e., 1 kcal/mol) for robust VHTS. Character (mathematics) ; Property (philosophy) ; Adiabatic process ; Computer science ; Ionization ; Transfer of learning ; Molecule ; Artificial intelligence ; Biological system ; Physics ; Machine learning ; Mathematics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: X. L. Lei | Weike Ye | Muratahan Aykol | Joseph H. Montoya
Source: ChemRxiv (preprint, 2022-02-08)
Field: Materials Science — Materials Chemistry

Novel inorganic crystal structures predicted using autonomous simulation agents We report a dataset of 96,962 new crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery). Of these, 931 are within 1 meV/atom of the convex hull and 27,075 are within 200 meV/atom of the convex hull. The dataset contains DFT-optimized pymatgen crystal structure objects, DFT-computed formation energies and phase stability calculations from the convex hull. It contains a variety of spacegroups and symmetries derived from crystal prototypes derived from known experimental compounds, and was generated from active learning campaigns of various chemical systems. This dataset can be used to benchmark future active-learning or generative efforts for structure prediction, to seed new efforts of experimental crystal structure discovery, or to construct new models of structure-property relationships. Convex hull ; Benchmark (surveying) ; Crystal (programming language) ; Stability (learning theory) ; Crystal structure ; Density functional theory ; Atom (system on chip) ; Workflow ; Property (philosophy) ; Computer science ; Regular polygon ; Algorithm ; Crystallography ; Materials science ; Artificial intelligence ; Mathematics ; Chemistry ; Computational chemistry ; Geometry ; Machine learning ; Geography ; Database ; Cartography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface Chemistry and Catalysis
Authors: Sana Bougueroua | Marie Bricage | Ylène Aboulfath | Dominique Barth | Marie‐Pierre Gaigeot
Source: ChemRxiv (preprint, 2022-11-03)
Field: Materials Science — Materials Chemistry

Algorithmic graph theory, reinforcement learning and game theory in MD simulations: from 3D-structures to topological 2D-MolGraphs and backwards This paper reviews graph theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers sampled over time but also allows to follow the interconversions between the conformers through graphs of transitions in time. Examples of gas phase molecules and inhomogeneous aqueous solid interfaces are presented to demonstrate the power of topological 2D graphs and their versatility for post-processing molecular dynamics trajectories. An even more complex challenge is to predict 3D structures from topological 2D graphs. Our first attempts to tackle such a challenge are presented with the development of game theory and reinforcement learning methods for predicting the 3D structure of a gas-phase peptide. Reinforcement learning ; Graph theory ; Computer science ; Game theory ; Conformational isomerism ; Random graph ; Topology (electrical circuits) ; Graph ; Molecular dynamics ; Theoretical computer science ; Gas phase ; Artificial intelligence ; Molecule ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Combinatorics ; Quantum mechanics ; Mathematical economics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaoyuan Liu | Ilya Tyagin | Hayato Ushijima‐Mwesigwa | Indradeep Ghosh | Ilya Safro
Source: arXiv (Cornell University) (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Towards Practical Explainability with Cluster Descriptors With the rapid development of machine learning, improving its explainability has become a crucial research goal. We study the problem of making the clusters more explainable by investigating the cluster descriptors. Given a set of objects $S$, a clustering of these objects $π$, and a set of tags $T$ that have not participated in the clustering algorithm. Each object in $S$ is associated with a subset of $T$. The goal is to find a representative set of tags for each cluster, referred to as the cluster descriptors, with the constraint that these descriptors we find are pairwise disjoint, and the total size of all the descriptors is minimized. In general, this problem is NP-hard. We propose a novel explainability model that reinforces the previous models in such a way that tags that do not contribute to explainability and do not sufficiently distinguish between clusters are not added to the optimal descriptors. The proposed model is formulated as a quadratic unconstrained binary optimization problem which makes it suitable for solving on modern optimization hardware accelerators. We experimentally demonstrate how a proposed explainability model can be solved on specialized hardware for accelerating combinatorial optimization, the Fujitsu Digital Annealer, and use real-life Twitter and PubMed datasets for use cases. Cluster analysis ; Computer science ; Disjoint sets ; Set (abstract data type) ; Pairwise comparison ; Constraint (computer-aided design) ; Cluster (spacecraft) ; Object (grammar) ; Optimization problem ; Machine learning ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Algorithm ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and Data Classification
Authors: Jinxing Liang
Source: Trends in Textile Engineering & Fashion Technology (article, 2022-04-20)
Field: Materials Science — Polymers and Plastics

Survey of Fabric Simulation Trends in Textile Engineering & Fashion Technology Survey of Fabric Simulation Xinrong Hu, Chen Yang, Jinxing Liang*, Tao Peng And Jin Huang (Engineering Research Center of Hubei Province for Clothing Information,Wuhan,China) (School of Computer Science and Artificial Intelligence, Wuhan Textile University,Wuhan,China) *Corresponding author:Jinxing Liang, Engineering Research Center of Hubei Province for Clothing Information, Wuhan, China Submission: April 01, 2022; Published: April 20, 2022 DOI: 10.31031/TTEFT.2022.06.000644 ISSN 2578-0271 Volume 6 Issue 4 China ; Clothing ; Textile ; Engineering ; Research center ; Center (category theory) ; Information center ; Library science ; Geography ; Computer science ; Political science ; Archaeology ; Psychology ; Mathematics education Textile materials and evaluations
Authors: Rohan Casukhela | Sriram Vijayan | Joerg R. Jinschek | Stephen R. Niezgoda
Source: arXiv (Cornell University) (preprint, 2022-06-14)
Field: Materials Science — Materials Chemistry

A Framework for the Optimal Selection for High-Throughput Data Collection Workflows by Autonomous Experimentation Systems Autonomous experimentation systems have been used to greatly advance the integrated computational materials engineering (ICME) paradigm. This paper outlines a framework that enables the design and selection of data collection workflows for autonomous experimentation systems. The framework first searches for data collection workflows that generate high-quality information and then selects the workflow that generates the \emph{best, highest-value} information as per a user-defined objective. We employ this framework to select the \emph{user-defined best} high-throughput workflow for material characterization on an additively manufactured Ti-6Al-4V sample for the purposes of outlining a basic materials characterization scenario, reducing the collection time of backscattered electron scanning electron scanning electron microscopy images by a factor of 5 times as compared to the benchmark workflow for the case study presented, and by a factor of 85 times as compared to the workflow used in the previously published study. Workflow ; Benchmark (surveying) ; Computer science ; Selection (genetic algorithm) ; Throughput ; Data collection ; Sample (material) ; Characterization (materials science) ; Factor (programming language) ; Data mining ; Software engineering ; Database ; Artificial intelligence ; Operating system ; Nanotechnology ; Materials science ; Programming language ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Chang Jie Leong | Andre K. Y. Low | Jose Recatala-Gomez | Pablo Quijano Velasco | Eléonore Vissol-Gaudin | Jin Da Tan | Balamurugan Ramalingam | Riko I Made | Shreyas Dinesh Pethe | Saumya Sebastian | Yee-Fun Lim | Zi Hui Jonathan Khoo | Yang Bai | Jayce Jian Wei Cheng | Kedar Hippalgaonkar
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An Object-Oriented Framework to Enable Workflow Evolution Across Materials Acceleration Platforms Workflow ; Computer science ; Acceleration ; Systems engineering ; Software engineering ; Engineering ; Database ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Bojing Zhang | Leon Fischer | Alexey O. Sanin | Helge S. Stein
Source: ChemRxiv (preprint, 2022-05-27)
Field: Materials Science — Materials Chemistry

Robotic cell assembly to accelerate battery research Manual cell assembly confounds with research digitalization and reproducibility. Both are however needed for data-driven optimization of cell chemistries and charging protocols. Therefore, we present herein an automatic battery assembly system (AutoBASS) that is capable of assembling batches of up to 64 CR2023 cells. AutoBASS allows us to acquire large datasets on in-house developed chemistries and is herein demonstrated with NMC811 and Si@Graphite electrodes with a focus on formation and manufacturing data. The large dataset enables us to gain insights into the formation process through dQ/dV analysis and assess cell to cell variability. Exact robotic electrode placement provides a baseline for laboratory-scale manufacturing and reproducibility towards the accelerated translation of findings from the laboratory to the pilot plant scale. Battery (electricity) ; Reproducibility ; Computer science ; Electrode ; Process (computing) ; Scale (ratio) ; Focus (optics) ; Translation (biology) ; Nanotechnology ; Embedded system ; Materials science ; Chemistry ; Power (physics) Machine Learning in Materials Science
Authors: Ji Wei Yoon | Adithya Kumar | Pawan Kumar | Kedar Hippalgaonkar | J. Senthilnath | Vijila Chellappan
Source:  (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

Enabling high-throughput electrical conductivity optimization of doped conjugated polymers using explainable machine learning The recent advent of workflows involving high-throughput experimentation techniques, in combination with machine learning optimization, has enabled the accelerated discovery of materials with state-of-the-art properties. However, there remains many other workflows which require measurements of quantities that cannot be easily automated, thereby limiting discovery. In particular, the optimization of the electrical conductivity of doped polymer materials requires laborious measurements. Here, we propose a workflow which involves a data-driven approach using a pair of classification and regression models to reduce the need for manual intervention. The first model classifies the samples at an accuracy of 100% for conductivity &gt;~ 25 to 100 S/cm. We predicted the conductivities of the smaller subset of samples using a second model, which has test R 2 of 0.984. To validate the approach, we showed that the models, neither trained on the samples with the two highest conductivities of 498 and 506 S/cm, were able to, in an extrapolative manner, correctly classify and predict them at errors of 137 and 140 S/cm, respectively. The workflow results in an improvement in measurement efficiency by 59%, which is 89% of the maximum achievable. Concurrently, we addressed the typical lack of explainability of machine-learned models by engineering bespoke features and coupling them with carefully selected models, and derived insights on the spectral influence of conductivity. This study offers a way to accelerate the optimization of doped polymer materials and demonstrates the insights that can be gained with deliberate uses of machine learning in experimental science. Workflow ; Computer science ; Machine learning ; Bespoke ; Artificial intelligence ; Throughput ; Conductivity ; Materials science ; Database ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and Data Classification
Authors: Kirby Broderick | Eric M. Lopato | Brook Wander | Stefan Bernhard | John R. Kitchin | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2022-07-20)
Field: Materials Science — Materials Chemistry

Identifying Limitations in Screening High-Throughput Photocatalytic Bimetallic Nanoparticles with Machine-Learned Hydrogen Adsorptions The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5,300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. About half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but these tools may become significantly more powerful when combined with other screening tools. Bimetallic strip ; Adsorption ; Workflow ; Catalysis ; Computer science ; Nanoparticle ; Throughput ; Nanotechnology ; Hydrogen ; Materials science ; Process engineering ; Chemistry ; Engineering ; Physical chemistry ; Database Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Kirby Broderick | Eric M. Lopato | Brook Wander | Stefan Bernhard | John R. Kitchin | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2022-06-16)
Field: Materials Science — Materials Chemistry

Hydrogen Adsorption Energy Necessary but Not Sufficient for HER Catalysis: Connecting Machine-Learned Descriptors with High-Throughput Experimental Catalysis over Bimetallic Nanoparticles The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity \emph{in silico} at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5,300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. About half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but these tools may become significantly more powerful when combined with other screening tools. Bimetallic strip ; Adsorption ; Catalysis ; Workflow ; Computer science ; Nanoparticle ; Throughput ; Hydrogen ; Nanotechnology ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry ; Database Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Andre K. Y. Low | Eléonore Vissol-Gaudin | Yee‐Fun Lim | Kedar Hippalgaonkar
Source:  (preprint, 2022-09-27)
Field: Materials Science — Materials Chemistry

Bayesian vs Evolutionary Optimisation in Exploring Pareto Fronts for Materials Discovery &lt;p&gt; With advancements in automated experimental setups, material optimisation and discovery can scale to higher throughput with larger evaluation budgets. Two state-of-the-art algorithms with conceptually different multi-objective optimisation strategies (Bayesian and Evolutionary) are compared on synthetic and real-world datasets. Our results show that the Bayesian optimisation strategy, q-Noisy Expected Hypervolume Improvement (qNEHVI) is superior in finding solutions at the Pareto Front rapidly, and when considering hypervolume improvement as a performance indicator. On the other hand, the Evolutionary optimisation strategy, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III), can exploit the Pareto Front and propose a larger pool of optimal solutions, given sufficient evaluation budget, and thus may be a better choice for materials discovery problems where knowing the complete Pareto Front provides greater scientific value to understanding materials space. We discuss the limitations of using hypervolume as a performance indicator for optimisation strategies, alongside hypervolume-based strategies such as qNEHVI, which do not adequately explain the number of solutions at or near the Pareto Front. We also performed a comparison of both optimisation strategies at different batch sizes to consider throughput capabilities. &lt;/p&gt; Sorting ; Multi-objective optimization ; Bayesian optimization ; Pareto principle ; Bayesian probability ; Evolutionary algorithm ; Computer science ; Mathematical optimization ; Genetic algorithm ; Throughput ; Exploit ; Machine learning ; Artificial intelligence ; Mathematics ; Algorithm Machine Learning in Materials Science
Authors: Guangqi Wu | Haisen Zhou | Jun Zhang | Zi‐You Tian | Xingyi Liu | Shuo Wang | Connor W. Coley | Hua Lu
Source: ChemRxiv (preprint, 2022-08-04)
Field: Chemistry — Organic Chemistry

A High-Throughput Platform for Efficient Exploration of Functional polypeptides Chemical Space Rapid and in-depth exploration of the chemical space of high molecular weight synthetic polypeptides via the ring-opening polymerization (ROP) of N-carboxyanhydride (NCA) is a viable approach towards protein mimics and functional biomaterials. The traditional synthetic workflow, however, has been labour-intensive and with limited throughput. Here, we develop an efficient chemistry for the high throughput diversification of polypeptides based on a click-like reaction between selenolate and various electrophiles in aqueous solutions. Importantly, the platform is amenable to automation, which allows rapid generation of up to 1200 homopolypeptides or random heteropolypeptides (RHP) within one day. With the assistance of machine learning, iterative exploration of the RHP library efficiently and effectively identifies candidates with improved glutathione peroxidase-like activity from complex chemical space of which we have little prior knowledge. This automated and high-throughput platform provides potential solutions to unmet challenges such as de novo design of artificial enzyme, biomacromolecule delivery, and understanding of intrinsically disordered proteins. Chemical space ; Throughput ; Computer science ; Workflow ; Click chemistry ; Chemistry ; On the fly ; Combinatorial chemistry ; Automation ; Nanotechnology ; Materials science ; Drug discovery ; Engineering ; Biochemistry Click Chemistry and Applications ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization