Research landscape

viewof visible_topics = Inputs.checkbox(
  topic_names,
  { value: topic_names,
    label: "Topics:"
  }
)

Topics:

space | ml | chemicaldispersion | potential | intermolecularkinetic | model | simulation | | data | science | researchsequence | peptide | growthcardiolipin | mitochondrial | selectivitymembrane | lipid | asymmetrypeptide | folding | insertionmodel | cg | coarsegrainedtwostate | analysis | cooperativitypermeability | permeation | meandynamic | chemistry | molecularreweighting | dynamical | implementationmodeling | polymer | landscapesystem | md | reactions

visible_topics = Array(16) ["space | ml | chemical", "dispersion | potential | intermolecular", "kinetic | model | simulation", " | | ", "data | science | research", "sequence | peptide | growth", "cardiolipin | mitochondrial | selectivity", "membrane | lipid | asymmetry", "peptide | folding | insertion", "model | cg | coarsegrained", "twostate | analysis | cooperativity", "permeability | permeation | mean", "dynamic | chemistry | molecular", "reweighting | dynamical | implementation", "modeling | polymer | landscape", "system | md | reactions"]

Plot

Plot.plot({
  grid: true,
  color: {
    scheme: "spectral",
    domain: topic_names,
    legend: true
  },
  x: {
    label: ""
  },
  y: {
    label: ""
  },
  marks: [
    Plot.dot(
        filtered,
        {
            x: "embedding.x",
            y: "embedding.y",
            title: (d) =>
        `${d.doi}\n ${d.title}\n ${d['journal.abbreviation']} ${d['journal.volume']} (${d['year']})`,
            fill: "embedding.topic_name",
            r: 6,
        }
    )
  ],
  tooltip: {
    stroke: "blue",
  }
})

space | ml | chemicaldispersion | potential | intermolecularkinetic | model | simulation | | data | science | researchsequence | peptide | growthcardiolipin | mitochondrial | selectivitymembrane | lipid | asymmetrypeptide | folding | insertionmodel | cg | coarsegrainedtwostate | analysis | cooperativitypermeability | permeation | meandynamic | chemistry | molecularreweighting | dynamical | implementationmodeling | polymer | landscapesystem | md | reactions

Data

Inputs.table(filtered, {
  columns: [
    "doi",
    "title",
    "journal.name",
    "journal.volume",
    "year",
  ],
  header: {
    "journal.name": "journal",
    "journal.volume": "volume",
  },
  format: {
    doi: doi => htl.html`<a href=https://doi.org/${doi} target=_blank>${doi}</a>`,
    title: title => htl.html`${title}`,
    "journal.volume": volume => htl.html`<b>${volume}</b>`,
    year: year => htl.html`${year}`,
  },
  width: {
    "title": 10
  },
  layout: "fixed",
})

	doi	title	journal	volume	year
	10.1039/D5SC03855C	Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining	Chemical Science	16	2025
	10.1063/5.0251736	Solvation free energies from neural thermodynamic integration	The Journal of Chemical Physics	162	2025
	10.1021/acs.jcim.4c01754	Martignac: Computational Workflows for Reproducible, Traceable, and Composable Coarse-Grained Martini Simulations	Journal of Chemical Information and Modeling	64	2024
	10.1021/acs.jpclett.4c01958	Neural Thermodynamic Integration: Free Energies from Energy-Based Diffusion Models	The Journal of Physical Chemistry Letters	15	2024
	10.1039/D4CE00752B	Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules	CrystEngComm	26	2024
	10.1021/acs.jcim.4c01300	EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning	Journal of Chemical Information and Modeling	64	2024
	10.1088/1361-651X/ad4d0d	Roadmap on data-centric materials science	Modelling and Simulation in Materials Science and Engineering	32	2024
	10.1038/s41597-023-02501-8	Shared metadata for data-centric materials science	Scientific Data	10	2023
	10.1038/s41467-023-40663-6	Data-mining unveils structure–property–activity correlation of viral infectivity enhancing self-assembling peptides	Nature Communications	14	2023
	10.1021/acs.jctc.3c00201	Condensed-Phase Molecular Representation to Link Structure and Thermodynamics in Molecular Dynamics	Journal of Chemical Theory and Computation	19	2023
	10.1039/D3BM00412K	Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings	Biomaterials Science	11	2023
	10.1016/j.bpj.2022.12.004	Induced asymmetries in membranes	Biophysical Journal	122	2022
	10.1021/acsfoodscitech.2c00251	Identifying Sequential Residue Patterns in Bitter and Umami Peptides	ACS Food Science & Technology	2	2022
	10.3389/fchem.2022.982757	Benchmarking coarse-grained models of organic semiconductors via deep backmapping	Frontiers in Chemistry	10	2022
	10.1063/5.0104914	Broad chemical transferability in structure-based coarse-graining	The Journal of Chemical Physics	157	2022
	10.1039/D2CB00125J	CLiB – a novel cardiolipin-binder isolated <i>via</i> data-driven and <i>in vitro</i> screening	RSC Chemical Biology	3	2022
	10.1038/s41586-022-04501-x	FAIR data enabling new horizons for materials research	Nature	604	2022
	10.1039/D2SC00116K	Data-driven discovery of cardiolipin-selective small molecules by computational active learning	Chemical Science	13	2022
	10.1021/acs.jpcb.1c05423	Multipolar Force Fields for Amide-I Spectroscopy from Conformational Dynamics of the Alanine Trimer	The Journal of Physical Chemistry B	125	2021
	10.1063/5.0061875	Publisher’s Note: “Data-driven equation for drug–membrane permeability across drugs and membranes” [J. Chem. Phys. 154, 244114 (2021)]	The Journal of Chemical Physics	155	2021
	10.1063/5.0053931	Data-driven equation for drug–membrane permeability across drugs and membranes	The Journal of Chemical Physics	154	2021
	10.1016/j.bpj.2021.03.043	Finite-size transitions in complex membranes	Biophysical Journal	120	2021
	10.1016/j.bpj.2021.04.025	Computer simulations of lipid regulation by molecular semigrand canonical ensembles	Biophysical Journal	120	2021

import {Plot} from "@mkfreeman/plot-tooltip"
papers = FileAttachment("data/all_papers.csv").csv({ typed: true })

topic_names = Array.from(new Set(papers.map(d => d["embedding.topic_name"])))

filtered = papers.filter(function(p) {
  return visible_topics.includes(p["embedding.topic_name"]);
})

  import {Plot as Plot} from "@mkfreeman/plot-tooltip"

papers = Array(82) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

topic_names = Array(16) ["space | ml | chemical", "dispersion | potential | intermolecular", "kinetic | model | simulation", " | | ", "data | science | research", "sequence | peptide | growth", "cardiolipin | mitochondrial | selectivity", "membrane | lipid | asymmetry", "peptide | folding | insertion", "model | cg | coarsegrained", "twostate | analysis | cooperativity", "permeability | permeation | mean", "dynamic | chemistry | molecular", "reweighting | dynamical | implementation", "modeling | polymer | landscape", "system | md | reactions"]

filtered = Array(82) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Note

This represents a two-dimensional embedding of all the group’s publications, clustered in topics. Publication abstracts are lemmatized and clustered using BERTopic with a UMAP dimensionality reduction.

For a more detailed description of our research, see Topics.